The author invites you to read three interviews with representatives of three large, modern and
interesting projects to learn about their software development methodologies and about how they use
static code analyzers in particular. The author hopes that you will find this article interesting. The
following companies took part as interviewees: Acronis, AlternativaPlatform, Echelon Company.
Sincerely yours, Aleksandr Timofeev
Static analysis as part of the development process in Unreal EnginePVS-Studio
Unreal Engine continues to develop as new code is added and previously written code is changed. What is the inevitable consequence of ongoing development in a project? The emergence of new bugs in the code that a programmer wants to identify as early as possible. One of the ways to reduce the number of errors is the use of a static analyzer like PVS-Studio. Moreover, the analyzer is not only evolving, but also constantly learning to look for new error patterns, some of which we will discuss in this article. If you care about code quality, this article is for you.
Regular use of static code analysis in team developmentAndrey Karpov
Static code analysis technologies are used in companies with mature software development processes. However, there might be different levels of using and introducing code analysis tools into a development process: from manual launch of an analyzer "from time to time" or when searching for hard-to-find errors to everyday automatic launch or launch of a tool when adding new source code into the version control system.
The article discusses different levels of using static code analysis technologies in team development and shows how to "move" the process from one level to another. The article refers to the PVS-Studio code analyzer developed by the authors as an example.
Regular use of static code analysis in team developmentPVS-Studio
Static code analysis technologies are used in companies with mature software development processes. However, there might be different levels of using and introducing code analysis tools into a development process: from manual launch of an analyzer "from time to time" or when searching for hard-to-find errors to everyday automatic launch or launch of a tool when adding new source code into the version control system.
The article discusses different levels of using static code analysis technologies in team development and shows how to "move" the process from one level to another. The article refers to the PVS-Studio code analyzer developed by the authors as an example.
Regular use of static code analysis in team developmentPVS-Studio
Static code analysis technologies are used in companies with mature software development processes. However, there might be different levels of using and introducing code analysis tools into a development process: from manual launch of an analyzer "from time to time" or when searching for hard-to-find errors to everyday automatic launch or launch of a tool when adding new source code into the version control system.
An ideal static code analyzer would have the following characteristics: 100% detection of all errors with 0% false positives, high performance across any operating system or IDE, and the ability to analyze any programming language. However, the author explains that such an ideal is unachievable. Perfect error detection and no false positives are impossible due to limitations in analyzing program logic and constantly evolving code. Wide system and language support requires significant development efforts. Quality customer support and tool maintenance require ongoing funding which supports an annual licensing model rather than one-time free use. While an ideal analyzer is unattainable, the characteristics define goals for product development.
Unit testing involves writing code to test individual units or components in isolation to determine if they are functioning as expected. Writing tests first, before production code (test-driven development or TDD) can lead to higher quality code, easier debugging, and increased confidence in changes. The TDD process involves writing a failing test, then code to pass the test, and refactoring code as needed. To apply TDD effectively, tests should focus on logical code, avoid duplications, and isolate dependencies to keep tests simple and maintainable. Both server-side and client-side code need testing, focusing on things like business rules, view models, repositories, and UI logic.
Synthesizing Continuous Deployment Practices in Software DevelopmentAkond Rahman
Continuous deployment speeds up the process of existing agile methods, such as Scrum, and Extreme Programming (XP) through the automatic deployment of software changes to end-users upon passing of automated tests. Continuous deployment has become an emerging software engineering process amongst numerous software companies, such as Facebook, Github, Netflix, and Rally Software. A systematic analysis of software practices used in continuous deployment can facilitate a better understanding of continuous deployment as a software engineering process. Such analysis can also help software practitioners in having a shared vocabulary of practices and in choosing the software practices that they can use to implement continuous deployment. The goal of this paper is to aid software practitioners in implementing continuous deployment through a systematic analysis of software practices that are used by software companies. We studied the continuous deployment practices of 19 software companies by performing a qualitative analysis of Internet artifacts and by conducting follow-up inquiries. In total, we found 11 software practices that are used by 19 software companies. We also found that in terms of use, eight of the 11 software practices are common across 14 software companies. We observe that continuous deployment necessitates the consistent use of sound software engineering practices such as automated testing, automated deployment, and code review.
Testing As A Bottleneck - How Testing Slows Down Modern Development Processes...TEST Huddle
We often claim the purpose of testing is to verify that software meets a desired level of quality. Frequently, the term “testing” is associated with checking for functional correctness. However, in large, complex software systems with an established user-base, it is also important to verify system constraints such as backward compatibility, reliability, security, accessibility, usability. Kim Herzig from Microsoft explores these issues with the latest webinar on test Huddle.
Static analysis as part of the development process in Unreal EnginePVS-Studio
Unreal Engine continues to develop as new code is added and previously written code is changed. What is the inevitable consequence of ongoing development in a project? The emergence of new bugs in the code that a programmer wants to identify as early as possible. One of the ways to reduce the number of errors is the use of a static analyzer like PVS-Studio. Moreover, the analyzer is not only evolving, but also constantly learning to look for new error patterns, some of which we will discuss in this article. If you care about code quality, this article is for you.
Regular use of static code analysis in team developmentAndrey Karpov
Static code analysis technologies are used in companies with mature software development processes. However, there might be different levels of using and introducing code analysis tools into a development process: from manual launch of an analyzer "from time to time" or when searching for hard-to-find errors to everyday automatic launch or launch of a tool when adding new source code into the version control system.
The article discusses different levels of using static code analysis technologies in team development and shows how to "move" the process from one level to another. The article refers to the PVS-Studio code analyzer developed by the authors as an example.
Regular use of static code analysis in team developmentPVS-Studio
Static code analysis technologies are used in companies with mature software development processes. However, there might be different levels of using and introducing code analysis tools into a development process: from manual launch of an analyzer "from time to time" or when searching for hard-to-find errors to everyday automatic launch or launch of a tool when adding new source code into the version control system.
The article discusses different levels of using static code analysis technologies in team development and shows how to "move" the process from one level to another. The article refers to the PVS-Studio code analyzer developed by the authors as an example.
Regular use of static code analysis in team developmentPVS-Studio
Static code analysis technologies are used in companies with mature software development processes. However, there might be different levels of using and introducing code analysis tools into a development process: from manual launch of an analyzer "from time to time" or when searching for hard-to-find errors to everyday automatic launch or launch of a tool when adding new source code into the version control system.
An ideal static code analyzer would have the following characteristics: 100% detection of all errors with 0% false positives, high performance across any operating system or IDE, and the ability to analyze any programming language. However, the author explains that such an ideal is unachievable. Perfect error detection and no false positives are impossible due to limitations in analyzing program logic and constantly evolving code. Wide system and language support requires significant development efforts. Quality customer support and tool maintenance require ongoing funding which supports an annual licensing model rather than one-time free use. While an ideal analyzer is unattainable, the characteristics define goals for product development.
Unit testing involves writing code to test individual units or components in isolation to determine if they are functioning as expected. Writing tests first, before production code (test-driven development or TDD) can lead to higher quality code, easier debugging, and increased confidence in changes. The TDD process involves writing a failing test, then code to pass the test, and refactoring code as needed. To apply TDD effectively, tests should focus on logical code, avoid duplications, and isolate dependencies to keep tests simple and maintainable. Both server-side and client-side code need testing, focusing on things like business rules, view models, repositories, and UI logic.
Synthesizing Continuous Deployment Practices in Software DevelopmentAkond Rahman
Continuous deployment speeds up the process of existing agile methods, such as Scrum, and Extreme Programming (XP) through the automatic deployment of software changes to end-users upon passing of automated tests. Continuous deployment has become an emerging software engineering process amongst numerous software companies, such as Facebook, Github, Netflix, and Rally Software. A systematic analysis of software practices used in continuous deployment can facilitate a better understanding of continuous deployment as a software engineering process. Such analysis can also help software practitioners in having a shared vocabulary of practices and in choosing the software practices that they can use to implement continuous deployment. The goal of this paper is to aid software practitioners in implementing continuous deployment through a systematic analysis of software practices that are used by software companies. We studied the continuous deployment practices of 19 software companies by performing a qualitative analysis of Internet artifacts and by conducting follow-up inquiries. In total, we found 11 software practices that are used by 19 software companies. We also found that in terms of use, eight of the 11 software practices are common across 14 software companies. We observe that continuous deployment necessitates the consistent use of sound software engineering practices such as automated testing, automated deployment, and code review.
Testing As A Bottleneck - How Testing Slows Down Modern Development Processes...TEST Huddle
We often claim the purpose of testing is to verify that software meets a desired level of quality. Frequently, the term “testing” is associated with checking for functional correctness. However, in large, complex software systems with an established user-base, it is also important to verify system constraints such as backward compatibility, reliability, security, accessibility, usability. Kim Herzig from Microsoft explores these issues with the latest webinar on test Huddle.
Static Analysis Techniques For Testing Application Security - Houston Tech FestDenim Group
Static Analysis of software refers to examining source code and other software artifacts without executing them. This presentation looks at how these techniques can be used to identify security defects in applications. Approaches examined will range from simple keyword search methods used to identify calls to banned functions through more sophisticated data flow analysis used to identify more complicated issues such as injection flaws. In addition, a demonstration will be given of two freely-available static analysis tools: FXCop and the beta version of Microsoft’s XSSDetect tool. Finally, some approaches will be presented on how organizations can start using static analysis tools as part of their development and quality assurance processes.
The document discusses how agile development and continuous deployment disrupt traditional functional testing processes. It describes how testing practices have evolved from waterfall development with long release cycles to frequent daily releases. This requires testing to be more automated, with practices like acceptance testing driven development (ATDD) where testing defines requirements and drives the engineering process. It presents a cloud-based platform for ATDD that supports collaboration and test management integration.
A motivation for automated testing.
Common pretexts to avoid testing compared to the security, time saving and monetary benefits of having good automated tests.
This document discusses static code analysis and tools like SonarQube and Coverity. Static code analysis examines code without executing it to find bugs. Monitoring and fixing code quality issues improves application quality and delivery. SonarQube is an open source platform for managing code quality. It provides continuous inspection, reporting, and community support. Coverity also helps developers find defects early through static analysis of concurrency, security, and other issues. Both tools analyze code to find bugs and improve code quality and development processes.
How to Actually DO High-volume Automated TestingTechWell
This document summarizes a presentation on high-volume automated testing (HiVAT). Cem Kaner and Carol Oliver will present on techniques for doing HiVAT testing, including examples implemented in Ruby code. They will describe three HiVAT techniques - functional equivalence testing, long-sequence regression testing, and a more flexible HiVAT architecture. The presentation will cover the basic ingredients needed for HiVAT, examples of the techniques, and ideas for making HiVAT work in practice.
The document discusses different types of software tests including unit tests, integration tests, acceptance tests, load tests, and performance tests. It emphasizes the importance of unit tests for validating individual code modules and classes work as intended. Writing unit tests before coding allows developers to test complex scenarios quickly, ensure code quality and readability, and refactor code with confidence.
The document discusses present problems and future solutions for software testing. It notes that science fiction ideas often become reality and proposes several futuristic testing ideas that could one day exist, such as self-testing code, integrated software monitoring systems, and automated distributed testing services. It also outlines challenges in testing like determining when enough testing has been done, estimating testing time, and getting developers involved in testing. The document envisions an integrated testing environment that maps requirements, design, code, and tests to automate much of the testing process.
Digital Transformation, Testing and AutomationTEST Huddle
The Digital Transformation is real. It is having a profound effect on how business is done and the nature of the systems required to deliver productive customer experiences and consequent business benefits.
Key Takeaways:
- What is the Digital Transformation and how does it affect testing?
- Some key findings from a recent and an ancient survey
- How to achieve testing and automation success.
To view the webinar, visit - http://testhuddle.com/resource/digital-transformation-testing-and-automation/
The document discusses various types and levels of software testing. It defines software testing as analyzing a software item to detect differences between existing and required conditions (i.e. defects). The key types discussed are positive and negative testing, white-box and black-box testing. The levels covered are unit testing, integration testing, system testing, and acceptance testing. Various testing tools are also listed for different testing purposes such as source code testing, functional testing, performance testing, and database testing.
Automated and agile testing techniques and tools can help teams get software to a "DONE" state. Key aspects include:
- Writing automated unit, integration, and other tests to validate functionality and catch bugs early. This includes techniques like test-driven development.
- Leveraging continuous integration to run tests automatically on each code change to prevent regressions and catch issues quickly.
- Maintaining a balance of test types from unit to acceptance level while focusing on automation to make testing efficient and free up humans for more investigative tasks.
Continuous Automated Regression Testing to the RescueTechWell
A major concern when developing new software features is that another part of the code will be affected in unexpected ways. With a typical development processes, testers often do not run a full set of product regression tests until late in the release when it is much more costly to fix and retest the product. Continuous automated regression testing to the rescue! Brenda Kise describes the team, project, and organization value and benefits of continuously performing automated regression tests throughout the development process. Brenda explains how this practice saves time and money in the long-run because the team and stakeholders gain an ongoing understanding of the quality of the code base every time a new build becomes available. Brenda describes the different approaches for introducing the practices of continuous automated regression testing into your organization. Find out how to create your immediate feedback mechanism to highlight the new code that creates regression defects.
Career in Software Testing | Skills Required for Software Test Engineer | Edu...Edureka!
YouTube Link: https://youtu.be/3eOd9NTRgJo
** Test Automation Engineer Masters Program: https://www.edureka.co/masters-program/automation-testing-engineer-training **
This Edureka PPT on "Careers in Software Testing" will provide you with detailed and comprehensive knowledge of careers trends in software testing. It will guide you through the path one should take with the appropriate skills and the roles and responsibilities of Software Test Engineer. This PPT will cover the following topics:
What is Software Testing and why it is important?
Types and levels of Software Testing
Roadmap to Become a Software Testing Engineer
Job Roles
Roles and Responsibilities
Companies
Selenium playlist: https://goo.gl/NmuzXE
Selenium Blog playlist: http://bit.ly/2B7C3QR
Software Testing Blog playlist: http://bit.ly/2UXwdJm
Follow us to never miss an update in the future.
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Peer code review is one of the most effective ways to find defects – but is it agile? Because agile teams loathe heavy process, code review practices can easily fail. However, lightweight peer code review aligns well with the central tenets of agile—keeping feedback close to the point of creation, increasing team velocity by finding defects faster, and improving collective code ownership through frequent collaboration. Gregg Sporar shares recent research on code review practices and describes an agile code review approach—how much time to spend, which code to review, how much code to review at a time, how to set goals, the value of annotation, and more. After comparing four styles of code review—pair programming, over-the-shoulder, email, and tool-assisted—Gregg gives specific advice for creating review checklists and dealing with the social effects of code review in an agile environment.
Enhancing Developer Productivity with Code ForensicsTechWell
Imagine an engineering system that could evaluate developer performance, recognize rushed check-ins, and use that data to speed up development. “Congratulations Jane. You know this code well. No check-in test gate for you.” Anthony Voellm shares how behavioral analysis and developer assessments can be applied to improve productivity. This approach was motivated by today's test systems, tools, and processes that are all designed around the premise that “all developers are created equal.” Studies have shown developer error rates can vary widely and have a number of root causes—the mindset of the developer at the time the code was written, experience level, amount of code in a check-in, complexity of the code, and much more. With Digital Code Forensics, a set of metrics that can evaluate developers, Anthony demonstrates how even modest applications of this approach can speed up development. Discover and use the cutting edge of engineering productivity.
This document provides an overview of manual software testing interview questions and answers. It discusses key terms like bugs, errors, defects, and different types of testing such as white box testing, black box testing, compatibility testing, and the V-model framework. Specific questions covered include what stubs and drivers are, explaining test cases, test suites, and the different phases of the software testing life cycle. The document also provides answers to questions about test techniques like boundary value analysis, equivalence partitioning, and test coverage criteria like statement coverage.
This document provides an overview of code review practices. It discusses why code review is important to improve code quality, readability and maintainability. It describes different types of code review including over-the-shoulder, pair programming, automated and various time-based methods. Tools for facilitating code review are also presented, such as Review Board, Gerrit, Fisheye/Crucible and SmartBear CodeCollaborator. The goal of the document is to educate about best practices for conducting code reviews.
This document contains 151 interview questions related to software testing. The questions cover a wide range of testing topics including definitions of software testing, the difference between various testing types, the testing process, test planning and documentation, defect management, and other quality assurance and development processes. Responses would require in-depth knowledge of software testing practices, tools, and methodologies.
Why Automated Testing Matters To DevOpsdpaulmerrill
“Automated testing is a pain in my ear! Why can’t QA get it right? Why do the tests keep breaking? And for Pete’s sake, stop blaming the infrastructure!”
…Ok, maybe you chose a different word than “ear”.
How often do you have thoughts like this? Daily?
Let’s talk about these frustrations, why they exist and how we can use them to improve our systems!
In this talk, Paul Merrill, founder and Principal Automation Engineer at Beaufort Fairmont explores why automated testing matters to DevOps. Join us to learn how automated testing can be a useful tool in the creation and release of your systems!
This document discusses unit testing and test-driven development (TDD) in ABAP. It defines unit testing as a method to test individual units of code, such as methods and functions, to find bugs early. An xUnit framework provides automated testing with setup, test, and teardown methods. SAP's implementation is called ABAP Unit. TDD involves writing tests before code is written in a cycle of red, green, refactor to drive the development process. The document concludes with a code kata example to practice TDD with converting numbers to Roman numerals.
Comparison of static code analyzers: CppCat, Cppcheck, PVS-Studio and Visual ...Andrey Karpov
We have carried out a thorough comparison of four analyzers for C/C++ code: CppCat, Cppcheck, PVS-Studio and Visual Studio's built-in analyzer. It is a serious, large investigation that we had spent about 170 man-hours on and which, in our opinion, gives a good idea of the general state of things in static analysis nowadays.
Introduction into Test Driven Development (TDD) with ABAP Unit.
Presented at SAP Inside Track Hamburg (sitHH) 2013: http://wiki.sdn.sap.com/wiki/display/events/SAP+Inside+Track+Hamburg+2013
Static Analysis Techniques For Testing Application Security - Houston Tech FestDenim Group
Static Analysis of software refers to examining source code and other software artifacts without executing them. This presentation looks at how these techniques can be used to identify security defects in applications. Approaches examined will range from simple keyword search methods used to identify calls to banned functions through more sophisticated data flow analysis used to identify more complicated issues such as injection flaws. In addition, a demonstration will be given of two freely-available static analysis tools: FXCop and the beta version of Microsoft’s XSSDetect tool. Finally, some approaches will be presented on how organizations can start using static analysis tools as part of their development and quality assurance processes.
The document discusses how agile development and continuous deployment disrupt traditional functional testing processes. It describes how testing practices have evolved from waterfall development with long release cycles to frequent daily releases. This requires testing to be more automated, with practices like acceptance testing driven development (ATDD) where testing defines requirements and drives the engineering process. It presents a cloud-based platform for ATDD that supports collaboration and test management integration.
A motivation for automated testing.
Common pretexts to avoid testing compared to the security, time saving and monetary benefits of having good automated tests.
This document discusses static code analysis and tools like SonarQube and Coverity. Static code analysis examines code without executing it to find bugs. Monitoring and fixing code quality issues improves application quality and delivery. SonarQube is an open source platform for managing code quality. It provides continuous inspection, reporting, and community support. Coverity also helps developers find defects early through static analysis of concurrency, security, and other issues. Both tools analyze code to find bugs and improve code quality and development processes.
How to Actually DO High-volume Automated TestingTechWell
This document summarizes a presentation on high-volume automated testing (HiVAT). Cem Kaner and Carol Oliver will present on techniques for doing HiVAT testing, including examples implemented in Ruby code. They will describe three HiVAT techniques - functional equivalence testing, long-sequence regression testing, and a more flexible HiVAT architecture. The presentation will cover the basic ingredients needed for HiVAT, examples of the techniques, and ideas for making HiVAT work in practice.
The document discusses different types of software tests including unit tests, integration tests, acceptance tests, load tests, and performance tests. It emphasizes the importance of unit tests for validating individual code modules and classes work as intended. Writing unit tests before coding allows developers to test complex scenarios quickly, ensure code quality and readability, and refactor code with confidence.
The document discusses present problems and future solutions for software testing. It notes that science fiction ideas often become reality and proposes several futuristic testing ideas that could one day exist, such as self-testing code, integrated software monitoring systems, and automated distributed testing services. It also outlines challenges in testing like determining when enough testing has been done, estimating testing time, and getting developers involved in testing. The document envisions an integrated testing environment that maps requirements, design, code, and tests to automate much of the testing process.
Digital Transformation, Testing and AutomationTEST Huddle
The Digital Transformation is real. It is having a profound effect on how business is done and the nature of the systems required to deliver productive customer experiences and consequent business benefits.
Key Takeaways:
- What is the Digital Transformation and how does it affect testing?
- Some key findings from a recent and an ancient survey
- How to achieve testing and automation success.
To view the webinar, visit - http://testhuddle.com/resource/digital-transformation-testing-and-automation/
The document discusses various types and levels of software testing. It defines software testing as analyzing a software item to detect differences between existing and required conditions (i.e. defects). The key types discussed are positive and negative testing, white-box and black-box testing. The levels covered are unit testing, integration testing, system testing, and acceptance testing. Various testing tools are also listed for different testing purposes such as source code testing, functional testing, performance testing, and database testing.
Automated and agile testing techniques and tools can help teams get software to a "DONE" state. Key aspects include:
- Writing automated unit, integration, and other tests to validate functionality and catch bugs early. This includes techniques like test-driven development.
- Leveraging continuous integration to run tests automatically on each code change to prevent regressions and catch issues quickly.
- Maintaining a balance of test types from unit to acceptance level while focusing on automation to make testing efficient and free up humans for more investigative tasks.
Continuous Automated Regression Testing to the RescueTechWell
A major concern when developing new software features is that another part of the code will be affected in unexpected ways. With a typical development processes, testers often do not run a full set of product regression tests until late in the release when it is much more costly to fix and retest the product. Continuous automated regression testing to the rescue! Brenda Kise describes the team, project, and organization value and benefits of continuously performing automated regression tests throughout the development process. Brenda explains how this practice saves time and money in the long-run because the team and stakeholders gain an ongoing understanding of the quality of the code base every time a new build becomes available. Brenda describes the different approaches for introducing the practices of continuous automated regression testing into your organization. Find out how to create your immediate feedback mechanism to highlight the new code that creates regression defects.
Career in Software Testing | Skills Required for Software Test Engineer | Edu...Edureka!
YouTube Link: https://youtu.be/3eOd9NTRgJo
** Test Automation Engineer Masters Program: https://www.edureka.co/masters-program/automation-testing-engineer-training **
This Edureka PPT on "Careers in Software Testing" will provide you with detailed and comprehensive knowledge of careers trends in software testing. It will guide you through the path one should take with the appropriate skills and the roles and responsibilities of Software Test Engineer. This PPT will cover the following topics:
What is Software Testing and why it is important?
Types and levels of Software Testing
Roadmap to Become a Software Testing Engineer
Job Roles
Roles and Responsibilities
Companies
Selenium playlist: https://goo.gl/NmuzXE
Selenium Blog playlist: http://bit.ly/2B7C3QR
Software Testing Blog playlist: http://bit.ly/2UXwdJm
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Peer code review is one of the most effective ways to find defects – but is it agile? Because agile teams loathe heavy process, code review practices can easily fail. However, lightweight peer code review aligns well with the central tenets of agile—keeping feedback close to the point of creation, increasing team velocity by finding defects faster, and improving collective code ownership through frequent collaboration. Gregg Sporar shares recent research on code review practices and describes an agile code review approach—how much time to spend, which code to review, how much code to review at a time, how to set goals, the value of annotation, and more. After comparing four styles of code review—pair programming, over-the-shoulder, email, and tool-assisted—Gregg gives specific advice for creating review checklists and dealing with the social effects of code review in an agile environment.
Enhancing Developer Productivity with Code ForensicsTechWell
Imagine an engineering system that could evaluate developer performance, recognize rushed check-ins, and use that data to speed up development. “Congratulations Jane. You know this code well. No check-in test gate for you.” Anthony Voellm shares how behavioral analysis and developer assessments can be applied to improve productivity. This approach was motivated by today's test systems, tools, and processes that are all designed around the premise that “all developers are created equal.” Studies have shown developer error rates can vary widely and have a number of root causes—the mindset of the developer at the time the code was written, experience level, amount of code in a check-in, complexity of the code, and much more. With Digital Code Forensics, a set of metrics that can evaluate developers, Anthony demonstrates how even modest applications of this approach can speed up development. Discover and use the cutting edge of engineering productivity.
This document provides an overview of manual software testing interview questions and answers. It discusses key terms like bugs, errors, defects, and different types of testing such as white box testing, black box testing, compatibility testing, and the V-model framework. Specific questions covered include what stubs and drivers are, explaining test cases, test suites, and the different phases of the software testing life cycle. The document also provides answers to questions about test techniques like boundary value analysis, equivalence partitioning, and test coverage criteria like statement coverage.
This document provides an overview of code review practices. It discusses why code review is important to improve code quality, readability and maintainability. It describes different types of code review including over-the-shoulder, pair programming, automated and various time-based methods. Tools for facilitating code review are also presented, such as Review Board, Gerrit, Fisheye/Crucible and SmartBear CodeCollaborator. The goal of the document is to educate about best practices for conducting code reviews.
This document contains 151 interview questions related to software testing. The questions cover a wide range of testing topics including definitions of software testing, the difference between various testing types, the testing process, test planning and documentation, defect management, and other quality assurance and development processes. Responses would require in-depth knowledge of software testing practices, tools, and methodologies.
Why Automated Testing Matters To DevOpsdpaulmerrill
“Automated testing is a pain in my ear! Why can’t QA get it right? Why do the tests keep breaking? And for Pete’s sake, stop blaming the infrastructure!”
…Ok, maybe you chose a different word than “ear”.
How often do you have thoughts like this? Daily?
Let’s talk about these frustrations, why they exist and how we can use them to improve our systems!
In this talk, Paul Merrill, founder and Principal Automation Engineer at Beaufort Fairmont explores why automated testing matters to DevOps. Join us to learn how automated testing can be a useful tool in the creation and release of your systems!
This document discusses unit testing and test-driven development (TDD) in ABAP. It defines unit testing as a method to test individual units of code, such as methods and functions, to find bugs early. An xUnit framework provides automated testing with setup, test, and teardown methods. SAP's implementation is called ABAP Unit. TDD involves writing tests before code is written in a cycle of red, green, refactor to drive the development process. The document concludes with a code kata example to practice TDD with converting numbers to Roman numerals.
Comparison of static code analyzers: CppCat, Cppcheck, PVS-Studio and Visual ...Andrey Karpov
We have carried out a thorough comparison of four analyzers for C/C++ code: CppCat, Cppcheck, PVS-Studio and Visual Studio's built-in analyzer. It is a serious, large investigation that we had spent about 170 man-hours on and which, in our opinion, gives a good idea of the general state of things in static analysis nowadays.
Introduction into Test Driven Development (TDD) with ABAP Unit.
Presented at SAP Inside Track Hamburg (sitHH) 2013: http://wiki.sdn.sap.com/wiki/display/events/SAP+Inside+Track+Hamburg+2013
- The document discusses unit testing in ABAP and provides examples of how to write unit tests for ABAP code.
- It describes how to create test classes and methods in ABAP using ABAP Unit and the best practices for writing testable code, such as initializing test objects and avoiding dependencies on external systems.
- The document also highlights common pitfalls to avoid when writing unit tests, like assuming a specific database state or system date.
The document discusses various ABAP performance analysis tools including Code Inspector (SCI), Performance Trace (ST05), and Runtime Analysis (SE30).
Code Inspector performs static code analysis to identify potential performance and security issues. Performance Trace allows recording and analysis of database access, locking activities, and remote calls. Runtime Analysis provides insight into time spent in database vs ABAP code and analysis of internal table operations.
These tools each have benefits and limitations but together provide a comprehensive set of options for evaluating SQL statements, code execution paths, and identifying optimization opportunities at both the static code and runtime levels. Regular usage of these tools should be part of the development process.
My slides form the session at sitHH at 12th May 2012 about static ABAP code analysis tools and my experience with them. Apart the tools I share my personal lessons learned for establishing a code profiling process.
The article describes the testing technologies used when developing PVS-Studio static code analyzer. The developers of the tool for programmers talk about the principles of testing their own program product which can be interesting for the developers of similar packages for processing text data or source code.
Static analysis is most efficient when being used regularly. We'll tell you w...Andrey Karpov
Some of our users run static analysis only occasionally. They find new errors in their code and, feeling glad about this, willingly renew PVS-Studio licenses. I should feel glad too, shouldn't I? But I feel sad - because you get only 10-20% of the tool's efficiency when using it in such a way, while you could obtain at least 80-90% if you used it otherwise. In this post I will tell you about the most common mistake among users of static code analysis tools.
An ideal static analyzer, or why ideals are unachievablePVS-Studio
Being inspired by Eugene Laspersky's post about an ideal antivirus, I decided to write a similar post about an ideal static analyzer. And meanwhile think how far from being it our PVS-Studio is.
Static analysis is most efficient when being used regularly. We'll tell you w...PVS-Studio
The document discusses best practices for using static code analysis tools to maximize their effectiveness. It recommends: 1) Marking false positives to reduce future messages, 2) Using incremental analysis to check modified files, 3) Checking files modified in the last few days, and 4) Running analysis nightly on a build server. Following all recommendations provides the highest return on investment in static analysis by catching errors earlier in development.
Machine Learning in Static Analysis of Program Source CodeAndrey Karpov
Machine learning has firmly entrenched in a variety of human fields, from speech recognition to medical diagnosing. The popularity of this approach is so great that people try to use it wherever they can. Some attempts to replace classical approaches with neural networks turn up unsuccessful. This time we'll consider machine learning in terms of creating effective static code analyzers for finding bugs and potential vulnerabilities.
The article describes the testing technologies used when developing PVS-Studio static code analyzer. The developers of the tool for programmers talk about the principles of testing their own program product which can be interesting for the developers of similar packages for processing text data or source code.
The Development History of PVS-Studio for LinuxPVS-Studio
Earlier this year, we started doing something that we had felt uncertain about for a long time, namely porting PVS-Studio to Linux. In this article, I will tell you how we made the decision to create a product for Linux distributions after 10 years of the Windows version's existence. It's a big job, which, unfortunately, involves much more work than simply compiling the source files for the new platform, as some may think.
Traps detection during migration of C and C++ code to 64-bit WindowsPVS-Studio
Appearance of 64-bit processors on PC market made developers face the task of converting old 32-bit applications for new platforms. After the migration of the application code it is highly probable that the code will work incorrectly. This article reviews questions related to software verification and testing. It also concerns difficulties a developer of 64-bit Windows application may face and the ways of solving them.
Code review is one of the crucial software activities where developers and stakeholders collaborate with each other in order to assess software changes. Since code review processes act as a final gate for new software changes to be integrated into the software product, an intense collaboration is necessary in order to prevent defects and produce a high quality of software products. Recently, code review analytics has been implemented in projects (for example, StackAnalytics4 of the OpenStack project) to monitor the collaboration activities between developers and stakeholders in the code review processes. Yet, due to the large volume of software data, code review analytics can only report a static summary (e.g., counting), while neither insights nor instant suggestions are provided. Hence, to better gain valuable insights from software data and help software projects make a better decision, we conduct an empirical investigation using statistical approaches. In particular, we use the large-scale data of 196,712 reviews spread across the Android, Qt, and OpenStack open source projects to train a prediction model in order to uncover the relationship between the characteristics of software changes and the likelihood of having poor code review collaborations. We extract 20 patch characteristics which are grouped along five dimensions, i.e., software changes properties, review participation history, past involvement of a code author, past involvement of reviewers, and review environment dimensions. To validate our findings, we use the bootstrap technique which repeats the experiment 1,000 times. Due to the large volume of studied data, and an intensive computation of characteristic extraction and find- ing validation, the use of the High-Performance-Computing (HPC) re- sources is mandatory to expedite the analysis and generate insights in a timely manner. Through our case study, we find that the amount of review participation in the past and the description length of software changes are a significant indicator that new software changes will suffer from poor code review collaborations [2017]. Moreover, we find that the purpose of introducing new features can increase the likelihood that new software changes will receive late collaboration from reviewers. Our findings highlight the need for the policies of software change submission that monitor these characteristics in order to help software projects improve the quality of code reviews processes. Moreover, based on our findings, future work should develop real-time code review analytics implemented on HPC resources in order to instantly provide insights and suggestions to software projects
The article observes some questions related to testing the 64-bit software. Some difficulties which a developer of resource-intensive 64-bit applications may face and the ways to overcome them are described.
Control source code quality using the SonarQube platformPVS-Studio
The document discusses the SonarQube platform for continuous analysis and measurement of code quality. Some key features of SonarQube include supporting multiple programming languages, providing metrics on code quality issues like bugs, duplications, test coverage, and technical debt. It integrates with build systems and IDEs and allows customizing dashboards and quality profiles. The author implemented SonarQube for a customer to provide centralized monitoring of metrics for a large, long-term project.
This document discusses various tools and procedures for ensuring high code quality in Java development, including:
- Enforcing coding standards through code reviews and unit testing as part of the software development lifecycle.
- Measuring software quality through metrics like ease of testing and number of defects.
- Using static code analysis tools like FindBugs to identify issues and ensure compliance with best practices.
- Monitoring runtime performance with tools like JConsole and VisualVM.
Different Methodologies For Testing Web Application TestingRachel Davis
The document discusses different methodologies for testing web applications, including functionality testing, performance testing, usability testing, compatibility testing, unit testing, load testing, stress testing, and security testing. It provides details on each type of testing, including definitions and the pros and cons of functionality testing specifically. The key methodologies covered are functionality testing, which validates outputs against expected outputs; performance testing, which evaluates a system under pressure; and usability testing, which tests the user-friendliness of an application.
PVS-Studio advertisement - static analysis of C/C++ codePVS-Studio
This document advertises the PVS-Studio static analyzer. It describes how using PVS-Studio reduces the number of errors in code of C/C++/C++11 projects and costs on code testing, debugging and maintenance. A lot of examples of errors are cited found by the analyzer in various Open-Source projects. The document describes PVS-Studio at the time of version 4.38 on October 12-th, 2011, and therefore does not describe the capabilities of the tool in the next versions. To learn about new capabilities, visit the product's site http://www.viva64.com or search for an updated version of this article.
Testing Hourglass at Jira Frontend - by Alexey Shpakov, Sr. Developer @ Atlas...Applitools
Alexey Shpakov presents on testing in Jira Frontend. He discusses the testing pyramid with unit, integration, and end-to-end tests. He then introduces the concept of a "testing hourglass" which adds deployment and post-deployment verification to the pyramid. Key aspects of each type of test are discussed such as using feature flags, monitoring for flaky tests, and gradual rollouts to reduce risk.
Rapid software testing and conformance with static code analysisRogue Wave Software
With growing connectivity between complex automotive software components, development teams are looking for new ways to verify code security and validate against standards. This explains an exciting new approach to software testing that combines the breadth and depth of static analysis with modern test automation to provide rapid feedback to developers on incremental code changes – continuous static code analysis. By connecting deep analysis to continuous integration workflows, testing is pulled forward earlier to eliminate defects and reduce rework costs.
Walk away with knowledge of real defects, security vulnerabilities, and automotive standards (such as MISRA and ISO 26262) plus key steps to start immediate deployment of continuous static code analysis for testing. Presented at GENIVI All Member Meeting & Open Community Days.
The field of machine programming — the automation of the development of software — is making notable research advances. This is, in part, due to the emergence of a wide range of novel techniques in machine learning. In today’s technological landscape, software is integrated into almost everything we do, but maintaining software is a time-consuming and error-prone process. When fully realized, machine programming will enable everyone to express their creativity and develop their own software without writing a single line of code. Intel realizes the pioneering promise of machine programming, which is why it created the Machine Programming Research (MPR) team in Intel Labs. The MPR team’s goal is to create a society where everyone can create software, but machines will handle the “programming” part.
1) The document discusses the software development method which includes 6 key phases: requirement gathering, system analysis, system design, coding, testing, and deployment.
2) It provides details on each phase, including requirement gathering and analysis to define requirements, system analysis to divide the system into modules, system design to design the software architecture, coding to develop the software, testing to identify bugs, and deployment to launch the software.
3) It also discusses related topics like problem analysis, pseudocode, algorithms, and flowcharts which are techniques used during the software development process.
PVS-Studio and CppCat: An Interview with Andrey Karpov, the Project CTO and D...Andrey Karpov
The developers of PVS-Studio analyzer regularly publish new articles about their tool (and sometimes about other analyzers as well) where they share the analysis results of various software projects produced by the analyzer and demonstrate code samples in which defects were found. Quite recently, a new product, CppCat, was released, which is a lightweight version of PVS-Studio at a low cost - compared to that of its heavier counterpart. You can find a brief description of the PVS-Studio project for Visual C++ here and here, and for a description of the new product see the article "An Alternative to PVS-Studio at $250".
Some of the most famous information breaches over the past few years have been a result of entry through embedded and IoT system environments. Often these breaches are a result of unexpected system architecture and service connectivity on the network that allows the hacker to enter through an embedded device and make their way to the financial or corporate servers. Experts in embedded security discuss key security issues for embedded systems and how to address them.
Similar to Three Interviews About Static Code Analyzers (20)
Здесь вы найдёте 60 вредных советов для программистов и пояснение, почему они вредные. Всё будет одновременно в шутку и серьёзно. Как бы глупо ни смотрелся вредный совет, он не выдуман, а подсмотрен в реальном мире программирования.
In this article, you're going to find 60 terrible coding tips — and explanations of why they are terrible. It's a fun and serious piece at the same time. No matter how terrible these tips look, they aren't fiction, they are real: we saw them all in the real programming world.
Ошибки, которые сложно заметить на code review, но которые находятся статичес...Andrey Karpov
Есть ошибки, которые легко прячутся от программистов на обзорах кода. Чаще всего они связаны с опечатками или недостаточным знанием тонких нюансах языка/библиотеки. Давайте посмотрим интересные примеры таких ошибок и как их можно выявить с помощью статического анализа. При этом анализаторы не конкурируют с обзорами кода или, например, юнит-тестами. Они отлично дополняют другие методологии борьбы с ошибками.
PVS-Studio analyzes source code and finds various errors and code quality issues across multiple languages and frameworks. The document highlights 20 examples of issues found, including uninitialized variables, unreachable code, incorrect operations, security flaws, and typos. PVS-Studio is able to find these issues using techniques such as data-flow analysis, method annotation analysis, symbolic execution, type inference, and pattern-based analysis to precisely evaluate the code and pinpoint potential bugs or code smells.
When should you start using PVS-Studio? What can PVS-Studio detect? Supported standards: MISRA, CWE, CERT, OWASP, AUTOSAR. What about analysis options? What about legacy code?
Двойное освобождение ресурсов. Недостижимый код. Некорректные операции сдвига. Неправильная работа с типами. Опечатки и copy-paste. Проблемы безопасности. Путаница с приоритетом операций.
Make Your and Other Programmer’s Life Easier with Static Analysis (Unreal Eng...Andrey Karpov
George Gribkov presented on how to introduce static analysis to make programmers' and QA engineers' lives easier. Static analysis automatically checks code for bugs without executing it. While initial attempts to analyze Unreal Engine 4 failed, monitoring compiler calls directly succeeded in finding over 1800 warnings. Epic Games now uses continuous static analysis to receive early warnings. The best practices are to start analysis early and regularly in development and CI/CD pipelines, and to gradually fix old warnings using suppression files to ratchet down reported issues over time. Static and dynamic analysis complement each other to thoroughly check for errors.
Best Bugs from Games: Fellow Programmers' MistakesAndrey Karpov
George Gribkov will present on errors found in the code of popular games like System Shock, Doom 3, and osu!. He will discuss how his tool searches for code errors, provide examples of bugs detected, and conclude his presentation. The examples will showcase issues like unused variables, incorrect increment variables in for loops, null pointer dereferences, and misunderstandings of operators like ??. Corrections will be proposed to address the bugs.
Does static analysis need machine learning?Andrey Karpov
This document discusses whether static analysis needs machine learning. It begins with an introduction to static analysis and outlines existing static analysis solutions like DeepCode, Infer, SapFix, Embold, Source{d}, Clever-Commit, and CodeGuru. It then addresses problems with learning manually or from real large code bases, like outdated code and lack of documentation. Finally, it discusses promising approaches like analyzing code style, collecting additional metrics, and best practices for specific frameworks.
Typical errors in code on the example of C++, C#, and JavaAndrey Karpov
Objectives of this webinar
How we detected error patterns
Patterns themselves and how to avoid them:
3.1 Copy-paste and last line effect
3.2 if (A) {...} else if (A)
3.3 Errors in checks
3.4 Array index out of bounds
3.5 Operator precedence
3.6 Typos that are hard to spot
How to use static analysis properly
Conclusion
Q&A
How to Fix Hundreds of Bugs in Legacy Code and Not Die (Unreal Engine 4)Andrey Karpov
How to fight bugs in legacy code?
Should you do it at all?
What to do if there are hundreds or even thousands of errors?(that’s usually the case)
How to avoid spending a plethora of man-hours on this?
And still, how did you work with Unreal Engine?
C++ Code as Seen by a Hypercritical ReviewerAndrey Karpov
We all do code reviews. Who doesn't admit this – does it twice as often. C++ code reviewers look like a sapper. .. except that they can make a mistake more than once. But sometimes the consequences are painful . Brave code review world.
The Use of Static Code Analysis When Teaching or Developing Open-Source SoftwareAndrey Karpov
The document discusses using static code analysis when teaching or developing open-source software. It outlines how static analysis can help instructors check student homework and projects more efficiently, and help students learn about error patterns. When using static analysis for open-source projects, it recommends integrating it into developers' workflows locally and via continuous integration systems. Regular use is key to maximizing its benefits for finding and fixing bugs.
Static Code Analysis for Projects, Built on Unreal EngineAndrey Karpov
Why Do You Need Static Analysis? Detect errors early in the program development process. Get recommendations on code formatting. Check your spelling. Calculate various software metrics.
Are С and C++ Alive? Even More, IBM RPG Is! C and C++ Are Not Just for Old Systems. Are С and C++ Alive? Summary for C, C++. Embedded: C and С++ Are on the Rise.
Zero, one, two, Freddy's coming for youAndrey Karpov
This post continues the series of articles, which can well be called "horrors for developers". This time it will also touch upon a typical pattern of typos related to the usage of numbers 0, 1, 2. The language you're writing in doesn't really matter: it can be C, C++, C#, or Java. If you're using constants 0, 1, 2 or variables' names contain these numbers, most likely, Freddy will come to visit you at night. Go on, read and don't say we didn't warn you.
Quarkus Hidden and Forbidden ExtensionsMax Andersen
Quarkus has a vast extension ecosystem and is known for its subsonic and subatomic feature set. Some of these features are not as well known, and some extensions are less talked about, but that does not make them less interesting - quite the opposite.
Come join this talk to see some tips and tricks for using Quarkus and some of the lesser known features, extensions and development techniques.
DDS Security Version 1.2 was adopted in 2024. This revision strengthens support for long runnings systems adding new cryptographic algorithms, certificate revocation, and hardness against DoS attacks.
May Marketo Masterclass, London MUG May 22 2024.pdfAdele Miller
Can't make Adobe Summit in Vegas? No sweat because the EMEA Marketo Engage Champions are coming to London to share their Summit sessions, insights and more!
This is a MUG with a twist you don't want to miss.
When deliberating between CodeIgniter vs CakePHP for web development, consider their respective strengths and your project requirements. CodeIgniter, known for its simplicity and speed, offers a lightweight framework ideal for rapid development of small to medium-sized projects. It's praised for its straightforward configuration and extensive documentation, making it beginner-friendly. Conversely, CakePHP provides a more structured approach with built-in features like scaffolding, authentication, and ORM. It suits larger projects requiring robust security and scalability. Ultimately, the choice hinges on your project's scale, complexity, and your team's familiarity with the frameworks.
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI AppGoogle
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See My Other Reviews Article:
(1) AI Genie Review: https://sumonreview.com/ai-genie-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
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Enterprise Resource Planning System includes various modules that reduce any business's workload. Additionally, it organizes the workflows, which drives towards enhancing productivity. Here are a detailed explanation of the ERP modules. Going through the points will help you understand how the software is changing the work dynamics.
To know more details here: https://blogs.nyggs.com/nyggs/enterprise-resource-planning-erp-system-modules/
Software Engineering, Software Consulting, Tech Lead, Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Transaction, Spring MVC, OpenShift Cloud Platform, Kafka, REST, SOAP, LLD & HLD.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Do you want Software for your Business? Visit Deuglo
Deuglo has top Software Developers in India. They are experts in software development and help design and create custom Software solutions.
Deuglo follows seven steps methods for delivering their services to their customers. They called it the Software development life cycle process (SDLC).
Requirement — Collecting the Requirements is the first Phase in the SSLC process.
Feasibility Study — after completing the requirement process they move to the design phase.
Design — in this phase, they start designing the software.
Coding — when designing is completed, the developers start coding for the software.
Testing — in this phase when the coding of the software is done the testing team will start testing.
Installation — after completion of testing, the application opens to the live server and launches!
Maintenance — after completing the software development, customers start using the software.
Introducing Crescat - Event Management Software for Venues, Festivals and Eve...Crescat
Crescat is industry-trusted event management software, built by event professionals for event professionals. Founded in 2017, we have three key products tailored for the live event industry.
Crescat Event for concert promoters and event agencies. Crescat Venue for music venues, conference centers, wedding venues, concert halls and more. And Crescat Festival for festivals, conferences and complex events.
With a wide range of popular features such as event scheduling, shift management, volunteer and crew coordination, artist booking and much more, Crescat is designed for customisation and ease-of-use.
Over 125,000 events have been planned in Crescat and with hundreds of customers of all shapes and sizes, from boutique event agencies through to international concert promoters, Crescat is rigged for success. What's more, we highly value feedback from our users and we are constantly improving our software with updates, new features and improvements.
If you plan events, run a venue or produce festivals and you're looking for ways to make your life easier, then we have a solution for you. Try our software for free or schedule a no-obligation demo with one of our product specialists today at crescat.io
SOCRadar's Aviation Industry Q1 Incident Report is out now!
The aviation industry has always been a prime target for cybercriminals due to its critical infrastructure and high stakes. In the first quarter of 2024, the sector faced an alarming surge in cybersecurity threats, revealing its vulnerabilities and the relentless sophistication of cyber attackers.
SOCRadar’s Aviation Industry, Quarterly Incident Report, provides an in-depth analysis of these threats, detected and examined through our extensive monitoring of hacker forums, Telegram channels, and dark web platforms.
Need for Speed: Removing speed bumps from your Symfony projects ⚡️Łukasz Chruściel
No one wants their application to drag like a car stuck in the slow lane! Yet it’s all too common to encounter bumpy, pothole-filled solutions that slow the speed of any application. Symfony apps are not an exception.
In this talk, I will take you for a spin around the performance racetrack. We’ll explore common pitfalls - those hidden potholes on your application that can cause unexpected slowdowns. Learn how to spot these performance bumps early, and more importantly, how to navigate around them to keep your application running at top speed.
We will focus in particular on tuning your engine at the application level, making the right adjustments to ensure that your system responds like a well-oiled, high-performance race car.
Why Mobile App Regression Testing is Critical for Sustained Success_ A Detail...kalichargn70th171
A dynamic process unfolds in the intricate realm of software development, dedicated to crafting and sustaining products that effortlessly address user needs. Amidst vital stages like market analysis and requirement assessments, the heart of software development lies in the meticulous creation and upkeep of source code. Code alterations are inherent, challenging code quality, particularly under stringent deadlines.
Atelier - Innover avec l’IA Générative et les graphes de connaissancesNeo4j
Atelier - Innover avec l’IA Générative et les graphes de connaissances
Allez au-delà du battage médiatique autour de l’IA et découvrez des techniques pratiques pour utiliser l’IA de manière responsable à travers les données de votre organisation. Explorez comment utiliser les graphes de connaissances pour augmenter la précision, la transparence et la capacité d’explication dans les systèmes d’IA générative. Vous partirez avec une expérience pratique combinant les relations entre les données et les LLM pour apporter du contexte spécifique à votre domaine et améliorer votre raisonnement.
Amenez votre ordinateur portable et nous vous guiderons sur la mise en place de votre propre pile d’IA générative, en vous fournissant des exemples pratiques et codés pour démarrer en quelques minutes.
Neo4j - Product Vision and Knowledge Graphs - GraphSummit ParisNeo4j
Dr. Jesús Barrasa, Head of Solutions Architecture for EMEA, Neo4j
Découvrez les dernières innovations de Neo4j, et notamment les dernières intégrations cloud et les améliorations produits qui font de Neo4j un choix essentiel pour les développeurs qui créent des applications avec des données interconnectées et de l’IA générative.
Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production.
Yet, they often turn into annoying tasks riddled with frustration, hostility, unclear feedback and lack of standards. How can we improve this crucial process?
In this session we will cover:
- The Art of Effective Code Reviews
- Streamlining the Review Process
- Elevating Reviews with Automated Tools
By the end of this presentation, you'll have the knowledge on how to organize and improve your code review proces
GraphSummit Paris - The art of the possible with Graph TechnologyNeo4j
Sudhir Hasbe, Chief Product Officer, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
GraphSummit Paris - The art of the possible with Graph Technology
Three Interviews About Static Code Analyzers
1. Three Interviews About Static Code
Analyzers
Author: Aleksandr Timofeev
Date: 26.09.2014
Hello, dear readers!
The author invites you to read three interviews with representatives of three large, modern and
interesting projects to learn about their software development methodologies and about how they use
static code analyzers in particular. The author hopes that you will find this article interesting. The
following companies took part as interviewees: Acronis, AlternativaPlatform, Echelon Company.
Sincerely yours, Aleksandr Timofeev
Interviewees and article structure
The author addressed three companies to take the interviews:
— Acronis, developer of the Acronis Backup product designed for data backup and subsequent recovery
— AlternativaPlatform, developer of the "Tanki Online" project, a multiplayer browser game
— Echelon Company, developer of a series of products for code revision in the field of information
security
All the companies were asked the same questions save Echelon – the questions were changed a bit for
this company to better reflect the specifics of their work.
Interview with Acronis
The interviewee is Kirill Korotaev, Acronis Backup product development vice-president
• Give us please an overview of the primary and most large-scale product of your
company/project (the main point of the product, the language its code is written in, the size of
the team working on it, the usual pace of commits in lines of code or Kbytes per 24
hours/week/month, for example; what VCS you use)
The main point of the Acronis Backup product we develop is about creating backup copies of users' data
on their computers, notebooks and servers so that they could use these copies to recover the data later.
Recovery may be needed when the computer starts malfunctioning, for example; or when one needs an
earlier version of some file or document, or a file was lost.
99% of our entire project is written in C++. There are about 70 developers working on it. On average, we
make 100 to 300 commits per week. We use SVN (Subversion).
2. • Who and how analyzes the project code? How is the testing cycle organized? Is the tester
team large? How does the company respond to error messages – do you have any established
protocol to handle such situations?
We have architects and leaders who are well familiar with the code of those project parts they are
responsible for; therefore, they carry out analysis of this code and know how to improve it. Every
commit is passed through the code review system – that is, any change is first analyzed by programmers
responsible for the corresponding code fragment.
Presently, the number of our testers is comparable to the number of developers. We employ both
automatic and manual tests. For example, we have build validation tests, i.e. a set of tests to verify
every new build. Ideally, a new build should be compiled after every commit into the code and tested
immediately.
The process of addressing a revealed issue is the following. Any issue found by the testing department is
registered in the Jira system (a more advanced paid counterpart of BugZilla). And all that is integrated
with SVN – when, for example, a commit is made which addresses a particular issue, we add a reference
to this commit to Jira. We may also learn about an issue from our users. They first contact our technical
support service and if they reveal any bugs that should be analyzed, then, again, the information about
them first gets to Jira, and we release bugfixes in the next few updates.
• Do you use static code analysis tools? If yes, which then? Could you please give an example of
the most remarkable and interesting issue found by analyzers? What results and statistics do
you usually get when using analyzers? How often do you run checks and according to what
scheme? How do you respond to an issue found by an analyzer?
Among analyzers we used earlier or use currently are various tools – for example, both free open-source
Cppcheck and PVS-Studio. Of course, code analyzers should be used in any project. But they all are very
different, each of them being good at catching a certain type of bugs – that's why I'm totally for
employing a wide variety of development means.
We do find some interesting potential bugs every now and then. For example, one of the most difficult
to find bugs is the one found by PVS-Studio when standard auto pointers from the STL library are used
incorrectly. Or, here is another interesting error: when you multiply a sizeof from one structure or
parameter by another sizeof, PVS-Studio reasonably notices that it is pretty strange, to put it mildly, to
multiply one sizeof by another, for this operation logically implies getting a quadratic quantity result.
Sometimes static analyzers can figure out when a pointer is not checked for null before being used. But
these are more complex checks as it is not always obvious if a pointer can be null in a certain code
fragment. It's quite a good practice to run static analyzers over the code once per day. And we also get
bugs to automatically be recorded into that very Jira system, which is very useful for the product under
development.
• What is your opinion regarding future methodologies of large-scale software development?
As separate questions, what do you expect and would like to get from static code analysis
tools in future?
Automated tools are and will go on developing. For example, there is not a single automated system
nowadays that could pick tests relying on the modifications made to the code – that is, select only those
tests that need to be run for some particular modification.
What the future of static analyzers is concerned, I think they will gradually learn to handle more and
more issues. At the same time, they will be shifting towards more complex analysis and perhaps even
become a guarantee of code's compliance with some protocol, for instance.
3. • A few words for your colleagues and readers?
Write high-quality code, test it and don't forget to use a wide variety of methodologies – including static
analyzers.
Interview with AlternativaPlatform
The interviewee is Aleksey Kviring, CTO of "Tanki Online" LLC
• Give us please an overview of the primary and most large-scale product of your
company/project (the main point of the product, the language its code is written in, the size of
the team working on it, the usual pace of commits in lines of code or Kbytes per 24
hours/week/month, for example; what VCS you use)
Currently we have only one product like that which is the Tanki Online game. The server part is written
in Java, the client part in AS3. We have about 20 programmers. We add approximately 5K lines of code
per week. We use GIT as a VCS.
• Who and how analyzes the project code? How is the testing cycle organized? Is the tester
team large? How does the company respond to error messages – do you have any established
protocol to handle such situations?
We use an approach typical of GIT. All the code runs through obligatory Code Review. We also use
continuous integration, and the build server regularly checks code and runs tests over it.
Testing is done in a number of stages: first automatic testing, then manual testing by developers
themselves (through playing the game), then by the tester team. If everything is alright, community
testers join the testing process. And only after that, all the changes get into production. Our tester team
is small – only three persons. But we intensively employ community testers: there are a few dozens of
volunteers.
If some bug still gets into production somehow, it is fixed right after we detect it. Usually all such errors
are fixed in a couple of days.
• Do you use static code analysis tools? If yes, which then? Could you please give an example of
the most remarkable and interesting issue found by analyzers? What results and statistics do
you usually get when using analyzers? How often do you run checks and according to what
scheme? How do you respond to an issue found by an analyzer?
We don't use such tools at the company level. In the past, I launched a couple of static analyzers just for
interest, but they found nothing serious (JetBrain IDEA checker).
I think static analysis is very useful for complex languages such as C and C++. But for simpler languages
like Java, it's not that relevant. Java is not subject to memory-related issues as a class. Its syntax is plain
and clear, no alternative interpretations are allowed, many issues are caught by the compiler at the
compilation stage. Development environments provide convenient refactoring tools, which excludes
accidental errors resulting from manual code modifications.
There is one area I'd use static analysis in when working with Java. It has to do with checking a program
for correct multithread execution. But there are simply no tools capable of that at present. Generally
speaking, if a static analyzer is quality and can find real bugs, it will be useful for one's project.
• What is your opinion regarding future methodologies of large-scale software development?
As separate questions, what do you expect and would like to get from static code analysis
tools in future?
4. Future belongs to automated testing systems, continuous integration systems, and code analyzers.
What I expect from static analysis is the ability to analyze multithread applications and architectural
solutions.
• A few words for your colleagues and readers?
Don't be afraid of incorporating new technologies into your development cycle. Learn from more
experienced fellow programmers. Revise your old solutions. And then you certainly will succeed.
Interview with Echelon
The interviewee is Andrey Fadin (a.fadin@cnpo.ru), chief designer of Echelon Company
• Give us please an overview of your company and its business related to software security.
Echelon Company is both a developer of information security analysis means and an active user of these
products within the framework of information protection means certification and commercial code
audit projects.
Means of information security analysis developed by our company include the following:
• AK-VS2, a cloud environment for conducting certification testing of source code for compliance
with the requirements of undocumented capabilities absence control (up to Level 1 inclusively);
• AppChecker, a product conducting signature-based and heuristic analysis of program code
aimed at detecting beetles, critical software vulnerabilities, and other issues related to program
code's defects;
• PIK, a means to fix and compare checksums of files, folders and physical digital media;
• Skaner-VS, a toolkit and environment to conduct network and local security audit including
security scanners, traffic analysis means, means of search of residual information on physical
media and a few other components.
The Echelon team managing code security analysis and penetration testing is an association of highly
skilled IT and information security specialists established on the personnel, research, and engineering
bases of Echelon Company and the leading technical university of Russia, Bauman Moscow State
Technical University.
We work with most of the popular programming languages such as PHP, Java, C#, C/C++, Perl, Python,
JavaScript, including their most recent standards.
Program code audit conducted by Echelon Company specialists allows us to solve the following tasks:
• control of in-house and outsourced code's quality, detection of typical defects (coding or
designing errors);
• detection of intentionally planted beetles in code;
• borrowed code control (analysis of software's external dependencies on open-source and other
external components)
Software that has successfully passed the audit can be certified according to information security
requirements in Echelon's test laboratory.
• Give us please an overview of your experts' work (what doesn't refer to classified
information): Who and how analyzes project code? How is the testing cycle organized? What
is the regular protocol when addressing an important issue found in code?
The code audit team is formed from specialists of two basic types:
5. Specialists of the first type are Echelon test laboratory's experts experienced in establishing cooperation
with developers of large-scale software projects (operating systems, firewalls) and also in team review
of large amounts of code.
Specialists of the second type are developers (personnel of Echelon's Research&Development
departments) who have high technical qualifications in various programming languages, their
frameworks and typical libraries. Whenever possible, we try to cooperate with static analysis tools'
developers themselves when conducting code audit, which allows them to appreciate the convenience
of our analysis means directly from their own experience. Besides, since developers are better skilled in
implementing new signatures for static analyzers, it does make sense to employ them for timely updates
of defect base when required by the specifics of a software project under testing.
Speaking generally, the process of software development and testing is made up of the following stages:
1. Decomposing project code into components (when analyzing a third-party project)
2. Building a threat model, analyzing these components and their interaction interfaces for severe
information security issues.
3. Running static and dynamic analysis tools taking into account the results of Stage 2.
4. Selective code review based on the results of Stages 3 and 2.
5. Preparing a report of potentially dangerous constructs we have detected and discussing the
results with the project's developer team.
Stages 3, 4 and 5 are usually repeated 3-4 times because, depending on tne analysis results for each
potential construct, either the software project is revised to eliminate the defect (which is followed by
repetition of stages starting with Stage 3) or the issue is found to be an expert's incorrect assumption or
false positive by a static analyzer (which is followed by repetition of stages starting with Stage 4).
• A few words about static analysis tools you use: What tools do you use? Could you give an
example of the most remarkable and interesting error found by analyzers? What results and
statistics do you usually get when using analyzers? How do you respond to an issue found by
an analyzer?
In their work, auditors use both our own solutions (AK-VS2, AppChecker) and open-source tools
(CppCheck, PMD) as well as purchased third-party commercial tools (CppCat).
The algorithm of addressing issues was described in section 2. What the statistics of using analyzers is
concerned, the ratio of false positives in large projects is usually above 50%, so we in any case have to
employ an expert to compose the final list of potentially dangerous constructs found in project code.
However, since the expert does not review the entire code but only a few critical parts of it which on
average make not more than 5% of the entire code size, we can save a considerable amount of time on
code analysis.
To avoid breaching any non-disclosure agreements, we unfortunately cannot tell you about errors found
in particular products. But as our experience shows, most of interesting errors are related to:
• use of hard-coded passwords (Use of Hard-coded Password, CWE-259) and other authentication
data (Use of Hard-coded Credentials, CWE-798);
• "easter eggs" and other hidden functionality (Hidden Functionality, CWE-912);
• rather common errors related to race conditions and shared resources (Race Condition, CWE-
362).
• What is your opinion regarding future software development methodologies and, as separate
questions, what do you expect and would like to get from static code analysis tools in future?
6. In our opinion, software verification will be getting more tightly connected with development processes,
both within the framework of continuous integration systems and continuous delivery systems.
Tight integration with these systems will in future allow developers to fully control software
development and delivery; that is, static analyzers will serve as kind of an IPS within these processes,
blocking code failed to pass the quality gate at the level of commits and releases. From this viewpoint,
any CI/CD system is also an interesting source of events for SIEM systems.
Rich prospects are also provided by the introduction of static analyzers into the model-driven
development paradigm; tight integration with CASE-means will allow developers to reveal errors at the
levels of syntax, software components and their interfaces, and even at the level of business
requirements so that an analyst, for instance, could already at the system designing stage substantiate
to customers why adding a certain access control role is necessary.
• A few words for your colleagues and readers?
Dear colleagues, during the last decade, emphasis in the field of enterprise information security was put
on network security as well as endpoint security.
However, when dealing with such tasks as detection of zero-day exploits, beetles and "implants" (code
fragments and configurations planted into software for the purposes of state and industrial espionage),
we appear to be facing the issue when classic network- and node-level information security means
(intrusion detection systems, antivirus software) cannot efficiently handle such threats.
To solve these issues, we need a comprehensive approach that on the one hand implies centralization of
enterprise information security management (SIEM-systems), and on the other, makes use of software
structural decomposition into components with control over their origin, as well as static analysis of
their contents and materials for their production (including source texts).
Conclusion
The author thanks the press services and experts of the companies that took part in these interviews for
prompt and detailed answers to the interview questions. The author is also thankful to the ООО
"Program Verification Systems" company, developer of a contemporary static code analyzer PVS-Studio
and sponsor of this article. Without their support, it might hardly have been published at all.