Predicting Defects Using Change Genealogies (ISSE 2013)Kim Herzig
This document discusses using change genealogies, which model dependencies between code changes, to predict defects. It finds that models using change genealogy metrics outperform those based on code complexity or dependency networks alone, achieving better precision while maintaining close recall. Key metrics include network efficiency and relationships between changes and dependency types. The study confirms that code entities combining functionalities from multiple older changes are more defect-prone.
Slides of session I presensented to my folks at University of Calgary on research paper on Mudflow and Flowdroid.
Links given below:
https://www.st.cs.uni-saarland.de/appmining/mudflow/
https://www.google.ca/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwik583ola7XAhUX6WMKHQYXCnoQFggoMAA&url=https%3A%2F%2Fblogs.uni-paderborn.de%2Fsse%2Ftools%2Fflowdroid%2F&usg=AOvVaw1t13BQnA07LA9FA3O5wNvN
Scout: A Contactless Active Vulnerability Tool - Dissertation, a year long pr...Jamie O'Hare
Scout is a tool that takes data from the internet-wide scanner Censys, performs an analysis to extract information, and associates that information with the National Vulnerability Database to identify potential known vulnerabilities in internet-connected systems. The author developed Scout after struggling to find a suitable internship project and exploring options like Shodan. They implemented Scout by writing their own Python script using techniques from academic papers, and evaluated it through initial validation, manual assessment, and comparison to other tools like OpenVAS.
Knowledge and Data Engineering IEEE 2015 ProjectsVijay Karan
List of Knowledge and Data Engineering IEEE 2015 Projects. It Contains the IEEE Projects in the Domain Knowledge and Data Engineering for the year 2015
Using Cognitive Dimensions Questionnaire to Evaluate the Usability of Securit...Chamila Wijayarathna
This was presented by me at the 28th annual gathering of Psychology of Programmers Interest Group (PPIG).
Usability issues that exist in security APIs cause programmers to embed those security APIs incorrectly to the applications they develop. This results in introduction of security vulnerabilities to those applications. One of the main reasons for security APIs to be not usable is currently there is no proper method by which the usability issues of security APIs can be identified. We conducted a study to assess the effectiveness of the cognitive dimensions questionnaire based usability evaluation methodology in evaluating the usability of security APIs. We used a cognitive dimensions based generic questionnaire to collect feedback from programmers who participated in the study. Results revealed interesting facts about the prevailing usability issues in four commonly used security APIs and the capability of the methodology to identify those issues.
O'Reilly Security New York - Predicting Exploitability FinalMichael Roytman
Security is all about reacting. It’s time to make some predictions. Michael Roytman explains how Kenna Security used the AWS Machine Learning platform to train a binary classifier for vulnerabilities, allowing the company to predict whether or not a vulnerability will become exploitable.
Michael offers an overview of the process. Kenna enriches the data with more specific, nondefinitional-level data. 500 million live vulnerabilities and their associated close rates inform the epidemiological data, as well as “in the wild” threat data from AlienVault’s OTX and SecureWorks’s CTU, Reversing Labs, and ISC SANS. The company uses 70% of the national vulnerability database as its training dataset and generates over 20,000 predictions on the remainder of the vulnerabilities. It then measures specificity and sensitivity, positive predictive value, and false positive and false negative rates before arriving at an optimal decision cutoff for the problem.
Performance testing involves understanding how quickly and efficiently software runs under typical loads. Key aspects include measuring how the software performs common tasks like database transactions or displaying images over time, both when the system is idle and under simulated heavy use with other programs running. The tests are typically run for extended periods to determine average performance metrics and ensure the software meets initial requirements for speed and responsiveness under real-world conditions.
This document discusses various black box testing techniques. Black box testing, also known as behavioral testing, involves testing a system without any knowledge of its internal structure or implementation. It involves testing a system based on its specifications and expected outputs for given inputs. The document describes several black box testing techniques including equivalence partitioning, boundary value analysis, comparison testing, orthogonal array testing, syntax-driven testing, decision table-based testing, and cause-and-effect graphs. These techniques help test a system from an external perspective to uncover errors in functionality, interfaces, behavior, and other issues.
Predicting Defects Using Change Genealogies (ISSE 2013)Kim Herzig
This document discusses using change genealogies, which model dependencies between code changes, to predict defects. It finds that models using change genealogy metrics outperform those based on code complexity or dependency networks alone, achieving better precision while maintaining close recall. Key metrics include network efficiency and relationships between changes and dependency types. The study confirms that code entities combining functionalities from multiple older changes are more defect-prone.
Slides of session I presensented to my folks at University of Calgary on research paper on Mudflow and Flowdroid.
Links given below:
https://www.st.cs.uni-saarland.de/appmining/mudflow/
https://www.google.ca/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwik583ola7XAhUX6WMKHQYXCnoQFggoMAA&url=https%3A%2F%2Fblogs.uni-paderborn.de%2Fsse%2Ftools%2Fflowdroid%2F&usg=AOvVaw1t13BQnA07LA9FA3O5wNvN
Scout: A Contactless Active Vulnerability Tool - Dissertation, a year long pr...Jamie O'Hare
Scout is a tool that takes data from the internet-wide scanner Censys, performs an analysis to extract information, and associates that information with the National Vulnerability Database to identify potential known vulnerabilities in internet-connected systems. The author developed Scout after struggling to find a suitable internship project and exploring options like Shodan. They implemented Scout by writing their own Python script using techniques from academic papers, and evaluated it through initial validation, manual assessment, and comparison to other tools like OpenVAS.
Knowledge and Data Engineering IEEE 2015 ProjectsVijay Karan
List of Knowledge and Data Engineering IEEE 2015 Projects. It Contains the IEEE Projects in the Domain Knowledge and Data Engineering for the year 2015
Using Cognitive Dimensions Questionnaire to Evaluate the Usability of Securit...Chamila Wijayarathna
This was presented by me at the 28th annual gathering of Psychology of Programmers Interest Group (PPIG).
Usability issues that exist in security APIs cause programmers to embed those security APIs incorrectly to the applications they develop. This results in introduction of security vulnerabilities to those applications. One of the main reasons for security APIs to be not usable is currently there is no proper method by which the usability issues of security APIs can be identified. We conducted a study to assess the effectiveness of the cognitive dimensions questionnaire based usability evaluation methodology in evaluating the usability of security APIs. We used a cognitive dimensions based generic questionnaire to collect feedback from programmers who participated in the study. Results revealed interesting facts about the prevailing usability issues in four commonly used security APIs and the capability of the methodology to identify those issues.
O'Reilly Security New York - Predicting Exploitability FinalMichael Roytman
Security is all about reacting. It’s time to make some predictions. Michael Roytman explains how Kenna Security used the AWS Machine Learning platform to train a binary classifier for vulnerabilities, allowing the company to predict whether or not a vulnerability will become exploitable.
Michael offers an overview of the process. Kenna enriches the data with more specific, nondefinitional-level data. 500 million live vulnerabilities and their associated close rates inform the epidemiological data, as well as “in the wild” threat data from AlienVault’s OTX and SecureWorks’s CTU, Reversing Labs, and ISC SANS. The company uses 70% of the national vulnerability database as its training dataset and generates over 20,000 predictions on the remainder of the vulnerabilities. It then measures specificity and sensitivity, positive predictive value, and false positive and false negative rates before arriving at an optimal decision cutoff for the problem.
Performance testing involves understanding how quickly and efficiently software runs under typical loads. Key aspects include measuring how the software performs common tasks like database transactions or displaying images over time, both when the system is idle and under simulated heavy use with other programs running. The tests are typically run for extended periods to determine average performance metrics and ensure the software meets initial requirements for speed and responsiveness under real-world conditions.
This document discusses various black box testing techniques. Black box testing, also known as behavioral testing, involves testing a system without any knowledge of its internal structure or implementation. It involves testing a system based on its specifications and expected outputs for given inputs. The document describes several black box testing techniques including equivalence partitioning, boundary value analysis, comparison testing, orthogonal array testing, syntax-driven testing, decision table-based testing, and cause-and-effect graphs. These techniques help test a system from an external perspective to uncover errors in functionality, interfaces, behavior, and other issues.
The document discusses lessons learned about software development and quality through comparisons to different types of construction projects like pyramids, cathedrals, cities, and skyscrapers. It covers topics like architecture, materials, tools, and processes used and how they relate to aspects of software development like planning, programming languages, tools, and development processes. It also discusses strategies for testing software requirements like unit testing, fuzz testing, code sabotage testing, and property-based testing to help validate specifications and find bugs. The document cautions that there are limits to testing based on concepts like Ashby's law of requisite variety and Bremermann's limit.
This document provides an overview of advance software engineering concepts. It discusses recommended books on software engineering and common software engineering activities like systems analysis and design. It also discusses key software engineering challenges like increasing diversity and demands for reduced delivery times. Different software development lifecycles are covered, including the waterfall model. Frequently asked questions about software engineering concepts are also answered. Agile software development practices like daily stand-ups, iteration planning, and test-driven development are explained.
The document discusses several key aspects of software and software engineering:
1. Software serves both as a product that transforms information and as a vehicle that delivers computing capabilities. It controls programs, enables communications, and helps build other software.
2. Software is more complex and difficult to develop than hardware but easier to modify and upgrade. Software costs are concentrated in design rather than production.
3. Software evolves and deteriorates over time unlike hardware, which wears out. Most software continues to be custom built despite a slow trend toward component-based construction. Maintaining and evolving legacy software poses challenges.
4. The document outlines several "laws" and myths regarding software evolution, management, customers, and practitioners
The document discusses several key aspects of software and software engineering:
1. Software serves both as a product that transforms information and as a vehicle that delivers computing capabilities. It controls programs, enables communications, and helps build other software.
2. Software is more complex and difficult to develop than hardware but easier to modify and upgrade. Software costs are concentrated in design rather than production.
3. Software evolves and deteriorates over time unlike hardware, which wears out. Most software continues to be custom built despite a slow trend toward component-based construction. Maintaining and evolving legacy software poses challenges.
4. The document outlines several "laws" and myths regarding software evolution, management, customers, and practitioners
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Embedded software static analysis_Polyspace-WhitePaper_finalTAMILMARAN C
This document discusses the challenges of testing embedded software and the limitations of traditional techniques like manual code reviews and dynamic testing. It introduces Polyspace Bug Finder and Polyspace Code Prover as static analysis tools that can overcome these limitations by automatically finding bugs, proving the absence of runtime errors, and providing stronger assurance of code reliability compared to non-exhaustive testing methods. The document argues that these static analysis tools allow businesses to reduce costs while accelerating delivery of reliable embedded systems.
This document discusses software quality, defining it as having three aspects: functional specification, quality specification, and resource specification. It describes factors of product operation quality, product revision quality, and product transition quality. Metrics for measuring qualities like correctness, reliability, efficiency, maintainability, and others are provided. The importance of software quality, intangibility of software, and accumulating errors are noted. Techniques to enhance quality like structured programming and cleanroom development are also summarized.
Three Interviews About Static Code AnalyzersAndrey Karpov
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
The document discusses software testing and debugging. It defines software testing as validating a software product to identify bugs and ensure it meets requirements. Debugging is defined as detecting and removing errors that cause unexpected behavior. The debugging process involves reproducing issues, analyzing variables, fixing bugs, and validating fixes. Common debugging tools and techniques like print statements, backtracking, and cause elimination are also outlined.
Adaptation of the technology of the static code analyzer for developing paral...PVS-Studio
This document discusses the adaptation of static code analysis tools for developing parallel programs. Static code analysis was originally introduced in the 1970s-1980s as a complement to compilers but declined in popularity in the 1990s as compiler diagnostics improved. However, interest has increased again as modern static analyzers can detect more complex errors, such as unsafe data access from multiple threads in parallel programs. The document examines how static analysis tools can help simplify the process of creating parallel program solutions by detecting errors even in rarely executed code sections.
This document provides an overview of software engineering concepts covered in lecture notes. It discusses the software development life cycle (SDLC) which includes key stages like requirements gathering, design, coding, testing, integration and maintenance. The SDLC framework aims to develop software efficiently using a well-defined process. Software engineering principles like abstraction and decomposition are used to reduce complexity when developing large programs.
No Silver Bullet - Essence and Accidents of Software EngineeringAditi Abhang
”There is no single development, in either technology or in management technique, that by itself promises even one order of magnitude improvement in productivity, in reliability, in simplicity.”
The document discusses various aspects of software testing such as test cases, test plans, test scenarios, testworthy criteria, testing types including functional, non-functional, manual and automated testing. It also covers topics like traceability matrix, test automation frameworks, fuzzing, mutation testing and references various standards and research papers related to software testing.
Accuracy and time_costs_of_web_app_scannersLarry Suto
The study tested seven web application security scanners on their ability to find vulnerabilities on intentionally vulnerable test sites created by the scanner vendors. When run in both "Point and Shoot" and "Trained" modes, NTOSpider found the most vulnerabilities with the fewest false positives. Appscan and Hailstorm also performed well after additional training. However, even fully trained, the scanners missed an average of 49% of vulnerabilities. Training scanners took significant time and may not be practical for large sites. The results were consistent with an earlier 2007 study and suggest accuracy should remain a top priority for security teams evaluating vulnerability scanners.
Start Up Austin 2017: Production Preview - How to Stop Bad Things From HappeningAmazon Web Services
The document discusses key areas to review for a production readiness review:
1. Architecture design, monitoring, logging, documentation, alerting, service level agreements, expected throughput, and testing are identified as important areas to review.
2. Specific topics within each area are discussed like defining system behavior for monitoring, using consistent logging formats, and implementing canary deployments.
3. The importance of automation, understanding performance baselines, and implementing dark launches are emphasized for production readiness.
Operations: Production Readiness Review – How to stop bad things from HappeningAmazon Web Services
The document provides an overview of key areas to review for production readiness including architecture design, monitoring, logging, documentation, alerting, service level agreements, expected throughput, testing, and deployment strategy. It summarizes best practices and considerations for each area such as using circuit breakers in monitoring, consistent logging formats, storing documentation near code, automating level 1 operations, and strategies for testing, deployments, and managing error budgets.
This document provides an introduction to software engineering. It discusses the importance of software today and how it has evolved significantly since the Apollo 11 moon landing. Some key characteristics of good software discussed include maintainability, correctness, reusability, reliability, and portability. The document also examines the software crisis and reasons it occurred, such as requirements constantly changing and not enough developers. Different paradigms for software development are presented, including waterfall model and agile development. Finally, the document introduces computer-aided software engineering (CASE) tools and how they can benefit the software development process.
Yazid Boutejder: AWS San Francisco Startup Day, 9/7/17
Operations: Production Readiness Review – how to stop bad things from happening - There is more to deploying code than pushing the deploy button. A good practice that many companies follow is a Production Readiness Review (PRR) which is essentially a pre-flight check list before a service launches. This helps ensure new services are properly architected, monitored, secured, and more. We’ll walk through an example PRR and discuss the value of ensuring each of these is properly taken care of before your service launches.
Extreme Programming (XP) is an agile software development methodology that values adaptability over predictability. It prescribes day-to-day practices meant to embody values like communication, simplicity, feedback, and courage. XP aims to create software that is more responsive to changing customer needs through practices like pair programming, test-driven development, and frequent small releases. The XP life cycle involves short iterative planning, designing, coding, testing, and listening phases to incorporate frequent customer feedback.
Bootstrap is a free and open-source JavaScript framework for developing responsive web sites and web applications. It contains HTML and CSS templates for common user interface components like buttons, navigation, and forms. Bootstrap is easy to implement, customizable through LESS or online tools, and has large community support. It is commonly used because it helps create a uniform look and feel across sites and allows developers to build responsive designs for multiple devices.
This document outlines the evolution of a software pipeline framework across 7 versions. The framework started with basic Pipeline and App components and added additional features like Settings and Plugins in later versions, with the latest version including Plugin registration capabilities.
The document discusses lessons learned about software development and quality through comparisons to different types of construction projects like pyramids, cathedrals, cities, and skyscrapers. It covers topics like architecture, materials, tools, and processes used and how they relate to aspects of software development like planning, programming languages, tools, and development processes. It also discusses strategies for testing software requirements like unit testing, fuzz testing, code sabotage testing, and property-based testing to help validate specifications and find bugs. The document cautions that there are limits to testing based on concepts like Ashby's law of requisite variety and Bremermann's limit.
This document provides an overview of advance software engineering concepts. It discusses recommended books on software engineering and common software engineering activities like systems analysis and design. It also discusses key software engineering challenges like increasing diversity and demands for reduced delivery times. Different software development lifecycles are covered, including the waterfall model. Frequently asked questions about software engineering concepts are also answered. Agile software development practices like daily stand-ups, iteration planning, and test-driven development are explained.
The document discusses several key aspects of software and software engineering:
1. Software serves both as a product that transforms information and as a vehicle that delivers computing capabilities. It controls programs, enables communications, and helps build other software.
2. Software is more complex and difficult to develop than hardware but easier to modify and upgrade. Software costs are concentrated in design rather than production.
3. Software evolves and deteriorates over time unlike hardware, which wears out. Most software continues to be custom built despite a slow trend toward component-based construction. Maintaining and evolving legacy software poses challenges.
4. The document outlines several "laws" and myths regarding software evolution, management, customers, and practitioners
The document discusses several key aspects of software and software engineering:
1. Software serves both as a product that transforms information and as a vehicle that delivers computing capabilities. It controls programs, enables communications, and helps build other software.
2. Software is more complex and difficult to develop than hardware but easier to modify and upgrade. Software costs are concentrated in design rather than production.
3. Software evolves and deteriorates over time unlike hardware, which wears out. Most software continues to be custom built despite a slow trend toward component-based construction. Maintaining and evolving legacy software poses challenges.
4. The document outlines several "laws" and myths regarding software evolution, management, customers, and practitioners
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Embedded software static analysis_Polyspace-WhitePaper_finalTAMILMARAN C
This document discusses the challenges of testing embedded software and the limitations of traditional techniques like manual code reviews and dynamic testing. It introduces Polyspace Bug Finder and Polyspace Code Prover as static analysis tools that can overcome these limitations by automatically finding bugs, proving the absence of runtime errors, and providing stronger assurance of code reliability compared to non-exhaustive testing methods. The document argues that these static analysis tools allow businesses to reduce costs while accelerating delivery of reliable embedded systems.
This document discusses software quality, defining it as having three aspects: functional specification, quality specification, and resource specification. It describes factors of product operation quality, product revision quality, and product transition quality. Metrics for measuring qualities like correctness, reliability, efficiency, maintainability, and others are provided. The importance of software quality, intangibility of software, and accumulating errors are noted. Techniques to enhance quality like structured programming and cleanroom development are also summarized.
Three Interviews About Static Code AnalyzersAndrey Karpov
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
The document discusses software testing and debugging. It defines software testing as validating a software product to identify bugs and ensure it meets requirements. Debugging is defined as detecting and removing errors that cause unexpected behavior. The debugging process involves reproducing issues, analyzing variables, fixing bugs, and validating fixes. Common debugging tools and techniques like print statements, backtracking, and cause elimination are also outlined.
Adaptation of the technology of the static code analyzer for developing paral...PVS-Studio
This document discusses the adaptation of static code analysis tools for developing parallel programs. Static code analysis was originally introduced in the 1970s-1980s as a complement to compilers but declined in popularity in the 1990s as compiler diagnostics improved. However, interest has increased again as modern static analyzers can detect more complex errors, such as unsafe data access from multiple threads in parallel programs. The document examines how static analysis tools can help simplify the process of creating parallel program solutions by detecting errors even in rarely executed code sections.
This document provides an overview of software engineering concepts covered in lecture notes. It discusses the software development life cycle (SDLC) which includes key stages like requirements gathering, design, coding, testing, integration and maintenance. The SDLC framework aims to develop software efficiently using a well-defined process. Software engineering principles like abstraction and decomposition are used to reduce complexity when developing large programs.
No Silver Bullet - Essence and Accidents of Software EngineeringAditi Abhang
”There is no single development, in either technology or in management technique, that by itself promises even one order of magnitude improvement in productivity, in reliability, in simplicity.”
The document discusses various aspects of software testing such as test cases, test plans, test scenarios, testworthy criteria, testing types including functional, non-functional, manual and automated testing. It also covers topics like traceability matrix, test automation frameworks, fuzzing, mutation testing and references various standards and research papers related to software testing.
Accuracy and time_costs_of_web_app_scannersLarry Suto
The study tested seven web application security scanners on their ability to find vulnerabilities on intentionally vulnerable test sites created by the scanner vendors. When run in both "Point and Shoot" and "Trained" modes, NTOSpider found the most vulnerabilities with the fewest false positives. Appscan and Hailstorm also performed well after additional training. However, even fully trained, the scanners missed an average of 49% of vulnerabilities. Training scanners took significant time and may not be practical for large sites. The results were consistent with an earlier 2007 study and suggest accuracy should remain a top priority for security teams evaluating vulnerability scanners.
Start Up Austin 2017: Production Preview - How to Stop Bad Things From HappeningAmazon Web Services
The document discusses key areas to review for a production readiness review:
1. Architecture design, monitoring, logging, documentation, alerting, service level agreements, expected throughput, and testing are identified as important areas to review.
2. Specific topics within each area are discussed like defining system behavior for monitoring, using consistent logging formats, and implementing canary deployments.
3. The importance of automation, understanding performance baselines, and implementing dark launches are emphasized for production readiness.
Operations: Production Readiness Review – How to stop bad things from HappeningAmazon Web Services
The document provides an overview of key areas to review for production readiness including architecture design, monitoring, logging, documentation, alerting, service level agreements, expected throughput, testing, and deployment strategy. It summarizes best practices and considerations for each area such as using circuit breakers in monitoring, consistent logging formats, storing documentation near code, automating level 1 operations, and strategies for testing, deployments, and managing error budgets.
This document provides an introduction to software engineering. It discusses the importance of software today and how it has evolved significantly since the Apollo 11 moon landing. Some key characteristics of good software discussed include maintainability, correctness, reusability, reliability, and portability. The document also examines the software crisis and reasons it occurred, such as requirements constantly changing and not enough developers. Different paradigms for software development are presented, including waterfall model and agile development. Finally, the document introduces computer-aided software engineering (CASE) tools and how they can benefit the software development process.
Yazid Boutejder: AWS San Francisco Startup Day, 9/7/17
Operations: Production Readiness Review – how to stop bad things from happening - There is more to deploying code than pushing the deploy button. A good practice that many companies follow is a Production Readiness Review (PRR) which is essentially a pre-flight check list before a service launches. This helps ensure new services are properly architected, monitored, secured, and more. We’ll walk through an example PRR and discuss the value of ensuring each of these is properly taken care of before your service launches.
Extreme Programming (XP) is an agile software development methodology that values adaptability over predictability. It prescribes day-to-day practices meant to embody values like communication, simplicity, feedback, and courage. XP aims to create software that is more responsive to changing customer needs through practices like pair programming, test-driven development, and frequent small releases. The XP life cycle involves short iterative planning, designing, coding, testing, and listening phases to incorporate frequent customer feedback.
Bootstrap is a free and open-source JavaScript framework for developing responsive web sites and web applications. It contains HTML and CSS templates for common user interface components like buttons, navigation, and forms. Bootstrap is easy to implement, customizable through LESS or online tools, and has large community support. It is commonly used because it helps create a uniform look and feel across sites and allows developers to build responsive designs for multiple devices.
This document outlines the evolution of a software pipeline framework across 7 versions. The framework started with basic Pipeline and App components and added additional features like Settings and Plugins in later versions, with the latest version including Plugin registration capabilities.
There are only 3 operations in a web appSimon Smith
The document discusses that there are only 3 core operations in web applications: 1) find 0 or more entities, 2) add a new entity, and 3) change an entity. It proposes creating microservices for each entity type that encapsulate business logic and validation and can only be accessed via messages. This decouples the services from consuming applications. Messages are used to transmit data between providers and consumers, while message processors focus solely on updating the message state and containing business logic. The framework allows applications to be built in a consistent, SOLID way by normalizing code into common components.
TileManager is an open source software that uses existing web pages to create "tiles" that can be used to navigate a site and look good across all platforms. These tiles contain fragments of web pages like images, text, or video. TileManager also includes built-in mouseover effects and page transitions to give websites a polished, "app-like" feel without needing to code separate mobile sites. The software is free to use and customize.
The document discusses N-tier application design, which involves breaking an application into well-defined regions or tiers, typically including a user interface (UI) tier, service tier, and data tier. Common mistakes in N-tier design include having the UI tier directly coupled to the data tier or duplicating validation logic across tiers. N-tiering addresses these issues by enforcing clear separation between tiers, encapsulating business rules in the service tier, and allowing new UIs to be developed without impacting existing applications.
The document discusses implementing a service-oriented architecture (SOA) in a C# environment. SOA is a flexible set of design principles that provides a loosely integrated suite of reusable services that can be used across multiple business domains. The key aspects of an SOA implementation include services that perform discrete functions, an orchestration layer to coordinate service requests and responses, and a service registry. Each service is independently testable, and new services can be added to extend functionality without impacting existing code. The SOA manager provides fault tolerance by monitoring errors and performance.
C# generics allow the specification of type parameters for classes, interfaces, and methods. This allows types to be deferred until runtime, providing type safety. Generics eliminate the need to use base object types and casts. Well-known generic collections like List<T> are provided in the System.Collections.Generic namespace.
1. “ When a program is modified, its complexity will increase, provided that one does not actively work against this.” - Wikipedia “ What we need more of is Science” – MC Hawkins “ If you think good code is expensive, try bad code” - Anon