Gathering SW Indicators
•Download as PPT, PDF•
1 like•370 views
This document discusses gathering software indicators from configuration management (CM) and bug tracking tools. It identifies CM as the primary source for reliable indicator data since all changes must go through CM. It lists various raw data that can be collected from source code, bug tracking, and CM systems. It then provides formulas for calculating indicators from this raw data, such as writing pressure, size of modules, average task age, and quality of work. The goal is to automate data collection and analysis to provide insights into a software project's quality, reliability, and development efforts over time.
Report
Share
Report
Share
![Overview ,[object Object],[object Object],[object Object],[object Object]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
Recommended
Measuring the Code Quality Using Software Metrics
The quality of the code is checked before deploying the software, the quality of the software will be assured.
SD & D Testing
Testing begins early in the software development process using prototypes to identify errors. Different types of test data are used including normal, extreme, and exceptional cases. Testing occurs in phases from unit testing of individual components to integration, system, acceptance, and alpha/beta testing involving end users. Various techniques are used like dry runs, trace tables, and breakpoints to step through code and check variable values. However, testing can only find errors and not prove their absence, as not all possibilities may be tested.
4. The Software Development Process - Testing
Testing is most cost effective when done earlier in the software development process. Errors found during the analysis and design stages are cheaper to fix than those discovered later. Various types of test data should be used, including normal, boundary, and exceptional data, to thoroughly test a program. Testing is conducted in phases from unit testing of individual components to system-wide acceptance testing by end users. However, testing can only find bugs but never prove a program is completely error-free.
SD & D analysis
The document outlines the seven stages of the software development process: analysis, design, implementation, testing, documentation, evaluation, and maintenance. It focuses on the analysis stage, explaining that analysts must understand the problem by interviewing clients, developing a software specification that clearly defines the problem and serves as a legal agreement between the analyst and client. Examples of questions analysts ask clients to determine program requirements like input/output details are provided.
Reporting On The Testing Process
The document discusses the importance of documenting the software testing process. It outlines that the testing process should be reported on to communicate test results, compare results to design specifications, and highlight problems. The documentation of the testing process should include test requirements, a test plan, test data and expected results, actual test results, and recommendations. Communication is also important and should occur between developers and clients as well as testers and developers. CASE tools can help automate parts of the testing process and generate test data.
1. The Software Development Process - Analysis
The document outlines the seven stages of a software development process: analysis, design, implementation, testing, documentation, evaluation, and maintenance. It then discusses the analysis stage in more detail, noting that analysts read and understand the problem, interview clients for clarity, and develop a software specification as a statement of the problem and basis for legal agreement between the analyst and client. Finally, it introduces questioning for requirements of a program to calculate averages, asking about inputs like number of numbers in the set, data types, and output display specifics.
Software fault management
This document discusses using measurement and modeling to improve software reliability engineering (SRE) through understanding software faults and change activity. It involves three phases:
1) Static code measurement to measure code implementing requirements and develop a fault index (FI) metric.
2) Measurement of change between builds/sprints to develop a change profile by measuring code churn and deltas.
3) Dynamic test measurement to develop a test profile and understand how tests hit changed code portions.
The goal is to predictably meet schedules with known quality by providing testers actionable information about faults and changes. Measurement helps understand evolving software as a "moving target" and anticipate faults based on past experience.
But Did You Test It
Moodle is a very flexible application with a large number of variables and roles. Testing upgrades and changes can be a challenge. This presentation should help attendees focus testing at their own workplace.
Recommended
Measuring the Code Quality Using Software Metrics
The quality of the code is checked before deploying the software, the quality of the software will be assured.
SD & D Testing
Testing begins early in the software development process using prototypes to identify errors. Different types of test data are used including normal, extreme, and exceptional cases. Testing occurs in phases from unit testing of individual components to integration, system, acceptance, and alpha/beta testing involving end users. Various techniques are used like dry runs, trace tables, and breakpoints to step through code and check variable values. However, testing can only find errors and not prove their absence, as not all possibilities may be tested.
4. The Software Development Process - Testing
Testing is most cost effective when done earlier in the software development process. Errors found during the analysis and design stages are cheaper to fix than those discovered later. Various types of test data should be used, including normal, boundary, and exceptional data, to thoroughly test a program. Testing is conducted in phases from unit testing of individual components to system-wide acceptance testing by end users. However, testing can only find bugs but never prove a program is completely error-free.
SD & D analysis
The document outlines the seven stages of the software development process: analysis, design, implementation, testing, documentation, evaluation, and maintenance. It focuses on the analysis stage, explaining that analysts must understand the problem by interviewing clients, developing a software specification that clearly defines the problem and serves as a legal agreement between the analyst and client. Examples of questions analysts ask clients to determine program requirements like input/output details are provided.
Reporting On The Testing Process
The document discusses the importance of documenting the software testing process. It outlines that the testing process should be reported on to communicate test results, compare results to design specifications, and highlight problems. The documentation of the testing process should include test requirements, a test plan, test data and expected results, actual test results, and recommendations. Communication is also important and should occur between developers and clients as well as testers and developers. CASE tools can help automate parts of the testing process and generate test data.
1. The Software Development Process - Analysis
The document outlines the seven stages of a software development process: analysis, design, implementation, testing, documentation, evaluation, and maintenance. It then discusses the analysis stage in more detail, noting that analysts read and understand the problem, interview clients for clarity, and develop a software specification as a statement of the problem and basis for legal agreement between the analyst and client. Finally, it introduces questioning for requirements of a program to calculate averages, asking about inputs like number of numbers in the set, data types, and output display specifics.
Software fault management
This document discusses using measurement and modeling to improve software reliability engineering (SRE) through understanding software faults and change activity. It involves three phases:
1) Static code measurement to measure code implementing requirements and develop a fault index (FI) metric.
2) Measurement of change between builds/sprints to develop a change profile by measuring code churn and deltas.
3) Dynamic test measurement to develop a test profile and understand how tests hit changed code portions.
The goal is to predictably meet schedules with known quality by providing testers actionable information about faults and changes. Measurement helps understand evolving software as a "moving target" and anticipate faults based on past experience.
But Did You Test It
Moodle is a very flexible application with a large number of variables and roles. Testing upgrades and changes can be a challenge. This presentation should help attendees focus testing at their own workplace.
Embedded Testing 2015
We deal with the impact of applying DevOps practices (infrastructure as code, continuous integration, release and deployment on demand) in the development of embedded projects spanning the full spectrum of mechanics, electronics and software.
Incorporating devops concepts within the regular test flow enables collaboration between engineers of all three disciplines with very fast feedback cycles.
Software is in a position to provide tools and data to electronics/mechanics to verify design decisions while moving forward with the implementation.
Deliverables are provided at any point in time of the development presenting a constant stream of functioning prototypes from the beginning of development while providing an almost seamless transition from 3D printed proof-of-concept using evaluation boards to integrated device.
In the talk we present the basic DevOps principles and concepts and how they map to established procedures like EOL Tests, affect production considerations and ease hardware-software integration.
MDD and the Tautology Problem: Discussion Notes.
MoDeVVA @ MODELS 2009, Denver. 10/5/2009. What are the dangers of using the same model to both generate a system and the test suite for that system.
The Art of Testing Less without Sacrificing Quality @ ICSE 2015
Testing is a key element of software development processes for the management and assessment of product quality. In most development environments, the software engineers are responsible for ensuring the functional correctness of code. However, for large complex software products, there is an additional need to check that changes do not negatively impact other parts of the software and they comply with system constraints such as backward compatibility, performance, security etc. Ensuring these system constraints may require complex verification infrastructure and test procedures. Although such tests are time consuming and expensive and rarely find defects they act as an insurance process to ensure the software is compliant. However, long lasting tests increasingly conflict with strategic aims to shorten release cycles. To decrease production costs and to improve development agility, we created a generic test selection strategy called THEO that accelerates test processes without sacrificing product quality. THEO is based on a cost model, which dynamically skips tests when the expected cost of running the test exceeds the expected cost of removing it. We replayed past development periods of three major Microsoft products resulting in a reduction of 50% of test executions, saving millions of dollars per year, while maintaining product quality.
Test Smarter: Efficient Coverage Metrics That Won't Leave You Exposed
Achieving optimum test and requirement coverage is critical for delivering high quality applications to the marketplace. Coverage can be very useful in determining the extent to which code and requirement has been tested. However, at the same time, a high coverage result (something in high 80s or 90s) cannot be taken as a measure of effective testing or an indicator of testers achieving perfection. Leveraging processes and tools that help determine sufficient coverage metrics, account for risk, trim costs incurred from unnecessary tests is therefore important.
In this webinar, Nikhil Kaul, Product Manager, joins Engineer, Rick Almeida, to discuss how to balance the trade-off between achieving high test coverage and testing code and requirements that matter. In this session, we will cover:
What coverage metrics to use and what percentages to aim for?
- How coverage metrics can be misused?
- How to use requirement and code coverage well?
- What metrics won’t tell you?
- Establish the connection between requirement and test coverage.
Testing Software Solutions
The document discusses various software testing and evaluation techniques used to ensure software solutions meet design specifications and are free from errors. It covers topics like unit testing, integration testing, system testing, black box and white box testing, test data generation, benchmarking, and quality assurance.
2. The Software Development Process - Design
The document discusses different aspects of program design including breaking problems down into smaller modules using top-down or bottom-up design, using flow charts, structure diagrams, and pseudocode as design notations, and ensuring a good human-computer interface adheres to consistency rules when interacting with users through menus, commands, and data display. The task is to create an Etherpad to develop a class list of human-computer interface rules.
Introduction and Role of a manual testing in a SDLC
Quality assurance involves systematically testing products and services to ensure they meet requirements. Software testing identifies bugs and ensures correctness, completeness, and quality. Common causes of bugs include unclear requirements, miscommunication, complexity, and unrealistic deadlines. Testing follows a defined process including requirement analysis, planning, design, coding, testing cycles, and implementation. Test cases are developed to validate requirements and check for expected results.
All you need to know about regression testing | David Tzemach
All you need to know about Regression testing| David Tzemach
1. Overview
2. What is “Regression” testing…?
3. When should you use it..?
4. How to implement..?
5. Test Recommendations
6. Considerations when building Regression tests
Regression testing
Regression testing is testing performed after changes to a system to detect whether new errors were introduced or old bugs have reappeared. It should be done after changes to requirements, new features added, defect fixes, or performance improvements. There are various strategies for regression testing including re-running all tests, test selection, test prioritization, and focusing on areas like frequently failing tests or recently changed code. While regression testing helps ensure system quality, managing large test suites over time can be challenging. Automating regression testing helps address these challenges.
Regression and performance testing
This document discusses software regression testing and performance testing. It defines regression testing as retesting software after changes to check for defects in modified areas and unaffected areas. Regression testing happens at the unit, integration, and system levels and requires analyzing software changes and impacts. Performance testing evaluates a system's compliance with performance requirements by testing response times under various loads. It helps ensure applications meet customers' expectations as defined in SLAs. The document discusses performance testing tools and benefits of the approach while cautioning that performance testing is as much an art as a science due to the many variables involved.
Software testing fundamentals
This document provides an overview of software testing concepts and processes. It discusses why testing is important, defines common testing terms, and outlines the typical phases of a testing lifecycle including planning, analysis, implementation, evaluation, and closure. It also describes different testing techniques like static reviews, functional testing, and performance testing. Risks of poor quality like defects, failures, and their impacts on humans, economy, business are highlighted. The roles of testers and differences between quality assurance and quality control are defined. Examples of testing in various industries are provided.
Using language workbenches and domain-specific languages for safety-critical ...
Language workbenches support the efficient creation, integration, and use of domain-specific languages. Typically, they execute models by code generation to programming language code. This can lead to increased productivity and higher quality. However, in safety-/mission-critical environments, generated code may not be considered trustworthy, because of the lack of trust in the generation mechanisms. This makes it harder to justify the use of language workbenches in such an environment. In this paper, we demonstrate an approach to use such tools in critical environments. We argue that models created with domain-specific languages are easier to validate and that the additional risk resulting from the transformation to code can be mitigated by a suitably designed transformation and verification architecture. We validate the approach with an industrial case study from the healthcare domain. We also discuss the degree to which the approach is appropriate for critical software in space, automotive, and robotics systems.
Understand regression testing
Regression testing is important to ensure new software changes do not break existing functionality. Automating regression testing helps manage the large number of test cases needed and speeds up release cycles. Key aspects of managing regression include establishing a baseline, comparing new results to the baseline, debugging failures efficiently, and automating testing processes to reduce human effort and testing time.
Software Testing
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.
Automated visual-regression-testing (1)
This document describes visual regression testing, which compares the visual output of software to detect changes. It introduces the Antenna House Regression Testing System (AHRTS), a tool that automatically compares PDF output documents on a pixel-by-pixel level to test for regressions in new releases of Antenna House Formatter software. AHRTS addresses challenges with manual visual regression testing by offering high-speed performance on large document sets and generating detailed reports on any differences found. The automated approach significantly reduces testing time and effort while improving accuracy and reliability over manual methods.
Software Testing Concepts
The document discusses various types of software testing. It defines software testing as executing a program to find bugs and ensure it meets requirements. There are different types of tests like unit, integration, system, and acceptance. Unit tests check individual code units work as intended. Integration tests check multiple units work together. System tests check the full application in a production environment. Acceptance tests check the software meets business needs. The document emphasizes the importance of testing for quality, flexibility, and avoiding expensive failures. Thorough testing helps ensure customer satisfaction and business success.
Empirically Detecting False Test Alarms Using Association Rules @ ICSE 2015
Applying code changes to software systems and testing these code changes can be a complex task that involves many different types of software testing strategies, e.g. system and integration tests. However, not all test failures reported during code integration are hinting towards code defects. Testing large systems such as the Microsoft Windows operating system requires complex test infrastructures, which may lead to test failures caused by faulty tests and test infrastructure issues. Such false test alarms are particular annoying as they raise engineer attention and require manual inspection without providing any benefit. The goal of this work is to use empirical data to minimize the number of false test alarms reported during system and integration testing. To achieve this goal, we use association rule learning to identify patterns among failing test steps that are typically for false test alarms and can be used to automatically classify them. A successful classification of false test alarms is particularly valuable for the product teams as manual test failure inspection is an expensive and time-consuming process that not only costs engineering time and money but also slows down product development. We evaluating our approach on system and integration tests executed during Windows 8.1 and Microsoft Dynamics AX development. Performing more than 10,000 classifications for each product, our model shows a mean precision between 0.85 and 0.90 predicting between 34% and 48% of all false test alarms.
Performance testing and j meter overview
Performance testing is used to determine how well a system performs under load. Key aspects include throughput, response time, tuning, and benchmarking. JMeter is a popular tool for load and performance testing. It allows recording tests, inserting timers and controllers, running tests, and analyzing results to identify bottlenecks. Common test types include load testing, stress testing, endurance testing, and volume testing. Performance testing helps ensure an application is scalable, stable, available, serviceable, and responsive under expected and unexpected user loads.
Reactive Performance Testing
Case study and guidelines for performance testing microservices. The talk focuses on clarifying the goals of performance testing, suggestions on tools that will help get started and common pitfalls. Originally presented at Reactive Summit 2018.
Software Testing Strategies ,Validation Testing and System Testing.
1. The document presents strategies for software testing by four individuals for their professor Sir Salman Mirza.
2. It discusses various types of software testing like unit testing, integration testing, validation testing, and system testing. Unit testing focuses on individual components while integration testing focuses on how components work together.
3. Validation testing ensures the software meets user requirements, while system testing evaluates the entire integrated system. Testing aims to find errors and should begin early in the development process.
Software maintenance
This document discusses software maintenance and metrics used to measure software complexity. It notes that maintenance makes up 70% of a software's lifecycle costs. Common maintenance activities include enhancements, adaptations, and corrections. Two important source code metrics discussed are Halstead's effort equation and McCabe's cyclomatic complexity, which measure properties of source code like tokens and control flow to evaluate complexity. Maintaining complexity metrics is important during software evolution and maintenance.
Chapter 11 Metrics for process and projects.ppt
This document discusses software process and project metrics. It describes two types of metrics - process metrics and project metrics. Process metrics are collected across projects over long periods of time to enable long-term process improvement. Project metrics enable project managers to assess project status, track risks, uncover problems, adjust work, and evaluate team ability. Measurement data is collected by projects and converted to process metrics for software improvement.
More Related Content
What's hot
Embedded Testing 2015
We deal with the impact of applying DevOps practices (infrastructure as code, continuous integration, release and deployment on demand) in the development of embedded projects spanning the full spectrum of mechanics, electronics and software.
Incorporating devops concepts within the regular test flow enables collaboration between engineers of all three disciplines with very fast feedback cycles.
Software is in a position to provide tools and data to electronics/mechanics to verify design decisions while moving forward with the implementation.
Deliverables are provided at any point in time of the development presenting a constant stream of functioning prototypes from the beginning of development while providing an almost seamless transition from 3D printed proof-of-concept using evaluation boards to integrated device.
In the talk we present the basic DevOps principles and concepts and how they map to established procedures like EOL Tests, affect production considerations and ease hardware-software integration.
MDD and the Tautology Problem: Discussion Notes.
MoDeVVA @ MODELS 2009, Denver. 10/5/2009. What are the dangers of using the same model to both generate a system and the test suite for that system.
The Art of Testing Less without Sacrificing Quality @ ICSE 2015
Testing is a key element of software development processes for the management and assessment of product quality. In most development environments, the software engineers are responsible for ensuring the functional correctness of code. However, for large complex software products, there is an additional need to check that changes do not negatively impact other parts of the software and they comply with system constraints such as backward compatibility, performance, security etc. Ensuring these system constraints may require complex verification infrastructure and test procedures. Although such tests are time consuming and expensive and rarely find defects they act as an insurance process to ensure the software is compliant. However, long lasting tests increasingly conflict with strategic aims to shorten release cycles. To decrease production costs and to improve development agility, we created a generic test selection strategy called THEO that accelerates test processes without sacrificing product quality. THEO is based on a cost model, which dynamically skips tests when the expected cost of running the test exceeds the expected cost of removing it. We replayed past development periods of three major Microsoft products resulting in a reduction of 50% of test executions, saving millions of dollars per year, while maintaining product quality.
Test Smarter: Efficient Coverage Metrics That Won't Leave You Exposed
Achieving optimum test and requirement coverage is critical for delivering high quality applications to the marketplace. Coverage can be very useful in determining the extent to which code and requirement has been tested. However, at the same time, a high coverage result (something in high 80s or 90s) cannot be taken as a measure of effective testing or an indicator of testers achieving perfection. Leveraging processes and tools that help determine sufficient coverage metrics, account for risk, trim costs incurred from unnecessary tests is therefore important.
In this webinar, Nikhil Kaul, Product Manager, joins Engineer, Rick Almeida, to discuss how to balance the trade-off between achieving high test coverage and testing code and requirements that matter. In this session, we will cover:
What coverage metrics to use and what percentages to aim for?
- How coverage metrics can be misused?
- How to use requirement and code coverage well?
- What metrics won’t tell you?
- Establish the connection between requirement and test coverage.
Testing Software Solutions
The document discusses various software testing and evaluation techniques used to ensure software solutions meet design specifications and are free from errors. It covers topics like unit testing, integration testing, system testing, black box and white box testing, test data generation, benchmarking, and quality assurance.
2. The Software Development Process - Design
The document discusses different aspects of program design including breaking problems down into smaller modules using top-down or bottom-up design, using flow charts, structure diagrams, and pseudocode as design notations, and ensuring a good human-computer interface adheres to consistency rules when interacting with users through menus, commands, and data display. The task is to create an Etherpad to develop a class list of human-computer interface rules.
Introduction and Role of a manual testing in a SDLC
Quality assurance involves systematically testing products and services to ensure they meet requirements. Software testing identifies bugs and ensures correctness, completeness, and quality. Common causes of bugs include unclear requirements, miscommunication, complexity, and unrealistic deadlines. Testing follows a defined process including requirement analysis, planning, design, coding, testing cycles, and implementation. Test cases are developed to validate requirements and check for expected results.
All you need to know about regression testing | David Tzemach
All you need to know about Regression testing| David Tzemach
1. Overview
2. What is “Regression” testing…?
3. When should you use it..?
4. How to implement..?
5. Test Recommendations
6. Considerations when building Regression tests
Regression testing
Regression testing is testing performed after changes to a system to detect whether new errors were introduced or old bugs have reappeared. It should be done after changes to requirements, new features added, defect fixes, or performance improvements. There are various strategies for regression testing including re-running all tests, test selection, test prioritization, and focusing on areas like frequently failing tests or recently changed code. While regression testing helps ensure system quality, managing large test suites over time can be challenging. Automating regression testing helps address these challenges.
Regression and performance testing
This document discusses software regression testing and performance testing. It defines regression testing as retesting software after changes to check for defects in modified areas and unaffected areas. Regression testing happens at the unit, integration, and system levels and requires analyzing software changes and impacts. Performance testing evaluates a system's compliance with performance requirements by testing response times under various loads. It helps ensure applications meet customers' expectations as defined in SLAs. The document discusses performance testing tools and benefits of the approach while cautioning that performance testing is as much an art as a science due to the many variables involved.
Software testing fundamentals
This document provides an overview of software testing concepts and processes. It discusses why testing is important, defines common testing terms, and outlines the typical phases of a testing lifecycle including planning, analysis, implementation, evaluation, and closure. It also describes different testing techniques like static reviews, functional testing, and performance testing. Risks of poor quality like defects, failures, and their impacts on humans, economy, business are highlighted. The roles of testers and differences between quality assurance and quality control are defined. Examples of testing in various industries are provided.
Using language workbenches and domain-specific languages for safety-critical ...
Language workbenches support the efficient creation, integration, and use of domain-specific languages. Typically, they execute models by code generation to programming language code. This can lead to increased productivity and higher quality. However, in safety-/mission-critical environments, generated code may not be considered trustworthy, because of the lack of trust in the generation mechanisms. This makes it harder to justify the use of language workbenches in such an environment. In this paper, we demonstrate an approach to use such tools in critical environments. We argue that models created with domain-specific languages are easier to validate and that the additional risk resulting from the transformation to code can be mitigated by a suitably designed transformation and verification architecture. We validate the approach with an industrial case study from the healthcare domain. We also discuss the degree to which the approach is appropriate for critical software in space, automotive, and robotics systems.
Understand regression testing
Regression testing is important to ensure new software changes do not break existing functionality. Automating regression testing helps manage the large number of test cases needed and speeds up release cycles. Key aspects of managing regression include establishing a baseline, comparing new results to the baseline, debugging failures efficiently, and automating testing processes to reduce human effort and testing time.
Software Testing
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.
Automated visual-regression-testing (1)
This document describes visual regression testing, which compares the visual output of software to detect changes. It introduces the Antenna House Regression Testing System (AHRTS), a tool that automatically compares PDF output documents on a pixel-by-pixel level to test for regressions in new releases of Antenna House Formatter software. AHRTS addresses challenges with manual visual regression testing by offering high-speed performance on large document sets and generating detailed reports on any differences found. The automated approach significantly reduces testing time and effort while improving accuracy and reliability over manual methods.
Software Testing Concepts
The document discusses various types of software testing. It defines software testing as executing a program to find bugs and ensure it meets requirements. There are different types of tests like unit, integration, system, and acceptance. Unit tests check individual code units work as intended. Integration tests check multiple units work together. System tests check the full application in a production environment. Acceptance tests check the software meets business needs. The document emphasizes the importance of testing for quality, flexibility, and avoiding expensive failures. Thorough testing helps ensure customer satisfaction and business success.
Empirically Detecting False Test Alarms Using Association Rules @ ICSE 2015
Applying code changes to software systems and testing these code changes can be a complex task that involves many different types of software testing strategies, e.g. system and integration tests. However, not all test failures reported during code integration are hinting towards code defects. Testing large systems such as the Microsoft Windows operating system requires complex test infrastructures, which may lead to test failures caused by faulty tests and test infrastructure issues. Such false test alarms are particular annoying as they raise engineer attention and require manual inspection without providing any benefit. The goal of this work is to use empirical data to minimize the number of false test alarms reported during system and integration testing. To achieve this goal, we use association rule learning to identify patterns among failing test steps that are typically for false test alarms and can be used to automatically classify them. A successful classification of false test alarms is particularly valuable for the product teams as manual test failure inspection is an expensive and time-consuming process that not only costs engineering time and money but also slows down product development. We evaluating our approach on system and integration tests executed during Windows 8.1 and Microsoft Dynamics AX development. Performing more than 10,000 classifications for each product, our model shows a mean precision between 0.85 and 0.90 predicting between 34% and 48% of all false test alarms.
Performance testing and j meter overview
Performance testing is used to determine how well a system performs under load. Key aspects include throughput, response time, tuning, and benchmarking. JMeter is a popular tool for load and performance testing. It allows recording tests, inserting timers and controllers, running tests, and analyzing results to identify bottlenecks. Common test types include load testing, stress testing, endurance testing, and volume testing. Performance testing helps ensure an application is scalable, stable, available, serviceable, and responsive under expected and unexpected user loads.
Reactive Performance Testing
Case study and guidelines for performance testing microservices. The talk focuses on clarifying the goals of performance testing, suggestions on tools that will help get started and common pitfalls. Originally presented at Reactive Summit 2018.
Software Testing Strategies ,Validation Testing and System Testing.
1. The document presents strategies for software testing by four individuals for their professor Sir Salman Mirza.
2. It discusses various types of software testing like unit testing, integration testing, validation testing, and system testing. Unit testing focuses on individual components while integration testing focuses on how components work together.
3. Validation testing ensures the software meets user requirements, while system testing evaluates the entire integrated system. Testing aims to find errors and should begin early in the development process.
What's hot (20)
The Art of Testing Less without Sacrificing Quality @ ICSE 2015
The Art of Testing Less without Sacrificing Quality @ ICSE 2015
Test Smarter: Efficient Coverage Metrics That Won't Leave You Exposed
Test Smarter: Efficient Coverage Metrics That Won't Leave You Exposed
Introduction and Role of a manual testing in a SDLC
Introduction and Role of a manual testing in a SDLC
All you need to know about regression testing | David Tzemach
All you need to know about regression testing | David Tzemach
Using language workbenches and domain-specific languages for safety-critical ...
Using language workbenches and domain-specific languages for safety-critical ...
Empirically Detecting False Test Alarms Using Association Rules @ ICSE 2015
Empirically Detecting False Test Alarms Using Association Rules @ ICSE 2015
Software Testing Strategies ,Validation Testing and System Testing.
Software Testing Strategies ,Validation Testing and System Testing.
Similar to Gathering SW Indicators
Software maintenance
This document discusses software maintenance and metrics used to measure software complexity. It notes that maintenance makes up 70% of a software's lifecycle costs. Common maintenance activities include enhancements, adaptations, and corrections. Two important source code metrics discussed are Halstead's effort equation and McCabe's cyclomatic complexity, which measure properties of source code like tokens and control flow to evaluate complexity. Maintaining complexity metrics is important during software evolution and maintenance.
Chapter 11 Metrics for process and projects.ppt
This document discusses software process and project metrics. It describes two types of metrics - process metrics and project metrics. Process metrics are collected across projects over long periods of time to enable long-term process improvement. Project metrics enable project managers to assess project status, track risks, uncover problems, adjust work, and evaluate team ability. Measurement data is collected by projects and converted to process metrics for software improvement.
Traps detection during migration of C and C++ code to 64-bit Windows
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.
Importance of software quality metrics
Software quality metrics provide important insights into software testing efforts and processes. They can help evaluate products and processes against goals, control resources, and predict future attributes. There are three categories of metrics: process, product, and project. Process metrics measure testing efficiency and effectiveness. Product metrics depict product characteristics like size and quality. Project metrics measure schedule, cost, productivity, and code quality. Choosing metrics based on organizational goals and providing feedback are best practices for an effective metrics program.
SE-Lecture-7.pptx
The document discusses function point analysis (FPA), a method used to estimate the size of a software project based on its functionality. FPA was initially developed by Allan J. Albrecht in 1979 at IBM. It measures the functional size of a software application in terms of function points, which are used to estimate factors like project time and resources required. FPA is independent of programming languages and can be used for various types of software systems. The document also discusses software quality metrics, which focus on measuring the quality of products, processes, and projects. These include metrics like defect density, customer problems, and customer satisfaction.
Introduction & Manual Testing
The document discusses various software development life cycle models and testing methodologies. It introduces the waterfall model, prototyping model, rapid application development model, spiral model, and component assembly model. It then covers testing fundamentals, test case design, white box and black box testing techniques, and the relationships between quality assurance, quality control, verification and validation.
Manualtestingppt
The document discusses various software development life cycle models and testing methodologies. It introduces the waterfall model, prototyping model, rapid application development model, spiral model, and component assembly model. It then covers testing fundamentals, objectives, design of test cases, white box and black box testing techniques, and the relationships between quality assurance, quality control, and validation/verification.
Sw quality metrics
The document discusses various software quality metrics that can be used to measure product quality, in-process quality, and maintenance quality. It describes three main groups of metrics: product metrics, process metrics, and project metrics. Specific metrics discussed include defect density, mean time to failure, customer problems, function points, defect arrival patterns, phase-based defect removal, and customer satisfaction surveys. The purpose of these metrics is to understand relationships between quality factors and improve both the development process and product quality.
Software reliability & quality
Reliability is a measure of how well a system performs its intended function under stated conditions for a specified period of time. It is dependent on factors like the operational profile, fault consequences, and perceived by different users. Various metrics can be used to measure reliability including probability of failure-free operation, mean time between failures, and availability. Reliability models help predict how reliability changes as faults are removed through testing, but no single model is universally applicable.
Mi0033 software engineering
This document provides information about obtaining fully solved assignments from an assignment help service. It lists the email and phone contact information and requests students to send their semester and specialization to receive help with assignments. It also lists some of the programs and subjects that assignments are available for, including MBADS, MBAFLEX, MBAN2, and PGDISMN.
Software Cost Estimation in Software Engineering SE23
Software cost estimation involves predicting resources required for development using metrics like productivity, size, and function points. The COCOMO 2 model is an empirical algorithmic model that estimates effort as a function of product, project, and process attributes. It operates at three levels - early prototyping based on object points, early design using function points mapped to lines of code, and post-architecture directly using lines of code. Key cost drivers include requirements, personnel experience, reuse, and development flexibility.
Software engineering
This document discusses software metrics for processes, projects, and products. It defines metrics as quantitative measures used as management tools to provide insight. Metrics in the process domain are used for strategic decisions, while project metrics enable tactical decisions. Size-oriented metrics normalize measures by lines of code or function points. Function-oriented metrics use functionality as a normalization value. Quality metrics measure correctness and maintainability. Establishing a metrics baseline from past projects allows for process, product, and project improvements.
Problems of testing 64-bit applications
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.
Software Process Models
This document discusses different process models used in software development. It describes the key phases and characteristics of several common process models including waterfall, prototyping, V-model, incremental, iterative, spiral and agile development models. The waterfall model involves sequential phases from requirements to maintenance without iteration. Prototyping allows for user feedback earlier. The V-model adds verification and validation phases. Incremental and iterative models divide the work into smaller chunks to allow for iteration and user feedback throughout development.
Software Risk Analysis
Describes a model to analyze software systems and determine areas of risk. Discusses limitations of typical test design methods and provides an example of how to use the model to create high volume automated testing framework.
Software reliability engineering
Here is an example operations list for a medical enteral pump system:
1. Power on pump
2. Navigate main menu
1. Set patient details
2. Set feeding program
1. Select feeding mode (continuous, intermittent)
2. Set feeding rate
3. Set feeding duration
3. Start/stop feeding
4. View feeding history
5. Adjust alarm settings
3. Acknowledge/silence alarms
4. Power off pump
This list was developed by walking through the menu structure and identifying the key operations a user could perform with the pump system. The numbering indicates sub-operations under main operations.
Lecture3
This document discusses software metrics that can be used to measure process and project attributes. It defines key terms like measurement, measure, metric and indicator. It describes different types of metrics like process metrics, project metrics, size-oriented metrics, function-oriented metrics and quality metrics. It also discusses concepts like defect removal efficiency and redefining defect removal efficiency to measure effectiveness of quality assurance activities.
Software Metrics
The document discusses different types of software metrics that can be used to measure various aspects of software development. Process metrics measure attributes of the development process, while product metrics measure attributes of the software product. Project metrics are used to monitor and control software projects. Metrics need to be normalized to allow for comparison between different projects or teams. This can be done using size-oriented metrics that relate measures to the size of the software, or function-oriented metrics that relate measures to the functionality delivered.
55 sample chapter
The document discusses software metrics and measuring software. It begins with an anecdote about a woman incorrectly trying to measure the length of software with a physical ruler. It then discusses that while software cannot be physically measured, it can be measured through various software metrics. The document goes on to describe different types of software metrics including product, process, and resource metrics and how they are used to measure characteristics of software and the development process.
55 sample chapter
The document discusses software metrics and measuring software. It begins with an anecdote about a woman incorrectly trying to measure the length of software with a physical ruler. It then discusses that while software cannot be physically measured, it can be measured through various software metrics. The document goes on to describe different types of software metrics including product, process, and resource metrics and how they are used to measure characteristics of software and the development process.
Similar to Gathering SW Indicators (20)
Traps detection during migration of C and C++ code to 64-bit Windows
Traps detection during migration of C and C++ code to 64-bit Windows
Software Cost Estimation in Software Engineering SE23
Software Cost Estimation in Software Engineering SE23
Recently uploaded
How to Get CNIC Information System with Paksim Ga.pptx
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
Presentation of the OECD Artificial Intelligence Review of Germany
Consult the full report at https://www.oecd.org/digital/oecd-artificial-intelligence-review-of-germany-609808d6-en.htm
Uni Systems Copilot event_05062024_C.Vlachos.pdf
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Building Production Ready Search Pipelines with Spark and Milvus
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
“I’m still / I’m still / Chaining from the Block”
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
UiPath Test Automation using UiPath Test Suite series, part 6
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Artificial Intelligence for XMLDevelopment
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Climate Impact of Software Testing at Nordic Testing Days
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.20240609 QFM020 Irresponsible AI Reading List May 2024
Everything I found interesting about the irresponsible use of machine intelligence in May 2024
Communications Mining Series - Zero to Hero - Session 1
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
TrustArc Webinar - 2024 Global Privacy Survey
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
AI 101: An Introduction to the Basics and Impact of Artificial Intelligence
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
Recently uploaded (20)
How to Get CNIC Information System with Paksim Ga.pptx
How to Get CNIC Information System with Paksim Ga.pptx
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
Presentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of Germany
Building Production Ready Search Pipelines with Spark and Milvus
Building Production Ready Search Pipelines with Spark and Milvus
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for Life Science to increase LLM accuracy
UiPath Test Automation using UiPath Test Suite series, part 6
UiPath Test Automation using UiPath Test Suite series, part 6
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
Climate Impact of Software Testing at Nordic Testing Days
Climate Impact of Software Testing at Nordic Testing Days
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
20240609 QFM020 Irresponsible AI Reading List May 2024
20240609 QFM020 Irresponsible AI Reading List May 2024
Communications Mining Series - Zero to Hero - Session 1
Communications Mining Series - Zero to Hero - Session 1
AI 101: An Introduction to the Basics and Impact of Artificial Intelligence
AI 101: An Introduction to the Basics and Impact of Artificial Intelligence
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
Gathering SW Indicators
- 1. Gathering SW Indicators What SW indicators may be gathered from CM & Bug tracking tools by Kiril Serebnik
- 24. Development effort number of lines delta number of closed changesets