The document discusses metrics for tracking a project's test management process. It provides examples of metrics that could be captured at different stages of the testing lifecycle, including test execution rates, defect rates, requirements tracing, and environment issues. Guidelines are also presented for establishing a process to define, collect, analyze, and report on metrics on a regular basis to improve visibility and decision making.
Software Test Metrics and MeasurementsDavis Thomas
Explains in detail with example about calculation of -
1.Percentage Test cases Executed [Test Coverage]
2.Percentage Test cases not executed
3.Percentage Test cases Passed
4.Percentage Test cases Failed
5.Percentage Test cases BLOCKED/Deferred
6.Defect Density
7.Defect Removal Efficiency (DRE)
8.Defect Leakage
9.Defect Rejection ratio [Invalid bug ratio]
10.Percentage of Critical defects
11.Percentage of High defects
12.Percentage of Medium defects
13.Percentage of Low/Lowest defects
To be most effective, test managers must develop and use metrics to help direct the testing effort and make informed recommendations about the software’s release readiness and associated risks. Because one important testing activity is to “measure” the quality of the software, test managers must measure the results of both the development and testing processes. Collecting, analyzing, and using metrics is complicated because many developers and testers are concerned that the metrics will be used against them. Join Rick Craig as he addresses common metrics—measures of product quality, defect removal efficiency, defect density, defect arrival rate, and testing status. Learn the guidelines for developing a test measurement program, rules of thumb for collecting data, and ways to avoid “metrics dysfunction.” Rick identifies several metrics paradigms and discusses the pros and cons of each. Delegates are urged to bring their metrics problems and issues for use as discussion points.
Testing metrics provide visibility into software quality and the testing process. Some key metrics include defect severity index, number of defects found, and test case effectiveness. It is important to analyze metrics over time and consider other factors, as metrics alone can sometimes be misleading. Looking at trends in multiple metrics together can provide valuable insights about software quality and areas for improvement.
Software testing metrics | David Tzemach David Tzemach
Overview
What we can measure using metrics
Common metrics to evaluate test process
why do we need to use metrics
Test metrics life cycle (TMLC)
Type of metrics
Fundamental testing metrics
Reliable Relevant Metrics to the Right Audience - Manual Testing WhitepaperIndium Software
'What cannot be measured cannot be managed” is the guiding philosophy behind testing metrics, a phenomenon that promises to deliver business efficiencies beyond just improving quality. Measurement helps with planning, tracking and managing the software project and enables organizations to objectively assess quality
The document discusses various metrics that can be used to measure product quality and testing effectiveness. It describes 12 categories of metrics including customer satisfaction, defect quantities, responsiveness, product volatility, defect ratios, defect removal efficiency, complexity, test coverage, costs of defects, costs of quality activities, re-work, and reliability. It also provides examples of specific metrics that can be measured within each category such as number of defects found, test coverage percentage, costs of testing, and reliability metrics like mean time between failures.
The document defines several formulas for calculating metrics related to testing efforts: % Effort Variation compares actual and estimated effort, % Duration Variation compares actual and planned durations, and % Schedule Variation compares actual and planned end dates. Other metrics include Load Factor, %Size Variation, Test Case Coverage%, Residual Defects Density, Test Effectiveness, Overall Productivity, Test Case Preparation Productivity, and Test Execution Productivity.
Software Test Metrics and MeasurementsDavis Thomas
Explains in detail with example about calculation of -
1.Percentage Test cases Executed [Test Coverage]
2.Percentage Test cases not executed
3.Percentage Test cases Passed
4.Percentage Test cases Failed
5.Percentage Test cases BLOCKED/Deferred
6.Defect Density
7.Defect Removal Efficiency (DRE)
8.Defect Leakage
9.Defect Rejection ratio [Invalid bug ratio]
10.Percentage of Critical defects
11.Percentage of High defects
12.Percentage of Medium defects
13.Percentage of Low/Lowest defects
To be most effective, test managers must develop and use metrics to help direct the testing effort and make informed recommendations about the software’s release readiness and associated risks. Because one important testing activity is to “measure” the quality of the software, test managers must measure the results of both the development and testing processes. Collecting, analyzing, and using metrics is complicated because many developers and testers are concerned that the metrics will be used against them. Join Rick Craig as he addresses common metrics—measures of product quality, defect removal efficiency, defect density, defect arrival rate, and testing status. Learn the guidelines for developing a test measurement program, rules of thumb for collecting data, and ways to avoid “metrics dysfunction.” Rick identifies several metrics paradigms and discusses the pros and cons of each. Delegates are urged to bring their metrics problems and issues for use as discussion points.
Testing metrics provide visibility into software quality and the testing process. Some key metrics include defect severity index, number of defects found, and test case effectiveness. It is important to analyze metrics over time and consider other factors, as metrics alone can sometimes be misleading. Looking at trends in multiple metrics together can provide valuable insights about software quality and areas for improvement.
Software testing metrics | David Tzemach David Tzemach
Overview
What we can measure using metrics
Common metrics to evaluate test process
why do we need to use metrics
Test metrics life cycle (TMLC)
Type of metrics
Fundamental testing metrics
Reliable Relevant Metrics to the Right Audience - Manual Testing WhitepaperIndium Software
'What cannot be measured cannot be managed” is the guiding philosophy behind testing metrics, a phenomenon that promises to deliver business efficiencies beyond just improving quality. Measurement helps with planning, tracking and managing the software project and enables organizations to objectively assess quality
The document discusses various metrics that can be used to measure product quality and testing effectiveness. It describes 12 categories of metrics including customer satisfaction, defect quantities, responsiveness, product volatility, defect ratios, defect removal efficiency, complexity, test coverage, costs of defects, costs of quality activities, re-work, and reliability. It also provides examples of specific metrics that can be measured within each category such as number of defects found, test coverage percentage, costs of testing, and reliability metrics like mean time between failures.
The document defines several formulas for calculating metrics related to testing efforts: % Effort Variation compares actual and estimated effort, % Duration Variation compares actual and planned durations, and % Schedule Variation compares actual and planned end dates. Other metrics include Load Factor, %Size Variation, Test Case Coverage%, Residual Defects Density, Test Effectiveness, Overall Productivity, Test Case Preparation Productivity, and Test Execution Productivity.
This document discusses various software testing metrics including defect density, requirement volatility, test execution productivity, and test efficiency. Defect density measures the number of defects found divided by the size of the software. Requirement volatility measures the percentage of original requirements that were changed. Test execution productivity measures the number of test cases executed per day. Test efficiency measures the percentage of defects found during testing versus post-release. These metrics provide ways to measure software quality and testing effectiveness.
Testing metrics provide objective measurements of software quality and the testing process. They measure attributes like test coverage, defect detection rates, and requirement changes. There are base metrics that directly capture raw data like test cases run and results, and calculated metrics that analyze the base metrics, like first run failure rates and defect slippage. Tracking these metrics throughout testing provides visibility into project readiness, informs management decisions, and identifies areas for improvement. Regular review and interpretation of the metrics is needed to understand their implications and make changes to the development lifecycle.
Testing a software for its efficiency requires a concentrated effort in terms of a quantified test data metrics. This PPT will shed light on Types & need of Metrics, OS/ Browser compatibility Matrix, Test Efficiency, Test Effectiveness and DRE(Defect Resolution Effectiveness) to enhance your understanding on the need and relevance of a test data metrics.
Innovations in Test Automation: It’s Not All about RegressionTechWell
Although classic test automation, which usually focuses on regression testing, has its its place in testing, there is much more you can do to improve testing productivity and its value to the project and your organization. Through experience-based examples, video clips, and demonstrations, John Fodeh shares one company’s innovation journey to improve its test automation practice. John illustrates how they learned to apply automated “test monkeys” that explore the software in new ways each time a test is executed. Then, he describes how the test team uses weighted probability tables to increase each test’s “intelligence” factor. Find out how they implemented model-based testing to improve automation effectiveness and how this practice led to the even more valuable behavior-driven testing approach they employ today. With these and other alternative approaches you, too, can get more mileage from your automation efforts. Join John to get inspired and start your own journey of innovation with new ideas that enhance your test automation strategy.
Software Testing is a very time consuming activity and consumes enormous amount of effort in any software project. It makes sense to improve productivity of software testing as well as to reduce the defect density in the software, so that overall economy in the project is achieved. In order to do this, we need to understand the defects, their root causes and be able to predict their outcome in advance during estimation.
This presentation by Oaksys is an attempt to share its experience of over 10 years (1998-2008) with the practitioners.
Learn software testing with tech partnerz 3Techpartnerz
Software configuration management identifies and controls all changes made during software development and after release. It organizes all information produced during engineering into a configuration that enables orderly control of changes. Some key items included in a software configuration are management and specification plans, source code, databases, and production documentation.
Software testing metrics are used extensively by many organizations to determine the status of their projects and whether or not their products are ready to ship. Unfortunately most, if not all, of the metrics being used are so flawed that they are not only useless but are possibly dangerous—misleading decision makers, inadvertently encouraging unwanted behavior, or providing overly simplistic summaries out of context. Paul Holland identifies four characteristics that will enable you to recognize the bad metrics in your organization. Despite showing how the majority of metrics used today are “bad”, all is not lost as Paul shows the collection of information he has developed that is more effective. Learn how to create a status report that provides details sought after by upper management while avoiding the problems that bad metrics cause.
'Houston We Have A Problem' by Rien van Vugt & Maurice SiteurTEST Huddle
Prevent the surprise, become a pro-active test manager. Too often projects suddenly seem to spin out of control. Challenges and risks keep stacking up and the defect count grows exponentially. At the same time, management can put pressure on you, asking when testing will be completed.
A surprise? Not really, defects only paint half the picture. The test effort, after all, is primarily determined by the number of tests that need to be completed. For an on the spot status of testing and accurate view on the quality and risks of the entire project we need to organize the test process to provide flexible, up-to-date metrics and trends on a daily basis. E.g. we need a view on baseline vs. actuals and ETC’s on test cases. Advanced metrics will provide answers on what needs to be done tomorrow to stay on track, the location and root cause of issues and who is required to take action. Also the test effort remaining for an acceptable product (or a specific risk level) can be estimated fairly accurately.
In addition early involvement and preparation in the development life cycle, performing test intakes rather than reviews, will help you bridge the gap between different development teams and allows you to verify consistency between business requirements, the integration model, functional specifications and technical specifications. It facilitates knowledge transfer and provides you with the “story” behind the specifications. This will help prevent structural issues in an early stage and avoid blocking issues during test execution.
This presentation combines daily test metrics and trends with test process dynamics and shows you how to become a “pro-active” test manager. Even better you can apply it tomorrow and take your test process to a distinct higher maturity level.
The document discusses various metrics that can be collected during software testing. It describes metrics for the requirements phase like requirement stability index and requirements leakage index. For the test design phase, it discusses metrics like test case preparation productivity. Metrics for the test execution phase include test case pass percentage and test case execution percentage. Defect metrics covered are defect summary, defect discovery rate, defect severity, defect density, and defect rejection ratio. Formulas for calculating these metrics are provided along with descriptions of what each metric measures.
Test planning involves defining the scope, objectives, and activities for testing a project. It is done early in the project and produces a master test plan. Key activities include identifying what needs testing, assigning roles and resources, and defining entry and exit criteria. Estimating test effort can be done using metrics from past projects or by eliciting estimates from subject matter experts. Product characteristics, development processes, and expected test outcomes all impact the level of effort required for testing.
Rob Baarda - Are Real Test Metrics Predictive for the Future?TEST Huddle
EuroSTAR Software Testing Conference 2008 presentation on Are Real Test Metrics Predictive for the Future? by Rob Baarda. See more at conferences.eurostarsoftwaretesting.com/past-presentations/
T19 performance testing effort - estimation or guesstimation revisedTEST Huddle
This document discusses performance testing estimation and provides tips to improve the estimation process. It recommends dividing estimation into stages like requirements analysis, design, development, testing and delivery. Key factors to consider include non-functional requirements, test cases, test runs, server monitoring needs, and data/environment setup. Tasks that typically consume more time include scripting, test execution and data setup. The document emphasizes estimating early, listing assumptions, and using a technique rather than guessing to improve accuracy.
Software testing is an essential activity of the software development lifecycle. To ensure quality, applicability, and usefulness of a product, development teams must spend considerable time and resources testing, which makes the estimation of the software testing effort, a critical activity. This presentation presents a simple and useful method called qEstimation to estimate the size and effort of the software testing activities. The method measures the size of the test case in terms of test case points based on its checkpoints, preconditions and test data, as well as the type of testing. The testing effort is then computed using the size estimated in test case points. All calculations are embedded in a simple Excel tool, allowing estimators easily to estimate testing effort by providing test cases and their complexity.
The document discusses various topics related to software testing including:
1. It introduces different levels of testing in the software development lifecycle like component testing, integration testing, system testing and acceptance testing.
2. It discusses the importance of early test design and planning and its benefits like reducing costs and improving quality.
3. It provides examples of how not planning tests properly can increase costs due to bugs found late in the process, and outlines the typical costs involved in fixing bugs at different stages.
Robert Magnusson - TMMI Level 2 - A Practical ApproachTEST Huddle
EuroSTAR Software Testing Conference 2008 presentation on TMMI Level 2 - A Practical Approach by Robert Magnusson. See more at conferences.eurostarsoftwaretesting.com/past-presentations/
The document discusses software inspection, which involves reviewing software artifacts like analysis, designs, and code with others besides the original developer. Inspections aim to find errors early in development. An inspection team consists of 3-8 members filling roles like moderator, author, reader, and recorder. Benefits include new perspectives finding flaws, knowledge sharing, and catching defects early to reduce rework and testing effort. Studies found inspections can reduce "rework" costs by 50% and save 20 hours of testing for every 1 hour spent inspecting.
IT Quality Testing and the Defect Management ProcessYolanda Williams
This document provides an overview of defect management processes. It discusses defining defects, defect prevention, discovery, resolution and process improvement. The key aspects covered are:
- Defining goals as preventing defects, early detection, minimizing impact and process improvement.
- Activities like root cause analysis, escape analysis and process metrics.
- The defect lifecycle of prevention, discovery, resolution and continuous improvement.
- Examples of defect analysis and status reporting including metrics like density, backlog and mean time to repair.
Ho Chi Minh City Software Testing Conference January 2015
Software Testing in the Agile World
Website: www.hcmc-stc.org
Author: Richard Taylor
Agile teams don’t need traditional metrics: we do everything so quickly that we only need to know our velocity and cycle time". Is this an extreme claim, or is it realistic? When it's possible to implement a completely pure and simple Agile methodology, and react to all feedback almost immediately, it might be true. It's certainly true that some of the metrics which work well in other types of project lifecycle aren't useful in an Agile one. But are test metrics irrelevant in a large Agile project, with multiple teams and a formal release mechanism? What happens when an Agile project has to comply with standards, or with regulatory requirements, to produce proof of product quality? And even if those things aren’t true, aren't there some things we can measure that will tell us how good our Agile testing is, and how it might get better? This presentation should be helpful to anybody who is, or will be, testing in or managing an Agile project team. In it, Richard Taylor explains how to make some of his favourite test metrics useful in an Agile environment and why some others might better be avoided. Various types of coverage, effectiveness and weighted defect measures are explained and demonstrated. Richard shows how we can present both product and process metrics in a way that gives their message clearly to all interested people, including those from the business and from management who aren’t IT specialists.
Michael Snyman - Software Test Automation Success TEST Huddle
EuroSTAR Software Testing Conference 2009 presentation on Software Test Automation Success by Michael Snyman. See more at conferences.eurostarsoftwaretesting.com/past-presentations/
The document discusses the history and current state of software testing certification. It covers:
1) The ISTQB/ISEB certification program began in the late 1990s and early 2000s to standardize software testing knowledge and professionalize the field.
2) The certifications include Foundation, Practitioner, and Specialist levels to cater to candidates with different experience levels.
3) International collaboration through the ISTQB has led to widespread adoption of a common certification syllabus across many countries.
The document discusses developing a control plan review system for a supplier. It identifies that the current process lacks a mechanism for reviewing control plans. The project aims to develop an online system for submitting, reviewing, and approving quality plans. This will help standardize the process and ensure all quality plans are properly reviewed. The turtle diagram methodology is used to map the process before and after the improvement. The project is expected to improve process efficiency and effectiveness.
The document provides a summary of an individual's career accomplishments and experience in various areas including technical, operational, business, project management, manufacturing, quality, supply chain, logistics, training, and IT skills. It also lists accomplishments in reducing metrics related to service turnaround time, on-time delivery, development time, and inventory. Finally, it discusses experience with people and soft skills like performance management, coaching, training, and customer contact.
This document discusses various software testing metrics including defect density, requirement volatility, test execution productivity, and test efficiency. Defect density measures the number of defects found divided by the size of the software. Requirement volatility measures the percentage of original requirements that were changed. Test execution productivity measures the number of test cases executed per day. Test efficiency measures the percentage of defects found during testing versus post-release. These metrics provide ways to measure software quality and testing effectiveness.
Testing metrics provide objective measurements of software quality and the testing process. They measure attributes like test coverage, defect detection rates, and requirement changes. There are base metrics that directly capture raw data like test cases run and results, and calculated metrics that analyze the base metrics, like first run failure rates and defect slippage. Tracking these metrics throughout testing provides visibility into project readiness, informs management decisions, and identifies areas for improvement. Regular review and interpretation of the metrics is needed to understand their implications and make changes to the development lifecycle.
Testing a software for its efficiency requires a concentrated effort in terms of a quantified test data metrics. This PPT will shed light on Types & need of Metrics, OS/ Browser compatibility Matrix, Test Efficiency, Test Effectiveness and DRE(Defect Resolution Effectiveness) to enhance your understanding on the need and relevance of a test data metrics.
Innovations in Test Automation: It’s Not All about RegressionTechWell
Although classic test automation, which usually focuses on regression testing, has its its place in testing, there is much more you can do to improve testing productivity and its value to the project and your organization. Through experience-based examples, video clips, and demonstrations, John Fodeh shares one company’s innovation journey to improve its test automation practice. John illustrates how they learned to apply automated “test monkeys” that explore the software in new ways each time a test is executed. Then, he describes how the test team uses weighted probability tables to increase each test’s “intelligence” factor. Find out how they implemented model-based testing to improve automation effectiveness and how this practice led to the even more valuable behavior-driven testing approach they employ today. With these and other alternative approaches you, too, can get more mileage from your automation efforts. Join John to get inspired and start your own journey of innovation with new ideas that enhance your test automation strategy.
Software Testing is a very time consuming activity and consumes enormous amount of effort in any software project. It makes sense to improve productivity of software testing as well as to reduce the defect density in the software, so that overall economy in the project is achieved. In order to do this, we need to understand the defects, their root causes and be able to predict their outcome in advance during estimation.
This presentation by Oaksys is an attempt to share its experience of over 10 years (1998-2008) with the practitioners.
Learn software testing with tech partnerz 3Techpartnerz
Software configuration management identifies and controls all changes made during software development and after release. It organizes all information produced during engineering into a configuration that enables orderly control of changes. Some key items included in a software configuration are management and specification plans, source code, databases, and production documentation.
Software testing metrics are used extensively by many organizations to determine the status of their projects and whether or not their products are ready to ship. Unfortunately most, if not all, of the metrics being used are so flawed that they are not only useless but are possibly dangerous—misleading decision makers, inadvertently encouraging unwanted behavior, or providing overly simplistic summaries out of context. Paul Holland identifies four characteristics that will enable you to recognize the bad metrics in your organization. Despite showing how the majority of metrics used today are “bad”, all is not lost as Paul shows the collection of information he has developed that is more effective. Learn how to create a status report that provides details sought after by upper management while avoiding the problems that bad metrics cause.
'Houston We Have A Problem' by Rien van Vugt & Maurice SiteurTEST Huddle
Prevent the surprise, become a pro-active test manager. Too often projects suddenly seem to spin out of control. Challenges and risks keep stacking up and the defect count grows exponentially. At the same time, management can put pressure on you, asking when testing will be completed.
A surprise? Not really, defects only paint half the picture. The test effort, after all, is primarily determined by the number of tests that need to be completed. For an on the spot status of testing and accurate view on the quality and risks of the entire project we need to organize the test process to provide flexible, up-to-date metrics and trends on a daily basis. E.g. we need a view on baseline vs. actuals and ETC’s on test cases. Advanced metrics will provide answers on what needs to be done tomorrow to stay on track, the location and root cause of issues and who is required to take action. Also the test effort remaining for an acceptable product (or a specific risk level) can be estimated fairly accurately.
In addition early involvement and preparation in the development life cycle, performing test intakes rather than reviews, will help you bridge the gap between different development teams and allows you to verify consistency between business requirements, the integration model, functional specifications and technical specifications. It facilitates knowledge transfer and provides you with the “story” behind the specifications. This will help prevent structural issues in an early stage and avoid blocking issues during test execution.
This presentation combines daily test metrics and trends with test process dynamics and shows you how to become a “pro-active” test manager. Even better you can apply it tomorrow and take your test process to a distinct higher maturity level.
The document discusses various metrics that can be collected during software testing. It describes metrics for the requirements phase like requirement stability index and requirements leakage index. For the test design phase, it discusses metrics like test case preparation productivity. Metrics for the test execution phase include test case pass percentage and test case execution percentage. Defect metrics covered are defect summary, defect discovery rate, defect severity, defect density, and defect rejection ratio. Formulas for calculating these metrics are provided along with descriptions of what each metric measures.
Test planning involves defining the scope, objectives, and activities for testing a project. It is done early in the project and produces a master test plan. Key activities include identifying what needs testing, assigning roles and resources, and defining entry and exit criteria. Estimating test effort can be done using metrics from past projects or by eliciting estimates from subject matter experts. Product characteristics, development processes, and expected test outcomes all impact the level of effort required for testing.
Rob Baarda - Are Real Test Metrics Predictive for the Future?TEST Huddle
EuroSTAR Software Testing Conference 2008 presentation on Are Real Test Metrics Predictive for the Future? by Rob Baarda. See more at conferences.eurostarsoftwaretesting.com/past-presentations/
T19 performance testing effort - estimation or guesstimation revisedTEST Huddle
This document discusses performance testing estimation and provides tips to improve the estimation process. It recommends dividing estimation into stages like requirements analysis, design, development, testing and delivery. Key factors to consider include non-functional requirements, test cases, test runs, server monitoring needs, and data/environment setup. Tasks that typically consume more time include scripting, test execution and data setup. The document emphasizes estimating early, listing assumptions, and using a technique rather than guessing to improve accuracy.
Software testing is an essential activity of the software development lifecycle. To ensure quality, applicability, and usefulness of a product, development teams must spend considerable time and resources testing, which makes the estimation of the software testing effort, a critical activity. This presentation presents a simple and useful method called qEstimation to estimate the size and effort of the software testing activities. The method measures the size of the test case in terms of test case points based on its checkpoints, preconditions and test data, as well as the type of testing. The testing effort is then computed using the size estimated in test case points. All calculations are embedded in a simple Excel tool, allowing estimators easily to estimate testing effort by providing test cases and their complexity.
The document discusses various topics related to software testing including:
1. It introduces different levels of testing in the software development lifecycle like component testing, integration testing, system testing and acceptance testing.
2. It discusses the importance of early test design and planning and its benefits like reducing costs and improving quality.
3. It provides examples of how not planning tests properly can increase costs due to bugs found late in the process, and outlines the typical costs involved in fixing bugs at different stages.
Robert Magnusson - TMMI Level 2 - A Practical ApproachTEST Huddle
EuroSTAR Software Testing Conference 2008 presentation on TMMI Level 2 - A Practical Approach by Robert Magnusson. See more at conferences.eurostarsoftwaretesting.com/past-presentations/
The document discusses software inspection, which involves reviewing software artifacts like analysis, designs, and code with others besides the original developer. Inspections aim to find errors early in development. An inspection team consists of 3-8 members filling roles like moderator, author, reader, and recorder. Benefits include new perspectives finding flaws, knowledge sharing, and catching defects early to reduce rework and testing effort. Studies found inspections can reduce "rework" costs by 50% and save 20 hours of testing for every 1 hour spent inspecting.
IT Quality Testing and the Defect Management ProcessYolanda Williams
This document provides an overview of defect management processes. It discusses defining defects, defect prevention, discovery, resolution and process improvement. The key aspects covered are:
- Defining goals as preventing defects, early detection, minimizing impact and process improvement.
- Activities like root cause analysis, escape analysis and process metrics.
- The defect lifecycle of prevention, discovery, resolution and continuous improvement.
- Examples of defect analysis and status reporting including metrics like density, backlog and mean time to repair.
Ho Chi Minh City Software Testing Conference January 2015
Software Testing in the Agile World
Website: www.hcmc-stc.org
Author: Richard Taylor
Agile teams don’t need traditional metrics: we do everything so quickly that we only need to know our velocity and cycle time". Is this an extreme claim, or is it realistic? When it's possible to implement a completely pure and simple Agile methodology, and react to all feedback almost immediately, it might be true. It's certainly true that some of the metrics which work well in other types of project lifecycle aren't useful in an Agile one. But are test metrics irrelevant in a large Agile project, with multiple teams and a formal release mechanism? What happens when an Agile project has to comply with standards, or with regulatory requirements, to produce proof of product quality? And even if those things aren’t true, aren't there some things we can measure that will tell us how good our Agile testing is, and how it might get better? This presentation should be helpful to anybody who is, or will be, testing in or managing an Agile project team. In it, Richard Taylor explains how to make some of his favourite test metrics useful in an Agile environment and why some others might better be avoided. Various types of coverage, effectiveness and weighted defect measures are explained and demonstrated. Richard shows how we can present both product and process metrics in a way that gives their message clearly to all interested people, including those from the business and from management who aren’t IT specialists.
Michael Snyman - Software Test Automation Success TEST Huddle
EuroSTAR Software Testing Conference 2009 presentation on Software Test Automation Success by Michael Snyman. See more at conferences.eurostarsoftwaretesting.com/past-presentations/
The document discusses the history and current state of software testing certification. It covers:
1) The ISTQB/ISEB certification program began in the late 1990s and early 2000s to standardize software testing knowledge and professionalize the field.
2) The certifications include Foundation, Practitioner, and Specialist levels to cater to candidates with different experience levels.
3) International collaboration through the ISTQB has led to widespread adoption of a common certification syllabus across many countries.
The document discusses developing a control plan review system for a supplier. It identifies that the current process lacks a mechanism for reviewing control plans. The project aims to develop an online system for submitting, reviewing, and approving quality plans. This will help standardize the process and ensure all quality plans are properly reviewed. The turtle diagram methodology is used to map the process before and after the improvement. The project is expected to improve process efficiency and effectiveness.
The document provides a summary of an individual's career accomplishments and experience in various areas including technical, operational, business, project management, manufacturing, quality, supply chain, logistics, training, and IT skills. It also lists accomplishments in reducing metrics related to service turnaround time, on-time delivery, development time, and inventory. Finally, it discusses experience with people and soft skills like performance management, coaching, training, and customer contact.
The document outlines the control phase tools and activities for a Lean Six Sigma project. It includes reviewing project documentation, validating goals and benefits, developing standard operating procedures and controls, implementing and monitoring the solution, confirming attainment of goals, identifying opportunities for replication, and transitioning the project to the process owner. Key metrics are monitored to ensure the process remains in control. Lessons learned are captured to improve future projects.
The document outlines the control phase tools and activities in a Lean Six Sigma project. It includes reviewing project documentation and metrics, developing standard operating procedures and controls, implementing and monitoring the solution, confirming goals are met, identifying opportunities for replication, and transitioning the project to the process owner. Key steps are developing a control plan to monitor processes and respond to variation, updating failure modes and effects analysis, and communicating project results and benefits.
This document discusses various types of software testing performed at different stages of the software development lifecycle. It describes component testing, integration testing, system testing, and acceptance testing. Component testing involves testing individual program units in isolation. Integration testing combines components and tests their interactions, starting small and building up. System testing evaluates the integrated system against functional and non-functional requirements. Acceptance testing confirms the system meets stakeholder needs.
The document is a presentation by Yuriy Malyi on simple QA audits. It begins with introducing the speaker and his company. It then provides definitions of audits from standards and discusses common reasons organizations conduct audits. The presentation outlines the audit process and focuses on auditing areas like product management, project management, Scrum management, QA processes, and configuration management. It presents points systems to rate compliance in these areas and shows results of applying this to a sample project.
Want to set up a Digital Signage network Business TV or Advertising and get best garanties? Go for a method that afford you to secure development and quality of service
Production Performance Results Internet Sampleantonioharenas
The document provides a monthly business review for December 2003 containing the following key information:
- Key productivity, quality, and cost issues including high absenteeism, purchasing delays, plastics delivery and quality problems, and rework.
- Actions being taken such as improving operator skills, monitoring suppliers' improvement plans, and teams working to address opportunities.
- Performance data for the month such as productivity, quality defect analysis, rework costs, scrap costs, and overtime analysis.
This document discusses the benefits of automation testing based on a company's experience. It provides details on the features of their automation framework including being modular, data-driven, and using object-oriented programming principles. A table shows the types of tests automated, manual regression efforts previously spent, automation execution efforts, and total effort saved by automating various test suites. In total, automation saved over 2,000 man days of effort, reduced testing time per sprint by 2 weeks, and found over 100 bugs in regression testing.
Kanban India 2023 | Renjith Achuthanunni and Anoop Kadur Vijayakumar | DevOps...LeanKanbanIndia
- The document discusses how an engineering team at Epsilon India established a DevOps metrics dashboard to improve their product development flow and quality.
- Key metrics like defects submitted, open defects, time to resolve issues, and time from commit to deploy were visualized. Tiered service level thresholds were also defined.
- Through iterative refinement and embracing continuous improvement (Kaizen), the team improved predictability from 90 to 30 days, and reduced defects and SLA breaches by 30-50% after implementing the dashboard and optimization efforts.
Real time trend and failure analysis using TTA-Anand Bagmar & Aasawaree Deshmukhbhumika2108
This document describes Test Trend Analyzer (TTA), a tool that provides visualizations and reports on test automation results to gauge the health of a product portfolio. TTA collects test run data, performs trend and failure analysis, and generates customizable reports. It integrates with continuous integration systems to automatically collect results and provide stakeholders with a single click view of test status.
Innovative Practices in Software Quality FacilitationSPIN Chennai
This presentation gives a practitioner's view of successful quality assurance process by adopting various practices and process facilitation with framework of workload distribution and its metrics.
The document outlines the schedule and milestones for a service pack 3 system release. Key dates and activities are listed on a timeline, including integration builds, testing gates, certification activities, documentation tasks and final release. The process involves scoping work, development, iterative testing, performance validation, documentation, sign-offs and ultimately a field readiness review before the final customer shipment date.
This document outlines the steps in a Six Sigma DMAIC process improvement project. It includes defining critical metrics, measuring current performance, analyzing processes, improving processes through pilot tests, and controlling ongoing performance. Key steps are defining customer needs, measuring baseline performance, determining critical factors, piloting solutions, creating control systems, and finalizing documentation.
This document outlines the steps in a Six Sigma DMAIC process improvement project. It includes defining the problem and critical metrics, measuring current performance, analyzing processes and measurements, improving the processes, and controlling future performance. Key steps are defining critical metrics, establishing baselines, determining root causes, piloting solutions, creating control systems, and finalizing documentation.
This document outlines a project to reduce cycle times in a manufacturing process. It provides details on the project charter, stakeholders, data collection plans, measurement systems analysis, and potential factors ("Xs") that could impact cycle times. The goal is to reduce cycle times from 405 to 300 seconds by June 15th. Current process capability is calculated and cycle time data is found to not be normally distributed. Potential factors are identified for further analysis to determine their impact on cycle times.
#Interactive Session by Dinesh Boravke, "Zero Defects – Myth or Reality" at #...Agile Testing Alliance
#Interactive Session by Dinesh Boravke, "Zero Defects – Myth or Reality" at #ATAGTR2023.
#ATAGTR2023 was the 8th Edition of Global Testing Retreat.
To know more about #ATAGTR2023, please visit: https://gtr.agiletestingalliance.org/
Here is the FMEA analysis for the LTE Release Management process:
1. Execute Patch Release Testing
- Incomplete testing due to time constraints
- Defects passed through to next stage
8
- Lack of test resources/capacity
- Complexity of software
- Timeboxed testing
7
- Test automation
- Peer review of test cases
6
336
2. Patch under Pilot
- Defects identified in Pilot phase
- Delays resolution and rollout
7
- Insufficient Pilot scope/scale
- Quality of testing prior to Pilot
6
- Staged Pilot rollout
-
Otto Vinter - Analysing Your Defect Data for Improvement PotentialTEST Huddle
EuroSTAR Software Testing Conference 2008 presentation on Analysing Your Defect Data for Improvement Potential by Otto Vinter. See more at conferences.eurostarsoftwaretesting.com/past-presentations/
Otto Vinter - Analysing Your Defect Data for Improvement Potential
Test Metrics
1. The Metrics Lifecycle Questions before starting to use metrics are: * Who will use these metrics? * What behavior are you trying to promote with these metrics? * What information is important to know across the project/enterprise? * What requires increased visibility or transparency?
2. Inception 6/2010 Interim 1/2011 Comprehensive 6/2011 Test Planning Test Creation Test Execution Defect Management Configuration Management Cost – Project, BA, Dev, QA Test Scripts Built Pass, Failed, Risk* # Scripts, Timeline Status, Severity Check Points, Test Plan, Traceability Matrix Trend, Block, No Runs BA/Dev/Bus Accepted/Rejected Test Automation Discovery Rate, Removal Rate, Trend, QA Impact (time, testers) Uptime, Domain Version verification, Release Notes Removal Cost, Root Cause BA/Dev/Bus Accepted/Rejected Build Cost # Scripts, Timeline Pass, Failed, Usage Artifact Lockdown Sample Metrics Stability Report Automation Save S C O R E C A R D
3. Process Overview Review Data Points with teams*. If Data are not captured, process is defined. Data is saved to a shared space On QC Data identity is defined (QC Project, Project ID, Date stamp, etc.) Teams or process create sheets. QM collects all reports, consolidates, reviews with teams and sends result to PGM. Automated Process (QTP, Excel Marco, etc.) to upload sheets into Master sheet. Macro/QM consolidates information Naming Standards - Sheet Name (Team-Date-Test Metrics- Project) Shared Space Location Name and Access Initial Not On QC How Manual
4. Summary Detail Project View Execution/Defect Overview Report Initial Test Execution AE/PE < 10% AE/PE < 20% AE/PE > 30% ▲ ▬▬ ▼ Trend Defects defect/test executed < 10% defect/test executed < 20% defect/test executed > 30% Not In QC Overall Progress DF % + TEP % + Risk < 10% DF % + TEP % + Risk < 20% DF % + TEP % + Risk > 30% In QC Sheet Key FID Project View 4/1/2010 Total Tests Planned To Date Execution Actual To Date Execution Defects Test Execution Progress Defects Overall Test Progress # of test cases % of Total % of Total % of Total Commodities 55 24% 36% 15% ▲ ▲ ▲ Credit 70 43% 36% 4% ▬▬ ▬▬ ▬▬ GFX 120 64% 37% 57% ▼ ▬▬ ▲ Project ABC 245 49% 36% 33% ▼ ▬▬ ▲ Commodities 100 60% 54% 9% ▲ ▲ ▲ Credit 83 53% 46% 18% ▬▬ ▬▬ ▬▬ Rates 120 64% 45% 7% ▼ ▲ ▬▬ Project XYZ 303 60% 48% 11% ▼ ▲ ▬▬ FID Project View 4/1/2010 Total Tests Planned To Date Execution Actual To Date Execution Defects Test Execution Progress Defects Overall Test Progress # of test cases % of Total % of Total % of Total Project ABC 245 49% 36% 33% ▼ ▬▬ ▲ Project XYZ 303 60% 48% 11% ▼ ▲ ▬▬
5. Summary Detail Product View Execution/Defect Overview Report FID Product View 4/1/2010 Total Tests Planned Execution Actual Execution Defects Test Execution Progress Defects Open Defects Overall Test Progress # of test cases % of Total % of Total % of Total ABC 55 24% 36% 10% ▲ ▲ ▬▬ ▲ XYZ 100 65% 52% 13% ▲ ▲ ▼ ▼ Commodities 155 50% 46% 13% ▼ ▬▬ ▬▬ ▲ ABC 70 23% 36% 1% ▲ ▲ ▬▬ ▬▬ XYZ 200 48% 26% 18% ▲ ▲ ▬▬ ▬▬ Credit 27000% 41% 29% 13% ▲ ▲ ▬▬ ▬▬ FID Product View 4/1/2010 Total Tests Planned Execution Actual Execution Defects Test Execution Progress Defects Open Defects Overall Test Progress # of test cases % of Total % of Total % of Total Commodities 155 50% 46% 13% ▼ ▬▬ ▬▬ ▲ Credit 270 41% 29% 13% ▲ ▲ ▬▬ ▬▬
6. Process Overview All test teams on QC or compliant tool on an open architecture. New metrics defined and prioritized. QM teams generates report, verifies with team and sends to PGM. How Used Defined Fields added and populated, workflows created, folder structures and added to QC project template. New metrics may require a data pass or bridge to other tools. (Configuration Management, Project Management, Remedy, etc,) Teams Trained* Dry Run* Interim Interim
9. Interim April Release - ETL Total Cases % Actual Executed % Plan Executed # Passed % Passed (not including Blocked and No Run) # Failed # No Run Not Executed # Blocked Active DRS Total DRs CIT 12/12 - 1/30 341 71% 100% 225 93% 18 73 25 0 29 82 SIT 2/2 - 2/27 385 6% 100% 18 75% 6 351 10 0 0 2 Show Stopper High Medium Low Total DR Status Total 58 22 82 Active New 2 2 Ready For Review 2 3 4 9 Ready For Development 1 3 4 In QA 8 5 13 Re-Open 1 1 Total 12 15 29 Closed Withdrawn 10 4 14 Completed 36 3 39 Total 46 7 53
10. Traceability By Requirements Interim PCR-PCR Title Total Cases Not Completed Passed Failed N/A No Run Test Blocked Active Dr's % Completed % In Progress Comments PCR88890 - Stage to OLTP ETL Interface 67 0.00% 0.00% PCR 88891 - Type Table Manager ETL Interface 1 0.00% 0.00% PCR 88892 - ADIM to OrgMart DDL & DML scripts 1 0.00% 0.00% PCR88893 - OrgMart to BPS ETL Interface 12 0.00% 0.00% PCR88894 - OrgMart to Entitlements ETL Interface 13 0.00% 0.00% PCR88993 - ADIM to OrgMart Stage ETL Interface 140 0.00% 0.00% PCR89092 - Oracle Views for OMDS 29 0.00% 0.00% PCR89093 - Data Patches Phase 1 1 0.00% 0.00% PCR89094 - Data Patches Final non-HR Update 4 0.00% 0.00% PCR89095 - Export to ADIM ETL interface 100 0.00% 0.00% PCR89096 - CMD, FSL, B2C Exports ETL/Views impact 1 0.00% 0.00% PCR89629 - SSRS Reports impact 16 0.00% 0.00% Total 385 0 0 0 0 0 0 0 0.00% 0.00%
11. Process Overview All test teams on QC or compliant tool on an open architecture. New metrics defined and prioritized span Test Management, time utilization of BA/Dev/QA and cost analysis. How Data is identified, workflows created to capture and maintain the data. Applications may need to be modified or acquired to support metric. Teams Trained* Dry Run* Comprehensive
12. Comprehensive Mar-10 Feb-10 Jan-10 Dec-10 BRDs/PCRs 39 28 23 43 Number of Defects in QA 118 76 30 54 Number of Defects in UA 9 2 0 7 Total Prod Fix BRDs in the Post Release 57 2 7 Total 135 32 68 Defect Efficiency (DR Hours/PCR Hours) 42% 25% 30% 21% Total Number of Builds In QA 3 5 4 10 Total Number of Hot Fixes - Production 1 0 0 Severity Mar Feb Jan Dec ShowStopper 2 3 1 6 High 73 52 19 33 Medium 33 15 7 13 Low 10 6 3 2 Total 118 76 30 54
13. Comprehensive Release date Total Hours (PCR + DRs) 12127 5107 5644 5349 12603 Total Effort SA (Hrs) 886 156 321 474 894 Total Effort Dev (Hrs) 3172 630 1578 481 1098 Total Effort QA (Hrs) 6217 2820 2355 3155 8446 Total PCR Effort (Hrs) 10275 3606 4254 4110 10438 DRs Total Effort SA (Hrs) 135 267 321 244 200 Total Effort Dev (Hrs) 1353 893 771 703 1470 Total Effort QA (Hrs) 364 341 298 292 495 Total DR Effort (Hrs) 1852 1501 1390 1239 2165 Defect Effort (Hrs) 42% 25% 30% 21% Total Showstopper Defects 11 2 3 1 6 Total High Defects 112 54 52 19 33 Total Medium Defects 59 10 15 7 13 Total Low Defects 16 7 6 3 2 Deferred DRs 20 45 0 88 Total Overall Defects 218 118 76 118 54 Total Number of Prod Fix BRDs discovered during QA Cycle 31 35 4 17 Number of Defects Withdrawn 39 26 18 2 8 Number of Actual Defects Resolved in Pre Release 24 45 116 46 QA Defect Detection Duration (Hours per Defect) 32.72 34.39 37.98 70.11 183.61 Environment Issues (QA) 7 6 5 6 11 Environment Issues (IT) 2 4 4 5 5 Total Environment Issues 9 10 9 11 16 Differed DRs converted in Prod Fix BRDs 19 6 10 1 Pre Release Metrics Mar Feb Jan Dec Nov Release date
14. Comprehensive Defect Type 3rd party 1 3 2 2 3 Architectural Design/Technical specs 3 0 2 Business Requirement/Project scope 2 Coding 19 7 7 5 14 db Patch issue/Incorrect Push 0 Existing production issue 0 Functional Specification 15 7 6 5 8 Technical Specification 7 5 2 Other 136 74 98 44 19 Build Process 0 Training 0 Configuration 2 1 1 Environment issue 0 BLANK 35 19 TOTAL 218 118 116 56 46 UAT DRs Opened in Viper 0 0 2 0 7 Test Cases Executed 20 13 19 Passed 13 9 8 Failed 7 4 2 No Run 0 0 9 Functional Test Cases Reviewed 339 505 236 159 121 Accepted 336 478 229 129 72 Rejected 3 27 7 30 46 Functional Test Results Reviewed 148 90 178 112 41 Accepted 128 90 169 98 30 Rejected 20 0 9 14 11 Integration Test Results Reviewed 35 51 178 112 41 Accepted 19 35 169 98 30 Rejected 16 16 9 14 11 Pre Release Metrics Mar Feb Jan Dec Nov Release date
15. A version collection tool is used to track all web service and UI versions in the QA, IT, and ST environments on a daily basis. Comprehensive
18. Requirements Tracking Comprehensive PCR # PCR Status PCR Title 89833 New NexOS - OrgMart 2.0 - Web Services Development 94123 New NexOS DB - OrgMart 2.0 - UI Oracle API Phase 2 91076 In Analysis NexOS DB OrgMart 2.0 Config Data cleanup and Oracle API Changes 94216 In Analysis NexOS Tools & Admin - OrgMart 2.0 Data Transformation and Services 89094 Pending Estimation NexOS DB - OrgMart 2.0 - P - Data Patches Final non-HR Update 89282 In Development NexOS DB - OrgMart 2.0 - P - Oracle Merge scripts to sync ORGMART_PROD to ORGMART (lower environments only) 90222 In Development NexOS DB - OrgMart 2.0 - Oracle PL/SQL API Packages referencing HR schema impact 91080 In Development NexOS DB OrgMart 2.0 Config to OrgMart Oracle API Changes 88890 In QA NexOS DB - OrgMart 2.0 - P - Stage to OLTP ETL Interface 88891 In QA NexOS DB - OrgMart 2.0 - P - Type Table Manager ETL Interface 88892 In QA NexOS DB - OrgMart 2.0 - P - ADIM to OrgMart DDL & DML scripts 88893 In QA NexOS DB - OrgMart 2.0 - P - OrgMart to BPS ETL Interface 88894 In QA NexOS DB - OrgMart 2.0 - P - OrgMart to Entitlements ETL Interface 88993 In QA NexOS DB - OrgMart 2.0 - P - ADIM to OrgMart Stage ETL Interface 89092 In QA NexOS DB - OrgMart 2.0 - P - Oracle Views for OMDS 89093 In QA NexOS DB - OrgMart 2.0 - P - Data Patches Phase 1 89095 In QA NexOS DB - OrgMart 2.0 - P - Export to ADIM ETL interface 89096 In QA NexOS DB - OrgMart 2.0 - P - CMD, FSL, B2C Exports ETL/Views impact 89097 In QA NexOS DB - OrgMart 2.0 - P - Marketing ETL Export interface impact 89247 In QA NexOS DB - OrgMart 2.0 - P - UI Oracle API 89248 In QA NexOS DB - OrgMart 2.0 - P - SuperMarvin Oracle API 89396 In QA NexOS DB - OrgMart 2.0 - P - Oracle API for LandSafe 89629 In QA NexOS DB - OrgMart 2.0 - P - SSRS Reports impact 91069 In QA NexOS DB OrgMart 2.0 Config Pipeline Delegation functionality XFER to iPipe
19.
Editor's Notes
Metrics are defined as “standards of measurement” and are a method of gauging the effectiveness and efficiency of a particular activity within a project. Test metrics exist in a variety of forms. The question is not whether we should use them, but rather, which ones should we use. Simpler is almost always better. For example, it may be interesting to derive the Binomial Probability Mass Function for a particular project, although it may not be practical in terms of the resources and time required to capture and analyze the data. Furthermore, the resulting information may not be meaningful or useful to the current effort of process improvement.
“ You cannot improve what you cannot measure.” When used properly, test metrics assist in the improvement of the software development process by providing pragmatic, objective evidence of process change initiatives.
Manual – intrusive, error prone and may conflict with other priorities