Join John Sonmez of SimpleProgrammer, Joe Colantonio of TestTalks and Dave Haeffner as they discuss how to get test automation right in your organization! https://www.youtube.com/watch?v=QWpm4py985o&feature=youtu.be
resume graham (2006) book FUNDAMENTALS OF TESTING
resume of Graham et al Foundationf of Software Testing (2006)
created by Fadhilla Elita information system class
This document discusses how machine learning can be applied to various activities in software testing. It describes how machine learning works using training and test data to make predictions. Supervised and unsupervised learning techniques are discussed. Specific applications mentioned include software defect prediction, test planning, test case management, debugging, and refining blackbox test specifications. Challenges include availability of past data and finding predictable patterns, while potential steps forward include expanding machine learning to more blackbox techniques, identifying the right patterns for different test activities, algorithm analysis, and crowdsourcing.
This document discusses using parallel_calabash to run automated tests in parallel to speed up test execution time. It describes how parallel_calabash works by grouping test features, spawning multiple processes across devices, and summarizing results. Running tests in parallel utilizes multiple CPU cores and significantly reduces test feedback time from over an hour to under 15 minutes, allowing for faster development cycles.
Agile software development is probably the most common methodology used by organizations today, as such; many people have started to ask more and more questions about this methodology that sometimes based on wrong assumptions.
In this presentation, I will review the most common Myths and Misconceptions that I encounter during agile training courses, hopefully, to help people to divide the truth from the assumptions.
Automation in the Bug Flow - Machine Learning for Triaging and TracingMarkus Borg
Issue management is a costly part of software development. In large projects, the continuous inflow of issue reports contributes to the information overload in a project, i.e., "a state where individuals do not have time or capacity to process all available information". In issue triaging, an initial step in issue management, a developer must be able to overview existing issue reports and easily navigate the software engineering project landscape. In this presentation, we present support for two work tasks involved in issue management: 1) issue assignment and 2) change impact analysis. We use machine learning to harness the ever-growing number of issue reports, by training recommendation systems on previous issues. Our industrial evaluations on 50,000+ issue reports in two large software development organizations indicate that automated issue assignment performs in line with current manual work. Moreover, we present how traceability from already resolved issue reports to various artifacts can be reused to jump start change impact analyses for newly submitted issues. Finally, we speculate on future ways to tame information overload into helpful software engineering recommendations.
This document discusses using a genetic algorithm to develop a machine learning model for predicting fault-prone software classes. It begins by introducing software reliability and fault prediction. It then explains that a genetic algorithm is a search technique that evaluates potential solutions, keeps the best ones, and generates new solutions iteratively. The algorithm uses software metrics like coupling, cohesion, inheritance, and size as inputs to classify classes as faulty or fault-free with 80.14% accuracy, helping to identify areas for improvement.
There is no doubt about the importance of automated frameworks in the Agile environment and as part of the day-to-day testing process. These are some insights to guide any automation project.
The document outlines 7 testing principles: 1) Testing finds defects but finding none does not mean none exist, 2) Exhaustive testing is impossible so smarter testing is needed, 3) Early testing saves time and money and makes customers happy, 4) Defects tend to cluster together, 5) Test cases must be updated periodically to avoid outdated "pesticide" tests, 6) Testing methods vary depending on the software context, and 7) Software should be stable before testing to avoid false negatives from instability.
resume graham (2006) book FUNDAMENTALS OF TESTING
resume of Graham et al Foundationf of Software Testing (2006)
created by Fadhilla Elita information system class
This document discusses how machine learning can be applied to various activities in software testing. It describes how machine learning works using training and test data to make predictions. Supervised and unsupervised learning techniques are discussed. Specific applications mentioned include software defect prediction, test planning, test case management, debugging, and refining blackbox test specifications. Challenges include availability of past data and finding predictable patterns, while potential steps forward include expanding machine learning to more blackbox techniques, identifying the right patterns for different test activities, algorithm analysis, and crowdsourcing.
This document discusses using parallel_calabash to run automated tests in parallel to speed up test execution time. It describes how parallel_calabash works by grouping test features, spawning multiple processes across devices, and summarizing results. Running tests in parallel utilizes multiple CPU cores and significantly reduces test feedback time from over an hour to under 15 minutes, allowing for faster development cycles.
Agile software development is probably the most common methodology used by organizations today, as such; many people have started to ask more and more questions about this methodology that sometimes based on wrong assumptions.
In this presentation, I will review the most common Myths and Misconceptions that I encounter during agile training courses, hopefully, to help people to divide the truth from the assumptions.
Automation in the Bug Flow - Machine Learning for Triaging and TracingMarkus Borg
Issue management is a costly part of software development. In large projects, the continuous inflow of issue reports contributes to the information overload in a project, i.e., "a state where individuals do not have time or capacity to process all available information". In issue triaging, an initial step in issue management, a developer must be able to overview existing issue reports and easily navigate the software engineering project landscape. In this presentation, we present support for two work tasks involved in issue management: 1) issue assignment and 2) change impact analysis. We use machine learning to harness the ever-growing number of issue reports, by training recommendation systems on previous issues. Our industrial evaluations on 50,000+ issue reports in two large software development organizations indicate that automated issue assignment performs in line with current manual work. Moreover, we present how traceability from already resolved issue reports to various artifacts can be reused to jump start change impact analyses for newly submitted issues. Finally, we speculate on future ways to tame information overload into helpful software engineering recommendations.
This document discusses using a genetic algorithm to develop a machine learning model for predicting fault-prone software classes. It begins by introducing software reliability and fault prediction. It then explains that a genetic algorithm is a search technique that evaluates potential solutions, keeps the best ones, and generates new solutions iteratively. The algorithm uses software metrics like coupling, cohesion, inheritance, and size as inputs to classify classes as faulty or fault-free with 80.14% accuracy, helping to identify areas for improvement.
There is no doubt about the importance of automated frameworks in the Agile environment and as part of the day-to-day testing process. These are some insights to guide any automation project.
The document outlines 7 testing principles: 1) Testing finds defects but finding none does not mean none exist, 2) Exhaustive testing is impossible so smarter testing is needed, 3) Early testing saves time and money and makes customers happy, 4) Defects tend to cluster together, 5) Test cases must be updated periodically to avoid outdated "pesticide" tests, 6) Testing methods vary depending on the software context, and 7) Software should be stable before testing to avoid false negatives from instability.
Slides used in Agile Testing Conference hosted by KnowledgeHut in Pune, India in March 2017.
The slides talk about the Testing Challenge posed by Machine Learning applications and some suggested approaches to point us in the right direction
Influence of emphasized automation in ciBugRaptors
To choose testing during software development, Bugraptors always uses the Continuous Integration and continuous deployment to decide the way of testing i.e: Automation or Manual. It is very important to decide the testing during software development to ensure quality meeting project constraints.
The document discusses common myths about test automation including:
1) That automation testing is better than manual testing, whereas they serve different purposes with automation for checking facts and manual for exploration.
2) Achieving 100% test coverage is not practical with either approach, and focus should be on important functionality rather than full coverage.
3) Automation does not necessarily find more defects than manual testing, as scripts only check what is programmed and major flaws can be missed. Automation is better for regression while manual is better for new functionality.
Testing may show the defects are present, but cannot prove that there are no defects. After testing the system or product thoroughly we cannot say that the product is complete defect free. Testing always reduces the no of undiscovered defects remaining in the software.
The document discusses defect management, defining defects, errors, faults and failures. It describes the defect life cycle from a tester finding an issue and reporting it, to a developer fixing it, and the tester retesting and closing the defect. It also covers determining defect severity and priority, attributes to capture in a defect report, advantages of defect reporting, and tools that can be used for defect tracking.
The document contains a session plan for a software testing principles and techniques course. The session objectives are to define various software testing terms and concepts, differentiate between different types of testing, and learn about the testing process. The session would include slides, demonstrations of testing software, and discussions. Test cases for an example ATM system are also provided to demonstrate initial functional testing.
Top 5 pitfalls of software test automatiionekatechserv
Automating tests is important to detect and fix defects early in the development cycle, which can be 100 times cheaper than fixing bugs after release. Automated tests allow bugs to be spotted and fixed early. While automation provides benefits like reduced costs, there are pitfalls to avoid like relying solely on automation for all testing needs, requiring extensive coding, producing false positives, and attempting to replace human testers. Key is using automation to aid, not replace, testers in executing tests efficiently.
It Seemed a Good Idea at the Time: Intelligent Mistakes in Test AutomationTechWell
Some test automation ideas seem very sensible at first glance but contain pitfalls and problems that can and should be avoided. Dot Graham describes five of these “intelligent mistakes”—1. Automated tests will find more bugs quicker. (Automation doesn’t find bugs, tests do.) 2. Spending a lot on a tool must guarantee great benefits. (Good automation does not come “out of the box” and is not automatic.) 3. Let’s automate all of our manual tests. (This may not give you better or faster testing, and you will miss out on some benefits.) 4. Tools are expensive so we have to show a return on investment. (This is not only surprisingly difficult but may actually be harmful.) 5. Because they are called “testing tools,” they must be tools for testers to use. (Making testers become test automators may be damaging to both testing and automation.) Join Dot for a rousing discussion of “intelligent mistakes”—so you can be smart enough to avoid them.
The 7 software testing principles briefly explained. Everyone who works in software development company should know these principles.
It happens frequently that testers or qa people are not taken into account as part of the process in the software development lifecycle and this happens expecially when the principles are not known.
Many organizations never achieve the significant benefits that are promised from automated test execution. Surprisingly often, this is due not to technical factors but to management issues, especially at system testing level. Surprisingly often, this is due not to technical factors but to management issues. Dot Graham describes the most important management concerns the test manager must address for test automation success, and helps you understand and choose the best approaches for your organization—no matter which automation tools you use or your current state of automation. Dot explains how automation affects staffing, who should be responsible for which automation tasks, how managers can best support automation efforts leading to success, and why return on investment can be dangerous and what you can realistically expect. Dot also reviews a few key technical issues that can make or break the automation effort. Come away with an example set of automation objectives and measures, and a draft test automation strategy that you can use to plan or improve your own automation.
The document discusses defect management processes. It defines defects, describes different types of defects and their severity. It outlines the key steps in a defect management process: testing for defects, logging defects found, investigating defects, prioritizing defect resolutions, correcting defects, and reporting resolved defects. Traceability from requirements to testing is important. Defect metrics can help improve processes by identifying where and how defects are introduced and resolved. Collaboration between developers, testers, and other roles is essential for effective defect management.
Death of Inspection : Reincarnation of Testing Community Sachin Natu
The document discusses the transition from an inspection testing model to a more collaborative testing model between testers and developers.
Some key challenges with the inspection model included long regression cycles due to late defect detection, high manual testing workloads, and testers focusing only on defect detection. The new model emphasized automating checks, placing tests closer to their origin like unit tests, and collaborating between testers and developers from the start.
This led to improvements like reduced manual testing workloads, defects found earlier, tests maintained with code changes, and shorter regression periods. It created a more efficient testing process and quality approach owned by the entire team rather than just testers.
1) The document discusses common pitfalls of test automation and provides recommendations to avoid them. It identifies pitfalls such as automating everything without prioritization, viewing automation as solely the tester's responsibility, being overly reliant on automation tools, treating test code as less important, and having unrealistic expectations of return on investment.
2) The recommendations are to prioritize what to automate based on risk and value, involve the whole team in automation, select the right tools for the job with critical thinking, follow good coding practices for test code, and take a long term and realistic view of the costs and benefits of automation.
3) The key takeaway is that automation requires investment of time and resources, but can
This document provides an introduction to software testing for startups. It discusses that testing early in the development cycle results in faster development, better software, and enhanced investment appeal. It recommends creating test cases based on functional specifications and menus. The document outlines six principles of testing, including that you cannot test every scenario and defects congregate in particular areas. It recommends testing frequently with both developers and testers working closely together.
The document outlines seven principles of software testing: 1) Testing shows the presence of errors, not their absence; 2) Exhaustive testing of all possible test cases is impossible; 3) Testing early in the development cycle is important to more easily fix defects; 4) Defects tend to cluster together, following an 80-20 distribution; 5) Test effectiveness fades over time as software changes; 6) Testing methods depend on the type of application; 7) Finding no errors does not mean the system is usable - user requirements must still be met.
What will testing look like in year 2020BugRaptors
One thing which we were observing since the year 2001 was how testing activities integrate with SDLC in early stages by using methodologies such as Agile. Agile was used by many organizations for shortening their development time. Also use of virtualization, cloud computing, and service-oriented architecture also become famous.
Fundamentals of testing what is testing (reference graham et.al (2006))Alfarizi ,S.Kom
The document discusses software testing, its objectives, and its importance. It uses an analogy to a driving test to explain software testing. Some key points made:
1) Testing helps find defects, provide confidence in quality, and prevent defects, similar to how a driving test evaluates a driver's skills.
2) Both static and dynamic testing provide information to improve the system and development/testing processes.
3) Over time, as processes improve, dynamic testing finds fewer defects while static testing finds more early on.
The document outlines seven fundamental principles of software testing:
1. Exhaustive testing is unrealistic and risk-based testing is required.
2. Defects tend to cluster in specific high-risk modules.
3. Repeated identical tests become less effective at finding bugs over time ("pesticide paradox").
4. Early testing in development cycles better identifies requirements and design issues.
5. The absence of errors in testing does not prove correctness or ensure the software meets user needs.
6. Testing approaches depend on the context and type of software.
7. These principles form the foundation of effective software testing.
Top 5 Pitfalls of Test Automation and How To Avoid ThemSundar Sritharan
The document discusses top pitfalls of test automation and how to avoid them. It identifies the top 5 pitfalls as: 1) diving into open source tools without preparation, 2) developing test scripts without standardization, 3) automating all test cases without prioritization, 4) choosing in-house testing over cloud options, and 5) assuming automation testing is not the tester's job. It provides guidance on how to effectively implement test automation by choosing the right tools, standardizing test development, prioritizing test cases, leveraging cloud options, and defining tester responsibilities.
How to Measure Success in Continuous Testing by Fernando Vidal and Amir Rozen...Sauce Labs
In this SauceCon 2019 talk, Fernando and Amir use observations from running 2 billion tests for thousands of Sauce Labs customers to outline the the key drivers of success in continuous testing and an provide an actionable way to see how organizations are doing relative to other successful testers. They discuss a new index which can be used by any organization to evaluate their progression towards continuous testing, and cover the components of the new continuous testing index, what makes these components such strong maturity indicators, how organizations can improve on each of the metrics, and what happens organizationally when those metrics are improved
Slides used in Agile Testing Conference hosted by KnowledgeHut in Pune, India in March 2017.
The slides talk about the Testing Challenge posed by Machine Learning applications and some suggested approaches to point us in the right direction
Influence of emphasized automation in ciBugRaptors
To choose testing during software development, Bugraptors always uses the Continuous Integration and continuous deployment to decide the way of testing i.e: Automation or Manual. It is very important to decide the testing during software development to ensure quality meeting project constraints.
The document discusses common myths about test automation including:
1) That automation testing is better than manual testing, whereas they serve different purposes with automation for checking facts and manual for exploration.
2) Achieving 100% test coverage is not practical with either approach, and focus should be on important functionality rather than full coverage.
3) Automation does not necessarily find more defects than manual testing, as scripts only check what is programmed and major flaws can be missed. Automation is better for regression while manual is better for new functionality.
Testing may show the defects are present, but cannot prove that there are no defects. After testing the system or product thoroughly we cannot say that the product is complete defect free. Testing always reduces the no of undiscovered defects remaining in the software.
The document discusses defect management, defining defects, errors, faults and failures. It describes the defect life cycle from a tester finding an issue and reporting it, to a developer fixing it, and the tester retesting and closing the defect. It also covers determining defect severity and priority, attributes to capture in a defect report, advantages of defect reporting, and tools that can be used for defect tracking.
The document contains a session plan for a software testing principles and techniques course. The session objectives are to define various software testing terms and concepts, differentiate between different types of testing, and learn about the testing process. The session would include slides, demonstrations of testing software, and discussions. Test cases for an example ATM system are also provided to demonstrate initial functional testing.
Top 5 pitfalls of software test automatiionekatechserv
Automating tests is important to detect and fix defects early in the development cycle, which can be 100 times cheaper than fixing bugs after release. Automated tests allow bugs to be spotted and fixed early. While automation provides benefits like reduced costs, there are pitfalls to avoid like relying solely on automation for all testing needs, requiring extensive coding, producing false positives, and attempting to replace human testers. Key is using automation to aid, not replace, testers in executing tests efficiently.
It Seemed a Good Idea at the Time: Intelligent Mistakes in Test AutomationTechWell
Some test automation ideas seem very sensible at first glance but contain pitfalls and problems that can and should be avoided. Dot Graham describes five of these “intelligent mistakes”—1. Automated tests will find more bugs quicker. (Automation doesn’t find bugs, tests do.) 2. Spending a lot on a tool must guarantee great benefits. (Good automation does not come “out of the box” and is not automatic.) 3. Let’s automate all of our manual tests. (This may not give you better or faster testing, and you will miss out on some benefits.) 4. Tools are expensive so we have to show a return on investment. (This is not only surprisingly difficult but may actually be harmful.) 5. Because they are called “testing tools,” they must be tools for testers to use. (Making testers become test automators may be damaging to both testing and automation.) Join Dot for a rousing discussion of “intelligent mistakes”—so you can be smart enough to avoid them.
The 7 software testing principles briefly explained. Everyone who works in software development company should know these principles.
It happens frequently that testers or qa people are not taken into account as part of the process in the software development lifecycle and this happens expecially when the principles are not known.
Many organizations never achieve the significant benefits that are promised from automated test execution. Surprisingly often, this is due not to technical factors but to management issues, especially at system testing level. Surprisingly often, this is due not to technical factors but to management issues. Dot Graham describes the most important management concerns the test manager must address for test automation success, and helps you understand and choose the best approaches for your organization—no matter which automation tools you use or your current state of automation. Dot explains how automation affects staffing, who should be responsible for which automation tasks, how managers can best support automation efforts leading to success, and why return on investment can be dangerous and what you can realistically expect. Dot also reviews a few key technical issues that can make or break the automation effort. Come away with an example set of automation objectives and measures, and a draft test automation strategy that you can use to plan or improve your own automation.
The document discusses defect management processes. It defines defects, describes different types of defects and their severity. It outlines the key steps in a defect management process: testing for defects, logging defects found, investigating defects, prioritizing defect resolutions, correcting defects, and reporting resolved defects. Traceability from requirements to testing is important. Defect metrics can help improve processes by identifying where and how defects are introduced and resolved. Collaboration between developers, testers, and other roles is essential for effective defect management.
Death of Inspection : Reincarnation of Testing Community Sachin Natu
The document discusses the transition from an inspection testing model to a more collaborative testing model between testers and developers.
Some key challenges with the inspection model included long regression cycles due to late defect detection, high manual testing workloads, and testers focusing only on defect detection. The new model emphasized automating checks, placing tests closer to their origin like unit tests, and collaborating between testers and developers from the start.
This led to improvements like reduced manual testing workloads, defects found earlier, tests maintained with code changes, and shorter regression periods. It created a more efficient testing process and quality approach owned by the entire team rather than just testers.
1) The document discusses common pitfalls of test automation and provides recommendations to avoid them. It identifies pitfalls such as automating everything without prioritization, viewing automation as solely the tester's responsibility, being overly reliant on automation tools, treating test code as less important, and having unrealistic expectations of return on investment.
2) The recommendations are to prioritize what to automate based on risk and value, involve the whole team in automation, select the right tools for the job with critical thinking, follow good coding practices for test code, and take a long term and realistic view of the costs and benefits of automation.
3) The key takeaway is that automation requires investment of time and resources, but can
This document provides an introduction to software testing for startups. It discusses that testing early in the development cycle results in faster development, better software, and enhanced investment appeal. It recommends creating test cases based on functional specifications and menus. The document outlines six principles of testing, including that you cannot test every scenario and defects congregate in particular areas. It recommends testing frequently with both developers and testers working closely together.
The document outlines seven principles of software testing: 1) Testing shows the presence of errors, not their absence; 2) Exhaustive testing of all possible test cases is impossible; 3) Testing early in the development cycle is important to more easily fix defects; 4) Defects tend to cluster together, following an 80-20 distribution; 5) Test effectiveness fades over time as software changes; 6) Testing methods depend on the type of application; 7) Finding no errors does not mean the system is usable - user requirements must still be met.
What will testing look like in year 2020BugRaptors
One thing which we were observing since the year 2001 was how testing activities integrate with SDLC in early stages by using methodologies such as Agile. Agile was used by many organizations for shortening their development time. Also use of virtualization, cloud computing, and service-oriented architecture also become famous.
Fundamentals of testing what is testing (reference graham et.al (2006))Alfarizi ,S.Kom
The document discusses software testing, its objectives, and its importance. It uses an analogy to a driving test to explain software testing. Some key points made:
1) Testing helps find defects, provide confidence in quality, and prevent defects, similar to how a driving test evaluates a driver's skills.
2) Both static and dynamic testing provide information to improve the system and development/testing processes.
3) Over time, as processes improve, dynamic testing finds fewer defects while static testing finds more early on.
The document outlines seven fundamental principles of software testing:
1. Exhaustive testing is unrealistic and risk-based testing is required.
2. Defects tend to cluster in specific high-risk modules.
3. Repeated identical tests become less effective at finding bugs over time ("pesticide paradox").
4. Early testing in development cycles better identifies requirements and design issues.
5. The absence of errors in testing does not prove correctness or ensure the software meets user needs.
6. Testing approaches depend on the context and type of software.
7. These principles form the foundation of effective software testing.
Top 5 Pitfalls of Test Automation and How To Avoid ThemSundar Sritharan
The document discusses top pitfalls of test automation and how to avoid them. It identifies the top 5 pitfalls as: 1) diving into open source tools without preparation, 2) developing test scripts without standardization, 3) automating all test cases without prioritization, 4) choosing in-house testing over cloud options, and 5) assuming automation testing is not the tester's job. It provides guidance on how to effectively implement test automation by choosing the right tools, standardizing test development, prioritizing test cases, leveraging cloud options, and defining tester responsibilities.
How to Measure Success in Continuous Testing by Fernando Vidal and Amir Rozen...Sauce Labs
In this SauceCon 2019 talk, Fernando and Amir use observations from running 2 billion tests for thousands of Sauce Labs customers to outline the the key drivers of success in continuous testing and an provide an actionable way to see how organizations are doing relative to other successful testers. They discuss a new index which can be used by any organization to evaluate their progression towards continuous testing, and cover the components of the new continuous testing index, what makes these components such strong maturity indicators, how organizations can improve on each of the metrics, and what happens organizationally when those metrics are improved
Why Automation Fails—in Theory and PracticeTechWell
Testers face common challenges in automation. Unfortunately, these challenges often lead to subsequent failures. Jim Trentadue explains a variety of automation perceptions and myths―the perception that a significant increase in time and people is needed to implement automation; the myth that, once automation is achieved, testers will not be needed; the myth that scripted automation will serve all the testing needs for an application; the perception that developers and testers can add automation to a project without additional time, resources, or training; the belief that anyone can implement automation. The testing organization must ramp up quickly on the test automation process and the prep-work analysis that needs to be done including when to start, how to structure the tests, and what system to start with. Learn how to respond to these common challenges by developing a solid business case for increased automation adoption by engaging manual testers in the testing organization, being technology agnostic, and stabilizing test scripts regardless of applications changes.
Test Automation - Everything You Need To KnowBugRaptors
Businesses face difficulty due to rapidly changing dynamic applications, and here test automation service provider can solves all your woes. Accelerate the release of your product with test automation solutions. Check out the PPT to know more or visit Bugraptors portfolio at www.bugraptors.com
The document discusses introducing automated testing to software projects using the Automated Testing Lifecycle Methodology (ATLM). The ATLM provides a structured six-phase approach to deciding on, acquiring, introducing, planning, executing, and reviewing automated testing. It addresses common misconceptions around test automation and outlines the methodology's phases and processes to help organizations implement automated testing successfully.
Automation testing is an essential practice for any software development project. It involves using software tools to automate the testing process, thereby increasing efficiency and accuracy. With the increasing complexity of software systems, automation testing has become a crucial part of the development process. https://bit.ly/3AgDryv
ReportPortal.io - Open Source experience. Showcase, benefits COMAQA.BY
The document discusses Report Portal, an open source test automation monitoring tool. It is presented by Dzmitry Humianiuk, Project Manager at EPAM Systems, who has been with the company for 10 years. Report Portal provides live visibility into test automation, reduces time spent analyzing results, and supports popular automation tools out of the box with nice graphical reporting. It has been successfully implemented on over 110 projects and in 8 external customer installations.
The document discusses various topics related to software testing including types of testing (manual vs automation), testing methods (black box, white box, grey box), testing levels (unit, integration, system), and common myths around testing. It provides definitions and examples of different testing techniques and clarifies misunderstandings around responsibilities and goals of testing. Videos are embedded to further explain key testing concepts like unit testing, integration testing, and differences between testing approaches.
Quality Assurance - The Other Side of the FenceTom Oketch
This document discusses different types of quality assurance testing for software, including unit testing, integration testing, and acceptance testing. It describes test-driven development (TDD) and behavior-driven development (BDD) approaches. TDD involves writing tests before code in small iterative cycles, while BDD is a collaborative way to define requirements to facilitate automated testing. The document provides guidance on writing effective behavior scenarios and assessing them.
The document discusses test automation, including defining it as using special software to control test execution and compare results. It lists major objectives like time and cost savings, improved productivity, accuracy, and coverage. It recommends automating repetitive, tedious, time-consuming, or high-risk tests. A typical automation process includes planning, design, tool development, deployment, and review. Choosing an automation tool requires considering ease of use, supported test types, and maintenance. Automated testing reduces long-term costs while manual testing has shorter-term benefits like more bugs found through hands-on testing.
The document discusses fundamentals of software testing including definitions of testing, why testing is necessary, seven testing principles, and the test process. It describes the test process as consisting of test planning, monitoring and control, analysis, design, implementation, execution, and completion. It also outlines the typical work products created during each phase of the test process.
Engineering leaders from eBay and Walmart discuss how they tackle test automation, testing data, accessibility and other areas within their departments.
The document discusses various aspects of software testing including the need for testing, types of testing, testing tools, the testing life cycle, and determining when to stop testing. It notes that software testing is a planned process used to identify correctness, completeness, security and quality of software. The testing life cycle involves requirements analysis, test planning, writing and reviewing test cases, bug logging and tracking, and closing and reopening bugs.
This document provides an overview of software testing fundamentals. It defines testing as executing software to find bugs and discusses why testing is necessary to ensure quality. It also covers causes of defects, different levels of testing from unit to acceptance, testing principles, and sample entry and exit criteria for different test stages. The goal of testing is to validate software meets requirements and works as expected while improving quality through the identification and fixing of defects.
How To Implement Efficient Test Automation In The Agile World.pdfAnanthReddy38
In today’s fast-paced software development landscape, agility is key. Agile methodologies have revolutionized the way software is built and delivered, allowing teams to respond quickly to changing requirements and customer feedback. Test automation plays a pivotal role in enabling agility by ensuring rapid and reliable testing processes. However, implementing efficient test automation in the Agile world requires a thoughtful approach. In this article, we will explore the best practices and strategies for achieving this goal.
Understanding Agile and Test Automation
Before delving into the specifics of implementing test automation in Agile, it’s essential to understand the core principles of both Agile and test automation.
Agile Development is an iterative and incremental approach to software development that emphasizes flexibility, collaboration, and customer-centricity. It focuses on delivering small, functional increments of software, known as “user stories” or “features,” in short timeframes called “sprints.”
Test Automation involves using automated testing tools and scripts to perform repetitive and time-consuming test activities, such as regression testing, load testing, and functional testing. Test automation aims to increase testing efficiency, accuracy, and speed while reducing manual effort.
Challenges in Implementing Test Automation in Agile
Continuous Change: Agile projects are characterized by changing requirements and frequent updates. This dynamic environment can pose challenges for maintaining automated test scripts, as they may become obsolete quickly.
Time Constraints: Agile emphasizes short development cycles. This means that testing needs to keep up with the pace of development, requiring automation to be both efficient and effective.
Collaboration: Agile teams rely on close collaboration between developers, testers, and other stakeholders. Effective communication and collaboration are essential to ensure that automated tests align with the project’s goals.
Best Practices for Efficient Test Automation in Agile
1. Start Early
Begin test automation efforts as soon as possible in the project lifecycle. Test automation should be an integral part of the development process from the beginning. This ensures that automated tests can evolve alongside the application, adapting to changing requirements.
2. Select the Right Tools
Choose test automation tools that are well-suited for Agile development. Tools like Selenium, Appium, and JUnit are popular choices because of their flexibility and compatibility with Agile practices. Additionally, consider tools that offer collaboration features and integrate easily with your development environment.
3. Test Automation Pyramid
Adhere to the Test Automation Pyramid concept, which suggests a hierarchy of test automation levels: unit testing, integration testing, and UI testing.
Selenium - A Trending Automation Testing Toolijtsrd
Selenium is an important testing tool for software quality assurance. In recent days number of websites are increasing rapidly and it becomes essential to test the websites against various quality factors to make sure it meets the expected quality goals. Several companies are spending a lot of bucks for the testing tool while Selenium is available completely free for the performance test. The open source tool is well known for its unlimited capabilities and unlimited reach. Selenium stands out from the crowd in this aspect. Anyone could visit the Selenium website and download the latest version and use it. It is not only an open source but also highly modifiable. Testers could make changes based upon their needs and requirements. Manav Kundra "Selenium - A Trending Automation Testing Tool" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31202.pdf Paper Url :https://www.ijtsrd.com/engineering/software-engineering/31202/selenium-%E2%80%93-a-trending-automation-testing-tool/manav-kundra
Challenges with agile testing process and how to debug and troubleshoot these...Chandan Patary
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1. WebinarAug 31
All information contained are Proprietary and Confidential
www.getzephyr.com
GETTING TEST
AUTOMATION RIGHT
2. 2
Todays PANELISTS
John Sonmez
Automation Engineer
Simple Programmer
www.simpleprogrammer.com
@jsonmez
Panelist
www.getzephyr.com
@yourzephyr
Chris Wright
Marketing Manager
Zephyr
Moderator
Joe Colantonio
CEO
JoeColantonio
www.joecolantonio.com
@jcolantonio
Panelist
Dave Haeffner
Automation Expert
Selenium
www.elementalselenium.com
@TourDeDave
Panelist
3. 3
Key TAKEAWAYS
When should you start to
consider automation
What you should automate vs.
what you shouldn't automate
Risks associated with
automated testing
Creating a proper automation
framework
The importance of using
automation in a testing environment
Rateofadoption
2012 2013 2014 2015
% Growth between 2012-2015
4. 4
Why Automated Testing is
IMPORTANT
> Removing human error
> Increase testing speed
without sacrificing quality
> Free up your team’s time
> Quick turnaround
> Continuous delivery
> Agile = automation
- Hinders scalability
Manual Testing Automated Testing
Test Execution Efficiency
5. 5
Automated Testing
EXPECTATIONS
> Maintaining same amount of defects
> Reducing the total time to test
> Making quick adjustments
> Better total coverage
> Maintenance
- Cost of ownership (personal and tools)
6. 6
What You Should / Shouldn’t
AUTOMATE
Shouldn’t
Automate
Should
Automate
> One off tests
- Items you only run once
> Tests that require manual steps in
between
> Non-deterministic tests
> Easily verified tests
> Four questions you should ask
- How does your business make money?
- How do your users use your application?
- What browser are your users using?
- What’s previous broken in your app ?
> Critical / risky portions of your app
7. 7
Risks Associated with
AUTOMATED TESTING
> Reliable failures
> Fake sense of security
> Trying to do too much
- Maintenance nightmare
> Making sure development is involved
8. 8
Creating an Automation
FRAMEWORK
> What is an automation
framework?
- Why having one is important?
> Defining your framework
> What is your framework
targeted at?
> What problem is your
framework solving?