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{{About|software development|the statistical analysis process|Regression analysis|}}▼
{{Short description|Checking whether changes to software have broken functionality that used to work}}
▲{{About|software development|the statistical analysis process|Regression analysis|}}
{{Software development process}}
'''Regression testing''' (rarely, ''non-regression testing''<ref>{{cite book |last1=Pezzè |first1=Mauro |last2=Young |first2=Michal |title=Software testing and analysis: process, principles, and techniques |date=2008 |publisher=Wiley |url=https://www.google.com/search?q=Mauro+%22non-regression%22+%22regression+testing%22 |quote=Testing activities that focus on regression problems are called (non) regression testing. Usually "non" is omitted}}</ref>) is re-running [[functional testing|functional]] and [[non-functional testing|non-functional tests]] to ensure that previously developed and tested software still performs as expected after a change.<ref>{{cite book|last=Basu|first=Anirban| title=Software Quality Assurance, Testing and Metrics| year=2015| publisher=PHI Learning| isbn=978-81-203-5068-7| url=https://books.google.com/books?id=aNTiCQAAQBAJ&pg=PA150}}</ref> If not, that would be called a ''[[software regression|regression]]''.
Changes that may require regression testing include [[software bug|bug]] fixes, software enhancements, [[configuration file|configuration]] changes, and even substitution of [[electronic component]]s ([[Computer hardware|hardware]]).<ref>[[National Academies of Sciences, Engineering, and Medicine|National Research Council]] Committee on Aging Avionics in Military Aircraft: [https://www.nap.edu/catalog/10108/aging-avionics-in-military-aircraft ''Aging Avionics in Military Aircraft'']. The National Academies Press, 2001, page 2: ″Each technology-refresh cycle requires regression testing.″</ref> As regression [[test suite]]s tend to grow with each found defect, test automation is frequently involved. The evident exception is the [[Graphical user interface|GUIs]] regression testing, which normally must be executed manually. Sometimes a [[change impact analysis]] is performed to determine an appropriate subset of tests (''non-regression analysis''<ref>{{cite book |last1=Boulanger |first1=Jean-Louis |title=CENELEC 50128 and IEC 62279 Standards |date=2015 |publisher=Wiley |isbn=978-1119122487 |url=https://books.google.com/books?id=IbZNCAAAQBAJ&pg=PA149}}</ref>).
==Background==
As software is updated or changed, or reused on a modified target, emergence of new faults and/or re-emergence of old faults is quite common.
Sometimes re-emergence occurs because a fix gets lost through poor [[revision control]] practices (or simple [[human error]] in revision control). Often, a fix for a problem will be "[[software brittleness|fragile]]" in that it fixes the problem in the narrow case where it was first observed but not in more general cases which may arise over the lifetime of the software. Frequently, a fix for a problem in one area inadvertently causes a [[software bug]] in another area.
Finally, it may happen that, when some feature is redesigned, some of the same mistakes that were made in the original implementation of the feature are made in the redesign. Therefore, in most software development situations, it is considered [[Best Coding Practices|good coding practice]], when a bug is located and fixed, to record a test that exposes the bug and re-run that test regularly after subsequent changes to the program.<ref name="kolawa">{{cite book | last = Kolawa | first = Adam |author2=Huizinga, Dorota | title = Automated Defect Prevention: Best Practices in Software Management | url = http://www.wiley.com/WileyCDA/WileyTitle/productCd-0470042125.html | year = 2007 | publisher = Wiley-IEEE Computer Society Press | page=73| isbn = 978-0-470-04212-0 }}</ref>
Although this may be done through [[manual testing]] procedures using programming techniques, it is often done using [[automated testing]] tools.<ref>[http://safari.oreilly.com/0201794292/ch08lev1sec4 Automate Regression Tests When Feasible], Automated Testing: Selected Best Practices, Elfriede Dustin, Safari Books Online</ref> Such a [[test suite]] contains software tools that allow the testing environment to execute all the regression [[test case]]s automatically; some projects even set up automated systems to re-run all regression tests at specified intervals and report any failures (which could imply a regression or an out-of-date test).<ref>{{cite web| url=http://www.drdobbs.com/tools/change-code-without-fear/206105233| title=Change Code Without Fear: Utilize a Regression Safety Net|last=daVeiga|first=Nada|work=[[Dr. Dobb's Journal]]| date=2008-02-06}}</ref>
Common strategies are to run such a system after every successful compile (for small projects), every night, or once a week. Those strategies can be automated by an external tool.
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== The role of AI in Regression Testing ==
Regression testing is a crucial phase in software development, ensuring that new code changes do not negatively impact existing functionalities. Over time, the role of Artificial Intelligence (AI) in regression testing has significantly evolved, offering several advantages to streamline and enhance the testing process.
=== Automated Test Creation
One of the primary roles of AI in regression testing tools is the ability to autonomously generate and automate test cases. AI algorithms can analyze the software's codebase and identify potential areas where regression tests should be conducted. This automation significantly reduces the manual effort required for test case creation, making the testing process more efficient.
=== Test Maintenance
AI-powered regression testing tools are equipped with capabilities to efficiently manage and update test suites. These tools can detect changes in the application's codebase and automatically adjust test cases accordingly, reducing the need for extensive manual test maintenance. This leads to a more agile and responsive testing process.
=== Enhanced Test Coverage
AI-driven regression testing tools can identify and prioritize test scenarios based on historical data and code changes. This ensures that critical test cases are executed more frequently, thereby enhancing test coverage and increasing the chances of identifying regressions early in the development cycle.
=== Efficient Bug Detection
AI algorithms can be trained to identify patterns associated with common regression issues. This enables these tools to detect potential regressions more effectively and with greater accuracy. AI can also perform exploratory testing, helping testers uncover hidden bugs and improve overall software quality.
=== Automated Reporting
AI can automate the generation of detailed test reports. These reports provide actionable insights into the state of the software, including information on test results, code changes, and potential regressions. Automated reporting saves time for quality assurance teams and allows them to focus on analyzing results rather than report generation.
=== AI Oversight
In regression testing, AI can work in conjunction with human testers. QA experts can monitor the performance of AI algorithms, provide evaluations, and offer feedback for continuous improvement. This collaborative approach ensures that AI adapts to evolving testing needs.
=== Product-Centric Approach
AI-powered regression testing tools enable development teams to concentrate on product development rather than managing testing frameworks, libraries, and scripts. This shift towards a product-centric approach enhances the overall efficiency of the software development process.
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