3 ways to optimize regression testing
Testing was often a one-off event that happened at the end of a project before it was delivered into production. However, with the inception of agile test management platforms, testing has become more engaged throughout the software development lifecycle. As a result, regression testing has taken center stage to ensure that developed features continue to function properly after the program has been changed through patches, configuration adjustments or enhancements. Let’s take a look at a few things quality assurance teams can do to optimize their regression testing:
Regression test selection
An indexed selection of standard test cases is the best lead-in to regression test coverage. The standardization level of test cases should allow for version updates. High on the list are automated tests, as well as timing and perimeter requirements. Well-selected standard test cases provide a logical platform for effectively consolidated bug detection.
Begin by categorizing your tests into reusable, retestable, or obsolete cases. TechWell contributor, Sunil Sehgal, noted that organizing tests also allows you to compare tests according to the depth and extent of their focus on risk mitigation, to reveal test factors that peak your awareness. Organizing tests also allows you to adapt or delete obsolete test cases from your regression testing efforts.
QA teams should also take into consideration the scope of change to best assess the required capacity of the test project. For a visible upgrade to regression test efficiency levels and outcomes, begin by updating test cases that are too long, outdated, or too complex.
Added to the resource of standard test cases are new release specific test cases. Automated versioning allows for the expansion of standard regression test packs, covering core component functionalities.
Code review
Once established, test cases cannot be ignored. Consequently, test cases require recurrent assessment, or code reviews, to ensure that they continue to pull their weight in verifying component functionality. Industry expert Arthur Hicken — known as the Code Curmudgeon — noted that QA teams should combine with developers for code reviews to identify high risk areas of change. Regression test suites can consequently be finely tuned to analyze the impact of change.
Code review delves more deeply into the test case to research the causes of incorrect output, such as inconsistent logic, undefined variables, or syntax errors. The code is examined for mistakes with either a dynamic review while the code is being written, or a static review after the code is written. A logical error, for instance, requires a dynamic code review.
Regular code reviews are crucial to the application design phase. Normal standards for coding viability require reviews that check for coding:
- Reliability
- Capability
- Security
- Integration
- Flexibility
- Upgradability
- Maintainability
Automated test procedures integrated into software design and development allow QA testers to:
- Detect logical errors in the code
- Assess coverage of requirements
- Automate versioning
- Report and record outcomes
- Monitor results
Recurrent code reviews provide improved understanding of application capabilities and allow QA teams to update test scripts with current compliance needs. With diligent practices and the use of test case management tools for your teams, a code review inherently leads to better regression testing for improved product quality.
Metrics monitoring
When considering software testing metrics, it’s important to understand the context. Regression testing is intent upon mitigating risk by identifying coding deficiencies. The number of bugs found in a regression test can tell you a lot about the coding, the extent of coverage within previous testing, and the extent to which previous development and testing have been reciprocally integrated.
Metrics monitoring assesses process efficiency. Perhaps there were more defects than usual this sprint. Time constraints could be the reason why more problems exist than anticipated. An unexpected change order, or a new issue, may be responsible for areas of incomplete test coverage.
Accounting for variable details is crucial to verifying your team’s performance and optimizing your regression efforts to catch any bugs that the process may have missed. Data is incredibly crucial to business operations and production, making effective regression testing critical to product success.
Best practices in regression testing
Regression testing retests the unrevised portions of an application to ensure they have not been negatively impacted by revisions, upgrades, or updates. Testing verifies that new changes to software do not introduce new bugs or deficiencies in functionality, and that detected bugs are fixed. Integrated regression testing should permeate every release cycle.
Regression testing should exist in every test plan and project estimation. Integrated into the production cycle and timeline, regression tests best ensure quality releases with reduced time to market.
Testing begins when a bug is fixed, or new code is added to an application for any reason. Analytics target anticipated possibilities of new dependencies being added or existing dependencies being impacted by the change. Regression testing can be a quality measure of whether new code complies or interferes with the functionality of the existing code with which it interacts. Test builds therefore initially probe within direct dependencies of software components to ensure that new functionality does not negatively impact dependent relationships.
The extent of the test build should align with the scope of any newly added feature(s). QA team collaboration with developers can best determine the character and change scope of impact on software and system interactions.
The repetitive format of regression tests can be smoothly adapted to test automation for quick and efficient requirements coverage. In addition, carefully selecting test cases that reflect new innovations maximizes test efficiency while minimizing the requirement for resources.
The standard set of test cases used in regression testing require continuous updates to provide cover for new changes and upgrades. As well, an expanded scope of product development, with its accompanying added complexities, best responds to automated analytics that delve deeply into component functions and interactions to examine the extent and nature of dependent interfaces. The scope can be enormous with seemingly never-ending incremental fixes and patches to the system. Integrating automated test analytics into incremental agile product development allows QA teams to keep pace with the regression testing requirements of complex projects.
Rerunning tests to verify whether anticipated results are affected by recent coding changes is the basis of regression testing. Unanticipated results are compared to previously anticipated results, initiating additional code reviews. Once fixes to an issue are verified, the regression test cycle resumes with focus on an additional coding update.
The most effective regression test projects begin with a solid test plan which clearly outlines the testing strategy, efficiently allocates resources, and keenly defines exit criteria. Included among standard regression test suites should also be performance testing to analyze the stability of application interfaces with system infrastructures.
Best practices in regression tests encourage continuous automated test runs for quick bug detection and rapid test results. Test automation frees QA engineers from redundant processing to more effectively build the test architecture and update test procedures. Additionally, test automation can be designed to fully cover the test project, conquering the problem of overlooked defects.
Automated testing is intuitive by design, and therefore adept in analyzing software functionalities. Changes and upgrades to software products can create incompatible interactions among applications and interfaces that would otherwise be difficult to locate and analyze. Intrinsic to automated testing is quick targeting of the asymmetric process flows that pinpoint coding inaccuracies.
Manually running previously executed or standard test cases on newly updated software is repetitively monotonous, possibly causing breaks in testing focus and concentration. Manual testing is also time consuming. Efficiency and efficacy in regression testing reside in automated test management.
The extent of test automation should be determined by the anticipated:
- Scope of the test project
- Complexity of the software project
- Number of required standard test cases
- General consistency in software design
Categorizing tests, reviewing the code in your active cases, and monitoring your testing metrics, can optimize your regression test strategic plan.
In conclusion
Regression testing powerfully delves into the examination of coding updates and their impact upon application relationships with dependencies and interfaces. The basic concept of regression testing is to ensure the product’s ability to perform intended functions uninterrupted by modifications or improvements. Cross-functional collaboration guides QA teams in strategizing test builds that prevent unforeseen errors in the product release, and therefore mitigate risk to the organization.
Ensuring that software complies to developer standards is as well an essential factor in regression testing. Apart from detecting and fixing bugs, regression testing can fine tune coding innovations to refine the user experience.
Introducing change into existing software requires assurance that the change does not negatively affect software functions. Software doesn’t exist in a vacuum. Applications activate in connection with other software systems and applications. Regression testing consequently targets primary interactions at code update locations.
Focus strategies on high risk areas early in the test cycle. Keep the strategy flexible to allow for increasing scopes and multiple contingencies. Integrate testing into the incremental development process. Make maintaining existing regression testing suites a priority for increased coverage of issues and skill in bug detection.
As new releases to a product increase, so can the scope of the testing project and the size of the regression suites. A consistent testing strategy that is both flexible and repeatable allows regression testing operations to grow organically in alignment with the application. A consistent strategy is also the prime resource for projects with combined or multiple testing and development teams.
Regression testing makes software a better product. The quality of test coverage is dependent upon the test design and build. Although initial test planning and procedures can entail rigorous analysis and adjustments, there is no question that the process significantly heightens the quality of the software release.
By best ensuring that product upgrades and updates work the first time, regression testing is all about ensuring that your customers have a consistently viable product. If issues are found after release, regression efforts are crucial to smoothly fixing problems within application and performance frameworks.
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