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Regression testing helps catch behavior that breaks after a software change, but it has recurring costs: suites can grow until full reruns slow feedback, tests need ongoing maintenance, and flaky results can send engineers investigating failures that the change did not cause. Reducing the suite can save time, but it requires deciding what to omit and checking whether the remaining tests provide adequate coverage. These drawbacks vary by system and team; they do not make regression testing inherently too expensive or unreliable.
What regression testing costs as a product grows
Regression tests check whether existing behavior still works after a code change. The cost is not limited to writing a test once: tests must be run, kept aligned with the product, and interpreted whenever a change is made. A survey by Yoo and Harman on regression-test minimization, selection, and prioritization describes a common scaling problem: suites tend to grow as software evolves, and running the entire suite can eventually become too costly.
Growth can affect both execution time and the time developers wait for useful feedback. A larger suite also means more results to examine, especially when failures are intermittent or require coordination across teams. These are pressures to manage, not proof that every large suite is inefficient. Actual costs depend on the software, test design, execution environment, and organization.
Why suites keep growing
New features, bug fixes, and supported behaviors can each prompt additional tests. Retaining those tests protects against regressions in more cases, but also expands the set that may need to run and be maintained. Removing tests simply because they are old or slow can discard useful checks; keeping every test in every run can make feedback less timely. Teams therefore face a continuing choice about which tests belong in which stage of their workflow.
Execution time is only one cost
Automated testing still takes schedule time to develop and maintain. Carnegie Mellon University’s Software Engineering Institute identifies test development and maintenance as planning concerns. In practice, teams also need ownership for updating tests when expected behavior changes and for diagnosing failures. The cost is therefore broader than machine time: it includes engineering attention and the organizational effort needed to keep the suite understandable and useful.
Why reducing the suite is a trade-off
Regression-test minimization, selection, and prioritization are established approaches to managing the cost of large suites, as described in the Yoo and Harman survey. They do not make the cost disappear. A team must decide which tests to retain or run, how to order them, and how to assess whether the resulting approach still covers the behaviors and faults that matter.
| Approach | Potential benefit | Cost or risk to manage |
|---|---|---|
| Run the full suite | Exercises the tests in the available suite rather than omitting tests from that run. | Execution can become costly as the suite grows; running all tests may lengthen feedback. |
| Select or minimize tests | Can reduce the set run for a change or workflow. | Selection decisions take work, and the team must evaluate what coverage the reduced set preserves. |
| Prioritize tests | Can place selected tests earlier to provide earlier results. | Ordering tests does not itself establish that omitted or later tests are unnecessary; the prioritization approach still needs assessment. |
The table describes general trade-offs, not guaranteed outcomes. The survey establishes that these strategies have been studied to manage suite cost; it does not provide a universal rule for which one a team should use. A selection that works for one project may not be adequate for another.
How to judge a reduced run
- State which tests are being selected and why; an unexplained subset is difficult to evaluate or maintain.
- Consider which changed components and existing behaviors the chosen tests exercise. The useful question is not only how many tests remain, but what the selection can miss.
- Assess the selection method against the team’s needs rather than assuming that shorter execution means equivalent confidence.
- Keep a path for broader testing where appropriate. Selection and prioritization manage the cost of execution; they do not prove that a smaller run catches every regression.
These are decision checks, not a prescribed selection algorithm. The available evidence supports the need to choose tests and assess adequacy, but does not establish one method as best for all systems.
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A flaky test is nondeterministic: under circumstances where the relevant code has not meaningfully changed, it can produce different outcomes. That makes a failure harder to interpret. A red result may indicate a regression, or it may be an intermittent failure unrelated to the change under review. Engineers may have to reproduce the outcome and investigate before they can decide whether the change is responsible.
A multivocal review of flaky-test causes, detection, impact, and responses, published in the Journal of Systems and Software in December 2023, associates flakiness with reduced testing effectiveness and efficiency and with delayed releases. The mechanism matters: time spent sorting out an unreliable signal is time not spent confidently acting on a real regression. If teams repeatedly encounter results that do not reproduce, confidence in the suite can also suffer.
Why a flaky failure can be expensive
- A test fails during a regression run.
- The team must determine whether the failure is related to the software change or is an intermittent result.
- If it cannot be reproduced consistently, diagnosis may involve investigating the test and its execution conditions as well as the changed code.
- Until the result is understood, the failure can delay a decision about the change or release.
This is not a claim that every flaky failure delays a release. It explains why nondeterminism reduces the usefulness of pass/fail results and can create investigation work.
What the studies do—and do not—show
A Mozilla Foundation summary dated 12 July 2019 reports that a study classified 200 flaky tests with 21 professional developers and surveyed 121 developers. Those are the study’s sample counts, not estimates of how prevalent flakiness is across the software industry.
Microsoft Research’s ICSE 2020 study examined six large-scale proprietary projects. In those projects, asynchronous calls were the leading cause reported by the study; that finding is not a universal ranking of causes. The study also reports that, in several cases, developer-claimed fixes did not reduce the observed frequency of flaky failures. This illustrates a practical drawback: a change believed to fix a flaky test may not actually resolve the observed problem.
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The same Microsoft Research abstract describes FaTB reducing running time by up to 78% in its evaluation of five flaky tests, without empirically affecting the frequency of those tests’ flaky failures. That is a narrow evaluation result, not a general expectation for test-running time or a guarantee for other suites.
Maintenance and coordination can become organizational burdens
Testing challenges are not identical across teams. A study of regression testing in large-scale embedded software development reports issues involving test time, information management, suite maintenance, communication, test selection and prioritization, and assessment. That evidence concerns the setting studied; it should not be treated as representative of every software organization.
Still, it highlights a distinction that can be missed when regression testing is discussed only as a technical activity. A suite can be hard to use when people cannot tell what its tests cover, who owns them, what changed, or how to interpret a result. In a large or distributed effort, coordinating those decisions may take additional time. The SEI’s planning guidance likewise identifies test development and maintenance as work that needs to be accounted for, rather than assumed to be free once automation exists.
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Questions that expose ownership gaps
- Who updates a test when intended product behavior changes?
- Can the team identify what a failing test covers and who can diagnose it?
- When a test is removed from a routine run, is the reason and coverage trade-off understood?
- Are flaky failures recorded and investigated, or do they become familiar noise?
These questions are useful because selection, maintenance, and failure diagnosis all rely on shared understanding. They are not claims that every team needs a particular role or process; the embedded-software study’s reported challenges are context-specific.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Ways to manage drawbacks without abandoning regression testing
The evidence points to managing trade-offs rather than treating regression testing as either free or inherently wasteful. A practical approach is to make costs and coverage decisions visible and to preserve confidence in the test signal.
- Plan for upkeep. Include test development and maintenance in planning, as the Software Engineering Institute advises. Tests that no one can update or interpret can become a liability.
- Choose runs deliberately. Use selection or prioritization to manage execution cost only when the team can explain the choice and assess what coverage remains.
- Distinguish signal from noise. Treat intermittent failures as investigation items, not automatically as proof that a code change broke behavior.
- Review the workflow in its context. A large embedded project may face information-sharing and coordination demands that do not apply in the same way to a smaller team. Do not import another setting’s solution without checking the fit.
- Evaluate claimed fixes. The Microsoft Research findings show that developer-claimed fixes did not always reduce observed flaky-failure frequency in several studied cases. Verify whether the failure pattern changes instead of relying on intention alone.
None of these actions removes the trade-off. A full run costs time; a reduced run needs an adequacy check; flaky tests need investigation; and maintained automation still consumes engineering effort. The goal is to make each cost proportionate to the confidence the team needs.
Where ScreenshotNeo fits—and where it does not
ScreenshotNeo is a website screenshot API and MCP server, not a regression-test selection or flaky-test management method. It may be relevant when a team’s regression workflow includes capturing website pages for visual review: the service can return a screenshot from a URL, and its options include full-page capture, CSS-selector element capture, viewport and device settings, and custom CSS or JavaScript. Those capabilities do not establish that it detects visual differences or replaces a visual regression testing system.
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For a website capture step, one GET request can return an image. See the ScreenshotNeo API documentation for request details.
curl -G "https://api.screenshotneo.com/v1/shot" -d access_key=YOUR_API_KEY --data-urlencode url=https://stripe.com -o shot.webpScreenshotNeo removes cookie and consent banners, newsletter popups, and chat widgets before capture; each cleanup step can be turned off. Bot checks or CAPTCHAs, blank pages, timeouts, failed loads, and cache hits are not billed, and the response identifies the page verdict and billing status in headers. Its MCP server provides the tools take_screenshot, get_page_info, and capture_pdf for AI agents and MCP clients. The free plan includes 1,000 screenshots a month with no card; paid plans start at $5 for 3,000 shots.
Sign up free for ScreenshotNeo.
When the drawbacks matter most
Regression testing’s disadvantages become material when suite growth slows feedback, maintenance competes with product work, test selection reduces confidence, or flaky failures make results difficult to trust. They are reasons to examine how a suite is run and owned—not reasons to assume that regression testing is always too costly. The studies cited here establish recurring challenges and bounded examples, not a single cost threshold or solution that applies to every team.
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