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Challenges of Generative AI in Software Testing

Generative AI can help create test ideas, but generated tests still need review. Learn what studies show about oracles, flakiness, benchmarks, and effectiveness.

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Generative AI can help produce test ideas and test code, but a generated test is only a candidate: it may assert the wrong behavior, pass to no useful effect, or fail inconsistently. The strongest way to use it is to review its assertions, execute tests in the conditions that matter, and evaluate bug-detection strength rather than treating generated code or coverage as proof of quality.

What makes generative AI testing difficult?

Software testing depends on more than producing executable test code. A useful test needs a sound account of expected behavior, a reliable execution setup, and evidence that it can expose relevant faults. Generative AI can assist with parts of that work, but its output does not establish any of those things by itself.

  • Oracle quality: Does the test assert the right result for the behavior being tested?
  • Stability: Does it behave consistently across runs and relevant environments?
  • Evaluation validity: Does a benchmark measure performance on examples independent of the model’s training data?
  • Effectiveness: Does the test detect faults or otherwise help with the task, rather than merely execute lines of code?
  • Human review: Can a developer explain and maintain the test, including its assumptions?

These are related but distinct questions. A test can have high coverage and a weak oracle, or a plausible assertion and a flaky setup. Results from one task, language, dataset, or participant group should not be treated as a general score for generative AI testing.

Can AI generate correct test oracles?

A test oracle specifies the expected behavior against which an observed result is judged. Getting that expectation right is a difficult part of testing: a test may run successfully yet encode an incorrect assumption about what the software should do.

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A 2025 study by Davide Molinelli, Luca Di Grazia, Alberto Martin-Lopez, Michael D. Ernst, and Mauro Pezzè evaluated 13,866 oracles from 135 Java projects. The projects’ oracles were created after the training cutoffs of the models tested. In that experiment, generated oracles had an average mutation score of 43%, compared with 45% for human-designed oracles. The authors also identify limits for complex oracles and frame thorough oracle generation as an open problem.

Those figures describe the study’s models, Java projects, data, and evaluation method; they are not a universal performance estimate for all models or testing tasks. The small difference in average scores also does not establish that generated and human-designed oracles are interchangeable in practice. Review the expected behavior, especially for complex cases, rather than accepting an assertion because it looks reasonable or a test passes.

What mutation score can and cannot tell you

Mutation testing probes a test suite by introducing seeded faults, or mutations, and checking whether the tests expose them. A mutation score is one way to assess whether tests distinguish some faulty behavior from the unmodified program. It does not prove that the suite covers every important requirement or will catch every real defect.

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A 2024 study introduced MuTAP as an approach for assessing generated tests with mutation testing. It is a studied evaluation method, not a guarantee of test quality or a universally accepted single measure. Where feasible, use it alongside requirement-based review and other task-appropriate checks rather than substituting one metric for another.

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Why generated tests can be flaky

A flaky test can produce different outcomes without a relevant change to the program under test. This can make failures hard to diagnose and can reduce confidence in a test suite. A study of four database systems found a slightly higher proportion of flaky cases among LLM-generated tests than among existing tests in the settings examined.

Among 115 flaky generated tests examined in that study, 72 (63%) relied on an order that was not guaranteed. One example is a test that assumes query results arrive in a particular order without specifying SQL ORDER BY. The 63% figure applies to those examined flaky tests, not to all generated tests or all database software.

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When reviewing generated tests, look for assumptions about collection order, database state, timing, randomness, shared resources, and runtime environment. Run tests repeatedly in relevant conditions when stability matters. Repeated execution can reveal some instability, but it cannot guarantee that every flaky behavior will be found.

How benchmark contamination can distort confidence

A benchmark can overstate a model’s apparent ability if its evaluation examples overlap with the data used to train the model. The 2025 oracle study identifies this overlap as a threat to evaluation validity. Its use of post-cutoff project oracles addresses that threat for the reported experiment because those oracles postdated the tested models’ training cutoffs.

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That design does not prove that all test-generation benchmarks are contaminated, nor that post-cutoff data removes every possible evaluation bias. When interpreting a result, ask what data was used, when it was created relative to model training, and whether the reported task resembles the work you need the model to do. Different datasets and evaluation procedures cannot be compared as if they were one controlled contest.

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Hallucinations, reasoning errors, and review responsibility

ISTQB’s 2025 sample-exam materials describe hallucinations and reasoning errors as intrinsic challenges with current AI technologies, and state that testers cannot prevent them from occurring but should identify and mitigate their risks. This is certification guidance, not a measured estimate of how often errors happen.

For test generation, a plausible-looking assertion can still encode the wrong requirement, misunderstand an API, or rely on an assumption the application does not guarantee. Treat generated code and expected values as review candidates. Check them against specifications, established behavior, and the relevant system state. No general hallucination rate is established by the evidence summarized here, so a precise percentage would be misleading.

What user studies do—and do not—show

An observational study by Ardic, Le Dilavrec, and Zaidman in 2025 involved 12 undergraduate participants. Participants reported perceived time savings and help with test ideation, alongside diminished trust, concerns about quality, and a lack of ownership of generated tests. The study did not find significant effects of prompting strategies on measured test effectiveness or test code quality.

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These findings illustrate a useful distinction: a participant can feel that a tool saves time or helps generate ideas without a study finding a measurable improvement in test effectiveness. The small novice-student sample is not evidence about all professional teams, languages, or workflows. Use it as context for questions to investigate in your own setting, not as a prediction of team-wide outcomes.

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How to assess AI-generated tests in a real workflow

  1. Start with the behavior to verify. State the requirement, boundary case, or failure condition before asking for a test. This gives reviewers something concrete to compare against the generated oracle.
  2. Review the setup and assertions. Check test data, preconditions, expected values, cleanup, and any assumptions about ordering or timing. Reject tests whose expected behavior cannot be justified.
  3. Execute tests in relevant conditions. Run them in the project’s normal environment and repeat runs where instability could matter. Inspect failures for nondeterministic ordering or hidden state rather than simply rerunning until a pass appears.
  4. Measure more than coverage where feasible. Coverage can indicate which code executed, but does not by itself show that assertions would expose faults. Mutation testing, as explored by MuTAP and the oracle study, is one additional way to probe test strength.
  5. Check evaluation independence. For model or workflow comparisons, document the model, programming language, project type, dataset, and evaluation method. Prefer data whose relationship to model training is understood.
  6. Keep a human accountable for the test. The person approving it should be able to explain what behavior it protects and why its assertions are correct. This is also a practical response to the trust and ownership concerns observed in the undergraduate study.

Which comparisons are meaningful?

Compare approaches using the same task and dataset where possible. Keep the measured axis explicit: oracle correctness or strength, execution stability, evaluation independence, or a task-level outcome such as bug detection, error tracing, or localization. The studies summarized here use different settings, so their results should not be ranked against each other as though they shared one test environment.

Question Useful evidence to examine What not to infer
Are the expected results sound? Requirement review and, where suitable, mutation score. A passing test or a high line-coverage figure alone proves the oracle is correct.
Is the test stable? Repeated execution and inspection of ordering, state, timing, and environment assumptions. A few successful runs establish reliability in all conditions.
Is the benchmark independent? Dataset provenance and timing relative to model training cutoffs. One post-cutoff study proves all benchmarks are contaminated or unbiased.
Does the workflow help? Task-level outcomes and measured effectiveness, kept distinct from participant impressions. Perceived time savings necessarily improve test quality or bug detection.

Where website screenshots fit—and where they do not

A screenshot can preserve a rendered page as a visual artifact for a human reviewer or a separate UI-testing workflow. It does not determine whether an assertion is correct, establish that a generated test detects faults, or replace execution and review. ScreenshotNeo is a website screenshot API and MCP server, not a generative test-oracle evaluator. Its captures may be useful when a workflow needs page images; they should not be mistaken for evidence that a test suite is effective.

For example, a request can capture a page as an image:

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curl -G "https://api.screenshotneo.com/v1/shot" -d access_key=YOUR_API_KEY --data-urlencode url=https://stripe.com -o shot.webp

See the ScreenshotNeo API documentation for request options. The service accepts a URL and returns a screenshot or PDF; its clean-shot workflow accepts cookie or consent banners and removes more than 60 known consent platforms, newsletter popups, and chat widgets before capture, with each step able to be turned off. Bot checks or CAPTCHAs, blank pages, timeouts, failed loads, and cache hits are not billed; response headers identify the page verdict and billing status. Its MCP server provides take_screenshot, get_page_info, and capture_pdf tools for AI agents and other MCP clients.

ScreenshotNeo offers 1,000 shots per month free with no card; paid plans start at $5 for 3,000 shots. Every feature is on every plan. Those are capture-service terms, not testing-platform prices or a claim about test effectiveness. Sign up for ScreenshotNeo’s free plan to try 1,000 screenshots a month with no card.

What the current evidence supports

The evidence supports a measured conclusion: generative AI can assist with test ideation and test creation, but correctness, stability, evaluation validity, and measured effectiveness still need to be established for the task at hand. The oracle, database-flakiness, benchmark, and student studies each illuminate a different risk in a bounded setting; none supplies a universal score for AI-generated tests. Make claims from the checks you actually performed, and keep human review central to deciding what a test means.

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