Machine learning can help software teams decide where to look for defects, flag unusual program behavior when expected results are hard to specify, and identify tests that may be flaky. These are three different tasks: a defect predictor estimates risk from project history; an anomaly detector flags behavior that differs from learned patterns; and a flakiness detector estimates whether a test’s result may vary between runs. None proves that a bug exists or that a failure is harmless. Treat each output as a lead to investigate, not a verdict.
Three different testing problems, three different ML signals
The right approach depends on what you want to detect and what evidence you have. In particular, defect prediction is not another name for anomaly detection.
| Approach | What it flags | Evidence it uses | What the result means |
|---|---|---|---|
| Defect prediction | Software components considered more likely to be defect-prone | Historical defect labels and features from code or project history | A risk ranking for prioritizing review or testing—not confirmation of a defect |
| Anomaly detection for testing | An execution, output, or trace that differs from learned behavior | Execution inputs, outputs, traces, or other observations | A potentially unusual result that needs comparison with intended behavior |
| Flaky-test detection | A test whose outcome may change across runs without relevant code changes | Test history, dynamic features, and sometimes rerun outcomes | An estimate of instability; reruns can provide additional evidence |
These methods can complement ordinary tests and code review, but their output is bounded by the data available and the way the model is evaluated.
How machine learning predicts defect-prone code
A defect-prediction system learns from software units—such as files or modules—that have been labeled according to their association with past defects. It extracts features from code or project history, trains a classifier or ranking model, and assigns risk estimates to units in the current project. A team can use those estimates to prioritize manual review or testing when time is limited.
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What it can and cannot tell you
The prediction is about patterns in the training data, not a static analysis proof or a verified bug report. A highly ranked component may be sound; a low-ranked one may still contain a serious defect. The result is useful only if the labels and features capture patterns relevant to the codebase and the decision being made.
A 2022 systematic literature review reports that commonly used defect-prediction datasets can have inadequate features and validation, and too few labels to represent defect detail. That makes project-specific validation and transparent data preparation important. Read the review.
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Practical safeguards
- Document what counts as a defect, what unit receives a label, and the time period covered.
- Check that features would actually have been available when the prediction is made. Otherwise, information from after a defect was fixed or reported can leak into evaluation.
- Validate on data separated by time or project where appropriate, rather than assuming a random split represents future releases.
- Use the score to allocate attention, and retain ordinary review and testing for both high- and low-scoring components.
How anomaly detection helps when expected results are hard to specify
A test oracle determines whether a program behaved correctly for a given execution. When a complete executable specification is unavailable, it can be difficult to label every output as right or wrong. Anomaly-based approaches learn patterns from execution data—such as inputs and outputs or execution traces—and flag cases that depart from those patterns.
This can help surface behavior for investigation, but unusual does not automatically mean incorrect. The learned pattern describes observed behavior, which could include a bug or an unintended convention. A flagged case still needs to be checked against requirements, domain knowledge, or a stronger oracle.
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A 2019 empirical comparison of machine-learning strategies and Daikon’s dynamic analysis found semi-supervised approaches performed better than Daikon in most of the evaluated systems, but not all. The result is specific to the systems and methods in that comparison, not a general ranking for every project. See the IEEE paper.
Using a flagged execution
- Capture the inputs, outputs, traces, and environment details needed to reproduce the execution.
- Determine what the model considers typical and which observations caused the alert.
- Compare the case with specifications, known invariants, domain expectations, and reproducible runs.
- Only classify it as a defect after investigation establishes a mismatch with intended behavior; otherwise, record the explanation or improve the oracle and data.
How machine learning identifies flaky tests
A flaky test can pass or fail for the same test and program version under conditions meant to remain constant. Flakiness is therefore about unstable test outcomes; it does not by itself establish that the product has a defect or that a particular failure can be ignored.
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ML approaches can use a test’s past behavior and dynamic features to estimate whether it is likely to be flaky. Rerunning tests can provide stronger evidence of instability, but repeated execution consumes CI time. A combined approach can use predictions to guide which tests to rerun rather than treating a model score as confirmation.
Parry and colleagues evaluated CANNIER, which combines machine learning and rerun-based techniques, on 89,668 test cases from 30 Python projects. In that evaluation, they reported an order-of-magnitude reduction in rerun-based detection time while maintaining better detection performance than ML alone. This is a result for that study’s dataset and setup; it is not a performance guarantee for other languages, repositories, or CI systems. Read the CANNIER study.
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Distinguish prediction from confirmation
- A model’s score helps prioritize investigation; it does not establish that a test is flaky.
- Reruns can reveal inconsistent outcomes, but their value depends on keeping relevant conditions controlled and recording the environment.
- Do not automatically suppress a failure based on a flakiness prediction. Preserve the failure evidence and determine whether the instability belongs to the test, environment, or product.
Testing software that contains machine learning
There is a related but distinct challenge: testing an ML system itself. In that case, teams may need to test data, learning programs, and supporting frameworks against properties such as correctness, robustness, and fairness. Zhang, Harman, Ma, and Liu’s 2020 survey covers 138 research papers and organizes ML testing by properties, system components, workflows, and application scenarios. Read the survey record.
Industry practice also involves data collection, execution, and interpretation of results—not just choosing a model. A Microsoft Research empirical study reports 87 survey responses and interviews with 7 senior practitioners. It identifies challenges including component entanglement and model-performance regression during execution, and describes result analysis as combining quantitative metrics with qualitative practitioner judgment. Those figures describe that study, not all software organizations. Read the Microsoft Research study.
How to choose and evaluate an approach
Start with the decision the team needs to make, then check whether the available data can support it. A model that performs well on one task or dataset may not transfer to another task, release, or environment.
| Decision axis | Questions to answer |
|---|---|
| Purpose | Are you ranking risky code, flagging unusual execution behavior, or identifying potentially unstable tests? |
| Evidence | Do you have defect labels, execution traces, input/output observations, test history, dynamic features, or rerun outcomes? |
| Representativeness | Do the data reflect the current codebase, test environment, release behavior, and kinds of defects or failures that matter? |
| Detection quality | Which errors matter most: missed defects, false alerts, poor precision, or poor recall? Choose task-appropriate measures and inspect the cases behind them. |
| Cost | What time and infrastructure are required for feature collection, instrumentation, reruns, training, and result analysis? |
| Robustness to change | What happens when code, tests, dependencies, environments, or data distributions change? Decide when to revalidate. |
| Human verification | Can an engineer review the signal against a specification or domain knowledge and take a concrete next step? |
A practical rollout
- Define the target precisely: a defect-prone component, an anomalous execution, or a flaky test.
- Inventory the labels and observations available, including how they were collected and what may be missing.
- Establish a baseline and evaluation plan that reflects the intended use, such as prioritization in a future release or targeted reruns.
- Track false alerts and missed cases as well as runtime and data-collection cost; a score alone does not show whether the system helps the team.
- Present alerts with enough context to investigate, and keep a path for recording the outcome and updating labels or tests.
- Reassess after meaningful changes to code, test suites, environment, or data. Earlier performance does not establish current reliability.
Common failure modes and how to respond
- High-risk code is treated as a confirmed bug. Use the prediction to prioritize inspection; verify the suspected issue through tests, review, or a reproducible failure.
- An unusual output is called incorrect without an oracle. Check intended behavior and domain constraints before labeling it a defect.
- A flaky-test score is used to discard failures. Preserve failures and investigate them; use controlled reruns where the cost is justified.
- Evaluation results look strong but do not transfer. Check for unrepresentative data, weak labels, leakage, and changes between the evaluation setting and current project.
- Alerts are too numerous to act on. Review false alerts and set thresholds according to the cost of missed problems versus investigation time; keep the threshold and its rationale visible.
- Reruns slow the pipeline. Use history or model-based prioritization to focus reruns, while retaining a route for high-impact tests that the model may miss.
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