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A CI build usually fails because of a code or quality-check regression, a flaky test, a dependency problem, a mismatch between the developer’s machine and the CI runner, or an infrastructure issue. Start with the first failing step in the job log: it is the best clue to which category fits. A rerun that passes points toward an intermittent test or service problem, but does not prove the code is correct.
These are five common categories, not a universal ranking. The causes overlap: for example, a test may be flaky only when CI runs it in parallel, or a dependency change may expose a defect in application code.
1. Flaky or nondeterministic tests
A flaky test passes or fails without a relevant code change because its result depends on uncontrolled state. As the pytest documentation puts it: “A flaky test indicates that the test relies on some system state that is not being appropriately controlled.”
That state can include shared fixtures, test ordering, timing, concurrency, randomness, network calls, or cleanup that leaves files or services behind. CI can expose these dependencies more often than a local run because it may execute tests in a different order or in parallel.
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- Clues: the same commit passes on rerun; failures move between tests; or a test fails only under parallel execution.
- Evidence to capture: test order, random seed if applicable, worker count, full stack trace, and whether a rerun changes the result.
- Next step: isolate shared state, control ordering and concurrency, and check setup and cleanup before treating the failure as a code regression.
A 2023 multivocal review also examines flaky tests as a recurring challenge in software testing: the review.
2. Code, compilation, or quality-check regressions
A commit can introduce a compile error, failing assertion, lint or type-check violation, security finding, or performance regression. These failures are usually reproducible for the same code and environment, and the error often points to a changed file or a check that evaluates it.
Read the earliest meaningful error, not just the final “job failed” summary. A compiler diagnostic, assertion trace, or linter message may identify the source file and rule directly. If the failing check is security- or performance-related, inspect its report and thresholds rather than assuming the application’s tests are the only gate.
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A 2026 empirical study of GitHub Actions workflows treats source-code issues, quality checks, vulnerabilities, performance degradation, and test failures as distinct failure categories. In that study’s sample, 54 of 375 cases (14.4%) belonged to one identified category; that is a study-specific result, not a general CI failure rate or a universal ranking of causes. See the ACM study for its scope and category definitions.
3. Dependency resolution and version conflicts
A build may fail before tests run if a package cannot be found, installation resolves incompatible versions, or packages are installed in the wrong order. It can also fail after installation when an upstream dependency changes behavior. In that case, application code may be unchanged even though the test result has changed.
Travis CI identifies upstream dependency changes as a common reason tests can suddenly break without a major code change. Its guidance on build stages and installation ordering can help when the sequence of setup steps matters.
- Save the complete package-manager and resolver output; note where installation stops.
- Compare the lockfile with the last green commit and look for unexpected drift.
- Record the package-manager version and the source of downloaded artifacts.
- Check whether the failure is a missing package, a version conflict, an installation-order problem, or a behavior change after resolution.
4. CI configuration and environment mismatch
“Works on my machine” often means the workstation and runner are not actually using the same conditions. Differences in runtime or toolchain versions, operating-system images, environment variables, credentials, locale, time zone, filesystem behavior, submodule settings, or service configuration can change build results.
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- Compare the failed and last green run’s runner image, runtime, compiler, and package-manager versions.
- Check required variables, credentials, services, submodule options, locale, and time zone.
- Look for machine-specific assumptions, such as case-sensitive paths or writable directories.
- Use retries only for operations that can reasonably recover; a retry should be bounded and should not hide a persistent failure.
5. Infrastructure, network, or unrelated-build failure
Not every red pipeline points to a defect in the latest change. Network timeouts, unavailable external services, runner instability, resource exhaustion, or commands that exceed a timeout can break a job even when application code is sound. A failure in an untouched component can also be unrelated to the push under test.
A 2026 empirical study defines an unrelated build failure as one whose root cause cannot be traced to files changed by the associated push, or that developers confirm is unrelated. That definition is useful for triage: “unrelated” is a conclusion supported by evidence, not a reason to ignore a red build. See the ACM study.
Quick Recap
- Check whether the same commit fails on the last green runner or in a different run.
- Look for timeouts, connection errors, service availability issues, and resource-limit messages in the full log.
- See whether the error occurs in files or components untouched by the change.
- Check runner, network, and upstream-service history before reverting code that does not explain the failure.
How the five causes differ
| Cause | Reproducibility | Link to changed files | External-service dependence | Useful evidence |
|---|---|---|---|---|
| Flaky or nondeterministic test | Intermittent; may change on rerun or with ordering and concurrency | May be in changed tests or exposed by a change; can also surface without a relevant file change | Possible, especially when tests call networks or services | Test order, seed, worker count, repeated-run results, shared-state and cleanup behavior |
| Code or quality-check regression | Usually reproducible for the same code and environment | Often traceable to changed source, tests, or configuration | Usually not required | First diagnostic, failing assertion, lint/type/security report, or performance result |
| Dependency issue | Often reproducible with the same resolution; may appear after upstream changes | May follow a lockfile or manifest change, or an upstream change outside the repository | May depend on package registries or artifact sources | Lockfile diff, resolver output, package-manager version, artifact source |
| Configuration or environment mismatch | Often repeatable on a particular runner or setup | May trace to workflow changes, but can also reflect runner-image or local-environment differences | Possible, depending on configured services | Runner image, tool versions, environment settings, credentials, services, submodule and filesystem assumptions |
| Infrastructure or unrelated failure | Can be intermittent; may persist while a service or runner is impaired | May have no traceable connection to changed files | Often relevant for network or upstream-service failures | Timeout and resource logs, service history, runner comparison, and whether untouched components fail |
A practical sequence for diagnosing a failed CI build
- Find the first failing step. Open the complete job log and locate the earliest actionable error, rather than relying on the final status line.
- Preserve the run context. Record the commit SHA, runner image, runtime and tool versions, dependency lockfile, test order or seed, and relevant environment settings with the stack trace.
- Compare with the most recent green run. Identify changes in code, dependencies, runner image, workflow configuration, and services. A different result on the same commit is evidence of an intermittent test or infrastructure problem, not proof the code is correct.
- Follow the failure signature. For intermittent tests, isolate shared state and control order and concurrency. For installation failures, inspect resolver output and lockfile drift. For environment errors, make tool versions, variables, services, and submodule settings explicit.
- Check for failures unrelated to the push. If no changed file explains the problem, examine runner, network, and upstream-service history and determine whether untouched components show the same error before reverting code.
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