Issue tracking systems such as Redmine, Jira, GitHub Issues, or GitLab are not just administrative tools. They define how work flows through a team: what gets fixed, what gets postponed, and what silently turns into technical debt. In many teams, delays are caused not by difficult bugs, but by poorly written issue reports.
A weak issue report forces others to ask follow-up questions, guess the intent, or abandon the task entirely. A good issue, on the other hand, allows someone unfamiliar with the problem to understand it, reproduce it, and verify the fix with minimal back-and-forth.
This article walks through the most common mistakes in issue reporting and explains how to avoid them. The goal is simple: make issues actionable, reproducible, and verifiable.
What an Issue Is (and Why the Type Matters)
An issue represents a unit of work or a unit of failure that can be tracked and resolved. Not all issues are bugs, and treating everything as a bug quickly breaks prioritization and planning.
- Bug / Defect: existing behavior is incorrect or broken.
- Feature request: new functionality or behavior is desired.
- Task / Chore: maintenance work without visible user-facing change.
- Question / Support: clarification is needed, not a code change.
- Documentation: missing, unclear, or incorrect documentation.
Choosing the wrong type creates noise. Bugs compete with questions, tasks look urgent when they are not, and real defects may get buried.
Mistake 1: Vague or Meaningless Titles
Titles like “Bug”, “Doesn’t work”, or “Urgent issue” communicate nothing. They are impossible to search, impossible to triage, and useless in reports or dashboards.
A practical title formula is: component + symptom + context.
- Poor: “Simulation problem”
- Better: “Diffusion solver diverges when dt > 1e-3 with Neumann boundaries”
- Poor: “Graph is broken”
- Better: “Plot viewer does not update after parameter change during runtime”
Mistake 2: Missing or Incomplete Reproduction Steps
Without reproduction steps, an issue becomes guesswork. Even real problems are often closed as “cannot reproduce” simply because the reporter skipped key actions.
Good reproduction steps are:
- minimal (only what is necessary),
- ordered (step by step),
- repeatable (lead to the same outcome).
“Open the app and it crashes” is not a reproduction path. “Run the simulation with nx=2000, dt=1e-3, then enable adaptive stepping” is.
Mistake 3: No Expected Result
Many issues describe what went wrong but never explain what should have happened instead. Without an expected result, there is no clear definition of “fixed”.
Expected behavior does not need theory or justification. A simple comparison is enough:
- Expected: simulation remains stable and converges.
- Actual: values grow unbounded after 50 iterations.
This distinction turns subjective complaints into testable conditions.
Mistake 4: No Evidence or Artifacts
Text alone is often insufficient, especially for UI problems, numerical instability, or performance issues.
Useful evidence may include:
- error messages or stack traces,
- logs with timestamps,
- screenshots or short videos,
- plots showing unexpected behavior.
Avoid dumping raw logs without context. Highlight the relevant section and explain why it matters.
Mistake 5: Missing Environment Information
“Works on my machine” is not sarcasm, it is reality. Software behaves differently across versions, platforms, and configurations.
At minimum, an issue should include:
- application or library version,
- operating system,
- runtime (Python version, compiler, etc.).
For research or simulation software, environment details often also include mesh size, time step, solver type, tolerance, and random seed.
Mistake 6: Multiple Problems in One Issue
Combining unrelated problems into a single issue makes it difficult to assign, prioritize, or close correctly.
One issue should correspond to one root cause or one failure mode. If problems are related, they can be linked. If not, they should be split.
Mistake 7: Incorrect Priority or Severity
Declaring everything “critical” removes meaning from the label. Priority should reflect impact, not frustration.
A helpful approach is to describe impact explicitly:
- How many users are affected?
- Is data corrupted or lost?
- Does this block further work?
Let the team or product owner assign priority based on this information.
Mistake 8: No Minimal Reproducible Example
Large projects often hide small bugs. Providing a minimal reproducible example saves hours of investigation.
Examples depend on context:
- For code: a short snippet that triggers the issue.
- For simulations: minimal domain, parameters, and boundary conditions.
- For UI: test account, test data, or feature flag.
Mistake 9: Emotional or Accusatory Language
Statements like “this was broken again” or “nothing works” add no technical value. They also make collaboration harder.
Neutral language keeps the focus on the system, not on people. Describe behavior, not blame.
Mistake 10: Issues Without Clear Closure Criteria
An issue should have a clear condition under which it can be closed. Without this, fixes are subjective and issues are often reopened.
Closure criteria may be simple:
- test passes,
- error no longer appears,
- output matches expected reference.
Quick Checklist Before Submitting an Issue
- Is the title specific and searchable?
- Can someone else reproduce the issue?
- Are expected and actual results clearly stated?
- Is the environment described?
- Does the issue describe only one problem?
Conclusion
A well-written issue is not bureaucracy. It is an investment in clarity, speed, and shared understanding.
A useful rule of thumb is this: write the issue as if you will be the one fixing it two weeks from now, with no memory of today’s context. If the report still makes sense, it is probably good enough.