Why Conduct Bug Analysis and How to Extract Value from Bug Data

Bug analysis is a valuable practice that benefits the entire project team, not just developers, by turning bugs into data that can improve testing methods, product quality, and development practices.

What can be analyzed from bugs? The bug record itself (title, description, steps, expected/actual results, screenshots, environment, creator, assignee, dates, priority, severity, resolution, status, reopen count, related requirements) and the stage at which the bug appears (interface testing, smoke testing, functional testing, UI/PM review, centralized testing, post‑release). A unified bug‑tracking standard is required for reliable analysis.

Bug resolution categories include coding errors, requirement changes, environment/configuration issues, design defects, non‑bugs, duplicate bugs, unreproducible bugs, legacy bugs, deferred fixes, compatibility issues, third‑party dependencies, mismatched documentation, and insufficient technical design.

Bug severity and priority are expressed in two dimensions: priority (high, medium, low) and severity (critical, serious, normal, informational, suggestion). Analyzing severity by iteration reveals test quality, while severity distribution per developer can indicate code quality.

Key data points for projects involve tracking effective bugs (excluding non‑bugs and duplicates) to assess individual and team performance, such as QA’s interface leak rate, functional test coverage, workload estimation, and bug effectiveness.

Perspectives:

QA view: interface test quality, functional test quality, workload, and bug precision.

Developer (RD) view: smoke test pass rate, code quality, reopen count, and bug severity.

Product/UI view: requirement design quality and change frequency.

Project view: bug creation and resolution trends, solution distribution, and the impact of process improvements (e.g., staged testing).

Analyzing individual bugs should focus on representative cases such as hard‑to‑detect issues, recurring patterns, integration problems, or “not‑bug” classifications.

Historical bug analysis across iterations enables evaluation of developer quality, QA thoroughness, and prediction of problematic areas, supporting data‑driven decision making.

Because manual analysis is labor‑intensive, tools and custom databases are recommended to store bug data, generate automated reports, and visualize trends, ultimately delivering long‑term benefits.