Группировка ошибок вручную и автоматически, или чем тестировщику занять своё время

Description and use

**Brief Summary**
This open‑source tool demonstrates manual and automatic bug‑grouping techniques, as described in a Habr article titled “Группировка ошибок вручную и автоматически, или чем тестировщику занять своё время”. It showcases a small prototype that clusters similar defects using simple heuristics and optional script‑driven rules, helping testers explore different grouping strategies.

**Value**

• **Rapid experimentation:** Provides ready‑made examples of both manual (tag‑based) and automated (similarity‑score) grouping, allowing QA teams to prototype their own triage pipelines without building the logic from scratch.
• **Learning resource:** The code and accompanying article serve as a practical guide for newcomers to defect management, illustrating how to combine human judgment with lightweight automation.
• **Extensibility:** The modular design (separate parsers, grouping engines, and output adapters) makes it easy to plug in custom data sources (e.g., JIRA, Azure DevOps) or replace the similarity algorithm with more sophisticated ML models.

**Practical Adoption Path**

1. **Review the repository** – clone the project, run the provided examples, and read the Habr article to understand the intended workflow.
2. **Validate licensing & maintenance** – confirm the license is compatible with your organization and check the issue tracker for recent activity.
3. **Integrate a data source** – replace the sample CSV/JSON input with your own defect export (e.g., from a bug‑tracker API).
4. **Customize grouping rules** – start with the built‑in heuristics, then add or tweak rule files to reflect your team’s taxonomy (severity, component, etc.).
5. **Pilot in a sandbox** – run the tool on a subset of recent tickets, compare the generated clusters with the current manual triage, and iterate on thresholds.
6. **Roll out** – once the clusters meet accuracy expectations, embed the script into your CI/CD or QA dashboard, optionally scheduling it as a nightly job.

**Production Readiness**

• **Maturity:** Medium. The project is a prototype with limited documentation and sparse activity signals (last update Tue 30 Jul). It is suitable for internal tooling or proof‑of‑concepts but not for mission‑critical production without additional hardening.
• **Dependencies:** Minimal (standard Python libraries, optional scikit‑learn for similarity); verify version compatibility with your environment.
• **Risks:** Limited community support, infrequent releases, and scarce issue resolution. Before production use, perform a security audit, add comprehensive tests, and consider forking to maintain a stable release cycle.

*Bottom line:* The project offers a handy sandbox for experimenting with bug‑grouping strategies and can be adopted quickly for internal QA workflows, provided you perform the necessary due‑diligence and add the robustness required for production environments.

Use in a workflow

Группировка ошибок вручную и автоматически, или чем тестировщику занять своё время may be useful when its README and activity match a concrete workflow.

Limitations and review notes

Medium: useful for prototypes or internal workflows, with dependency and maintenance checks before production.