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Software Quality

AI tooling for process methods

These methods run on human judgment, so AI is taking a turn at the judging rather than automating a check. The field moves quickly, and these entries may be out of date.

Code review

The built-in reviewers (Claude, Copilot, Cursor) and the open-source PR-Agent comment at PR time. An early-results paper reports initial tests of a four-agent GPT-4 reviewer and leaves the comparison against manual review to future work (Rasheed et al. 2024)1. Human review catches mostly small logical mistakes rather than design problems (Code review).

Exploratory testing

General-purpose vision-LLM browser agents (Claude / OpenAI computer use, the open-source Browser Use) extend happy-path coverage. WebVoyager (He et al. 2024)2 and AppAgent (Zhang et al. 2023)3 are research agents for the web and for smartphones; VisualWebArena (Koh et al. 2024)4 evaluates such agents on visual web tasks. These agents substitute poorly for skilled exploratory testing.

Refactoring practice

General-purpose agents (Cursor, Claude Code, Aider) are the everyday tool; JetBrains AI Assistant augments IntelliJ's mechanical refactorings. Structured bulk refactoring uses a deterministic rule engine as the oracle: OpenRewrite. SWE-agent and AutoCodeRover are research systems for repo-level autonomous changes. CodeScene's hotspot core is legitimate; its smell-catalog layer is folklore-tier per Git-history hotspots.

Requirements quality checking

Requirements tooling splits by activity: gathering requirements from informal input has usable tools, while auditing existing requirements for ambiguity and contradiction is mostly research (AI tooling for requirements engineering).

References

  1. Rasheed, Zeeshan, Malik Abdul Sami, Muhammad Waseem, et al. 2024. AI-powered Code Review with LLMs: Early Results. https://doi.org/10.48550/arXiv.2404.18496. ↩

  2. He, Hongliang, Wenlin Yao, Kaixin Ma, et al. 2024. WebVoyager: Building an End-to-End Web Agent with Large Multimodal Models. https://doi.org/10.48550/arXiv.2401.13919. ↩

  3. Zhang, Chi, Zhao Yang, Jiaxuan Liu, et al. 2023. AppAgent: Multimodal Agents as Smartphone Users. https://doi.org/10.48550/arXiv.2312.13771. ↩

  4. Koh, Jing Yu, Robert Lo, Lawrence Jang, et al. 2024. "VisualWebArena: Evaluating Multimodal Agents on Realistic Visual Web Tasks." Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 881–905. https://doi.org/10.18653/v1/2024.acl-long.50. ↩