TDAD: Test-Driven Agentic Development - Reducing Code Regressions in AI Coding Agents via Graph-Based Impact Analysis
This addresses a critical issue for developers using AI coding agents by providing a practical tool to improve code reliability, though it is an incremental improvement over existing methods.
The paper tackles the problem of AI coding agents introducing regressions by breaking previously passing tests, and presents TDAD, a tool that uses graph-based impact analysis to reduce regressions by 70% from 6.08% to 1.82% in evaluations on SWE-bench Verified.
AI coding agents can resolve real-world software issues, yet they frequently introduce regressions -- breaking tests that previously passed. Current benchmarks focus almost exclusively on resolution rate, leaving regression behavior under-studied. This paper presents TDAD (Test-Driven Agentic Development), an open-source tool that performs pre-change impact analysis for AI coding agents. TDAD builds a dependency map between source code and tests so that before committing a patch, the agent knows which tests to verify and can self-correct. The map is delivered as a lightweight agent skill -- a static text file the agent queries at runtime. Evaluated on SWE-bench Verified with two open-weight models running on consumer hardware (Qwen3-Coder 30B, 100 instances; Qwen3.5-35B-A3B, 25 instances), TDAD reduced regressions by 70% (6.08% to 1.82%) compared to a vanilla baseline. In contrast, adding TDD procedural instructions without targeted test context increased regressions to 9.94% -- worse than no intervention at all. When deployed as an agent skill with a different model and framework, TDAD improved issue-resolution rate from 24% to 32%, confirming that surfacing contextual information outperforms prescribing procedural workflows. All code, data, and logs are publicly available at https://github.com/pepealonso95/TDAD.