ABTest: Behavior-Driven Testing for AI Coding Agents
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866917427049136128 |
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| author | Dai, Wuyang Openja, Moses Pham, Hung Viet Uddin, Gias Yang, Jinqiu Wang, Song |
| author_facet | Dai, Wuyang Openja, Moses Pham, Hung Viet Uddin, Gias Yang, Jinqiu Wang, Song |
| contents | AI coding agents are increasingly integrated into real-world software development workflows, yet their robustness under diverse and adversarial scenarios remains poorly understood. We present ABTest, a behavior-driven fuzzing framework that systematically tests coding agents by turning real-world failure reports into repository-grounded behavioral tests. ABTest (1) mines user-reported anomalies to derive reusable workflow patterns (Interaction Patterns) and behaviors (Action types); (2) composes them into stepwise fuzzing templates; (3) instantiates executable test cases in real repositories; (4) executes them with coding agents while recording traces and artifacts; and (5) detects and validates anomalous behaviors.
We apply ABTest to three widely used coding agents: Claude Code, OpenAI Codex CLI, and Gemini CLI. From 400 user-reported developer-confirmed agent failures, we extract 47 Interaction Patterns and 128 Action types, generating 647 repository-grounded fuzzing cases. Executing the 647-case bundle once per evaluated configuration, ABTest flags 1,573 behavioral anomalies across the three coding agent families, of which 642 are manually confirmed as new true anomalies, achieving a detection precision of 40.8%. Our results demonstrate that ABTest effectively uncovers real-world failures, exposes robustness differences across models, and reveals previously unreported failure modes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_03362 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ABTest: Behavior-Driven Testing for AI Coding Agents Dai, Wuyang Openja, Moses Pham, Hung Viet Uddin, Gias Yang, Jinqiu Wang, Song Software Engineering AI coding agents are increasingly integrated into real-world software development workflows, yet their robustness under diverse and adversarial scenarios remains poorly understood. We present ABTest, a behavior-driven fuzzing framework that systematically tests coding agents by turning real-world failure reports into repository-grounded behavioral tests. ABTest (1) mines user-reported anomalies to derive reusable workflow patterns (Interaction Patterns) and behaviors (Action types); (2) composes them into stepwise fuzzing templates; (3) instantiates executable test cases in real repositories; (4) executes them with coding agents while recording traces and artifacts; and (5) detects and validates anomalous behaviors. We apply ABTest to three widely used coding agents: Claude Code, OpenAI Codex CLI, and Gemini CLI. From 400 user-reported developer-confirmed agent failures, we extract 47 Interaction Patterns and 128 Action types, generating 647 repository-grounded fuzzing cases. Executing the 647-case bundle once per evaluated configuration, ABTest flags 1,573 behavioral anomalies across the three coding agent families, of which 642 are manually confirmed as new true anomalies, achieving a detection precision of 40.8%. Our results demonstrate that ABTest effectively uncovers real-world failures, exposes robustness differences across models, and reveals previously unreported failure modes. |
| title | ABTest: Behavior-Driven Testing for AI Coding Agents |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2604.03362 |