ABTest: Behavior-Driven Testing for AI Coding Agents

Wuyang Dai; Moses Openja; Hung Viet Pham; Gias Uddin; Jinqiu Yang; Song Wang

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arXiv · 2604.03362

ABTest: Behavior-Driven Testing for AI Coding Agents

Wuyang Dai·Moses Openja·Hung Viet Pham·Gias Uddin·Jinqiu Yang·Song Wang

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Abstract

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.

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Wuyang Dai, Moses Openja, Hung Viet Pham, Gias Uddin, Jinqiu Yang, Song Wang. 2026-04-03. ABTest: Behavior-Driven Testing for AI Coding Agents. https://arxiv.org/abs/2604.03362

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