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Python Prototype Explores Cost-Aware Bug Investigation Policies for AI Coding

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A developer has released v0.1.0 of 'bug-cause-inference-game,' a small Python prototype designed to explore cost-aware bug investigation in AI-assisted coding workflows. The tool works on synthetic bug cases, updating probability estimates across five cause categories and selecting the next investigation action from a defined set of eight options based on cost and information value. Rather than acting as a production debugger or automated repair tool, the prototype benchmarks policies — including random, greedy, and information-gain-per-cost approaches — to evaluate how efficiently a system can identify likely bug causes under budget constraints. The project draws from probabilistic debugging and Bayesian fault diagnosis, aiming to help developers decide what evidence to collect next rather than generating confident but potentially misleading explanations. The authors explicitly note the tool's limitations, stating it is not a fault-localization engine, LLM benchmark, or formal game-theoretic debugger, with that boundary being a deliberate design choice.

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