Three AI Models Score Perfectly on Small C++ Bug Detection Benchmark
A C++ learner created a three-task benchmark on Kaggle to evaluate how well AI models detect common logical errors in code, as part of the DEV × Kaggle Benchmarking Challenge. The benchmark tested Qwen 3 Coder 480B, GPT-5.4 mini, and Gemini 3.7 Flash on tasks involving incorrect variable usage, off-by-one errors, and wrong array indexing. All three models achieved a perfect score of 100, successfully completing all nine model-task evaluations. The author acknowledges the benchmark is limited in scope and does not reflect model performance on complex codebases or real-world debugging scenarios. Future iterations are planned to include harder challenges such as pointer bugs, nested loops, and edge cases.
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