{"slug": "building-a-c-logical-bug-detection-benchmark-on-kaggle-testing-3-ai-models", "title": "Building a C++ Logical Bug Detection Benchmark on Kaggle: Testing 3 AI Models", "summary": "A developer built the C++ Logical Bug Detection Benchmark on Kaggle, a three-task evaluation testing whether AI models can identify common logical errors in C++ code and suggest fixes. Qwen 3 Coder 480B, GPT-5.4 mini, and Gemini 3.7 Flash each scored 100.00, passing all nine model-task evaluations, though the developer cautions the sample is too small to generalize to complex debugging or large codebases.", "body_md": "This is a submission for the [Kaggle Benchmarking Challenge](https://dev.to/challenges/kaggle-2026-09-23).\n\nAs a C++ learner, I wanted to explore how well AI models can identify common logical errors in code and suggest appropriate fixes.\n\nFor the DEV × Kaggle Benchmarking Challenge, I created a small benchmark on Kaggle called **C++ Logical Bug Detection Benchmark**.\n\nThe goal is to test whether AI models can recognize common programming mistakes, explain why they occur, and suggest corrections.\n\nThe benchmark contains three tasks:\n\nThis task checks whether a model can identify incorrect variable usage in a summation loop and suggest the correct fix.\n\nThis task evaluates whether a model can identify incorrect loop boundaries and indexing that may cause an off-by-one error.\n\nThis task checks whether a model can detect incorrect array index usage and explain how to correct it.\n\nI tested three AI models:\n\nI selected these models to compare how different AI systems handle simple C++ logical bug-detection tasks.\n\nAll three models achieved a score of **100.00** on the benchmark.\n\n| Model | Score | \n|---|---|\n| Qwen 3 Coder 480B | 100.00 | \n| GPT-5.4 mini | 100.00 | \n| Gemini 3.7 Flash | 100.00 | \n\nAll three models passed the three tasks, giving a total of **9 successful model-task evaluations out of 9**.\n\nThe results show that all three models handled the three simple bug-detection tasks successfully.\n\nHowever, this is a small benchmark with only three tasks. It does not establish how well these models perform on complex C++ debugging, large codebases, or real-world software projects.\n\nIn the future, I would like to expand the benchmark with more challenging logical errors, nested loops, pointer-related bugs, and edge cases.\n\nBuilding this benchmark helped me understand how AI models can be evaluated using specific programming tasks and measurable results.\n\nIt also encouraged me to think about how to design better tests that go beyond simple examples.\n\nYou can explore my benchmark here:\n\n[C++ Logical Bug Detection Benchmark on Kaggle](https://www.kaggle.com/benchmarks/mahenderprataps/cpp-logical-bug-detection-benchmark)\n\nI would appreciate feedback and suggestions for adding more challenging C++ debugging tasks.", "url": "https://wpnews.pro/news/building-a-c-logical-bug-detection-benchmark-on-kaggle-testing-3-ai-models", "canonical_source": "https://dev.to/mahenderpratap/building-a-c-logical-bug-detection-benchmark-on-kaggle-testing-3-ai-models-1nfn", "published_at": "2026-09-28 14:34:05+00:00", "updated_at": "2026-09-28 14:51:17.341933+00:00", "lang": "en", "topics": ["ai-research", "large-language-models", "ai-tools", "developer-tools"], "entities": ["Kaggle", "Qwen 3 Coder 480B", "GPT-5.4 mini", "Gemini 3.7 Flash", "DEV"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/building-a-c-logical-bug-detection-benchmark-on-kaggle-testing-3-ai-models", "markdown": "https://wpnews.pro/news/building-a-c-logical-bug-detection-benchmark-on-kaggle-testing-3-ai-models.md", "text": "https://wpnews.pro/news/building-a-c-logical-bug-detection-benchmark-on-kaggle-testing-3-ai-models.txt", "jsonld": "https://wpnews.pro/news/building-a-c-logical-bug-detection-benchmark-on-kaggle-testing-3-ai-models.jsonld"}}