As large language models (LLMs) find their way into software development workflows, the need for rigorous benchmarks to evaluate their coding capabilities has grown rapidly. Today, software engineering benchmarks go far beyond simple code generation. They test how well a model can comprehend large codebases, fix real-world bugs, interpret vague requirements, and simulate tool-assisted development. These benchmarks aim to answer a central question: can LLMs behave like reliable engineering collaborators' One of the most important and challenging benchmarks in this space is SWE-bench. Built from real GitHub issues and corresponding pull requests, SWE-bench tasks models with generating code changes that resolve bugs and pass unit tests. It demands a deep understanding of software context, often across multiple files and long token sequences. SWE-bench stands out because it reflects how engineers actually work: reading reports, understanding dependencies, and producing minimal, testable...
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