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SWE-CI: Evaluating Agent Capabilities in Maintaining Codebases via CI
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Overview
Large language model-powered agents can automate software engineering tasks, including static bug fixing, as shown by benchmarks like SWE-bench. Real-world software development requires navigating complex requirements beyond these capabilities.
Summary from arxiv.org.
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Hacker News · arxiv.org
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Based on 5 sampled comments from this thread. Analyzed 2026-03-08.
- Praise: The benchmark of 100 tasks over 233 days provides a comprehensive evaluation of agent capabilities, which is crucial for understanding real-world performance.
- Praise: The potential to cross-reference AI-generated code with existing GitHub issues for training and validation could enhance model accuracy and relevance in practical applications.
- Concern: Benchmarking Opus 4.6 against GPT-5.2, which is three generations behind, raises concerns about the fairness and relevance of the comparison.
- Concern: Evaluating maintainability based on only 500 lines of code changes does not adequately reflect long-term maintainability, which is essential for practical software development.