c-CRAB benchmark shows state-of-the-art code review agents solve only around 40% of tasks derived from human reviews, suggesting potential for human-AI collaboration.
Ai-powered code review with llms: Early results
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MetaLint uses meta-learning to let models generalize from easy synthetic linting data to hard human-curated best practices, yielding large F-score gains on a new PEP-inspired benchmark.
A literature survey that collects and categorizes 124 papers on LLM-based agents for software engineering from SE and agent perspectives.
Survey mapping LLM applications in software quality assurance to established standards including ISO/IEC 12207, ISO 25010, CMMI, and TMM, with case studies, challenges, and future directions.
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Code Review Agent Benchmark
c-CRAB benchmark shows state-of-the-art code review agents solve only around 40% of tasks derived from human reviews, suggesting potential for human-AI collaboration.
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MetaLint: Easy-to-Hard Generalization for Code Linting
MetaLint uses meta-learning to let models generalize from easy synthetic linting data to hard human-curated best practices, yielding large F-score gains on a new PEP-inspired benchmark.
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Large Language Model-Based Agents for Software Engineering: A Survey
A literature survey that collects and categorizes 124 papers on LLM-based agents for software engineering from SE and agent perspectives.
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A Blueprint for AI-Driven Software Quality: Integrating LLMs with Established Standards
Survey mapping LLM applications in software quality assurance to established standards including ISO/IEC 12207, ISO 25010, CMMI, and TMM, with case studies, challenges, and future directions.