Ecosystem-scale measurement shows commit signing on GitHub is rarely deliberate or sustained by developers, with rising lapse rates and unrevoked expired keys, so supply-chain security frameworks relying on it do not hold in practice.
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A survey of 457 SE researchers finds widespread GenAI use concentrated in writing and ideation, with productivity gains but persistent concerns over accuracy, bias, and the need for clearer governance rules.
LLMs propose volatile performance improvements on real-world Java tasks that lag human developers on average, showing algorithmic benchmarks overestimate capabilities.
SpecDetect4ML detects 22 ML code smells via DSL specifications and CPG-based analysis, reporting 95.82% precision and 88.14% recall on 890 ML systems while outperforming prior tools.
Transportability methods can transport causal effects from experimental samples to broader target populations in software engineering by leveraging observational data to improve external validity.
A composable DSL for describing sampling workflows on code repositories enables explicit specification and statistical reasoning about the generalizability of empirical software engineering findings.
Longitudinal surveys show AI coding assistants reduce time on code writing but increase supervisory verification tasks, with stable productivity perceptions yet rising reports of worsened developer experience.
A multi-case study plus survey produces seven actionable recommendations for efficient and responsible LLM use in industrial software engineering.
citing papers explorer
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Analysis of Commit Signing on Github
Ecosystem-scale measurement shows commit signing on GitHub is rarely deliberate or sustained by developers, with rising lapse rates and unrevoked expired keys, so supply-chain security frameworks relying on it do not hold in practice.
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Taking a Pulse on How Generative AI is Reshaping the Software Engineering Research Landscape
A survey of 457 SE researchers finds widespread GenAI use concentrated in writing and ideation, with productivity gains but persistent concerns over accuracy, bias, and the need for clearer governance rules.
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Do AI Models Dream of Faster Code? An Empirical Study on LLM-Proposed Performance Improvements in Real-World Software
LLMs propose volatile performance improvements on real-world Java tasks that lag human developers on average, showing algorithmic benchmarks overestimate capabilities.
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ML Code Smells: From Specification to Detection
SpecDetect4ML detects 22 ML code smells via DSL specifications and CPG-based analysis, reporting 95.82% precision and 88.14% recall on 890 ML systems while outperforming prior tools.
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Towards Improving the External Validity of Software Engineering Experiments with Transportability Methods
Transportability methods can transport causal effects from experimental samples to broader target populations in software engineering by leveraging observational data to improve external validity.
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Modeling Sampling Workflows for Code Repositories
A composable DSL for describing sampling workflows on code repositories enables explicit specification and statistical reasoning about the generalizability of empirical software engineering findings.
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The Impact of AI Coding Assistants on Software Engineering: A Longitudinal Study
Longitudinal surveys show AI coding assistants reduce time on code writing but increase supervisory verification tasks, with stable productivity perceptions yet rising reports of worsened developer experience.
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Recommendations for Efficient and Responsible LLM Adoption within Industrial Software Development
A multi-case study plus survey produces seven actionable recommendations for efficient and responsible LLM use in industrial software engineering.