Most 'verified' commits on GitHub are automatically signed by the platform's web interface; genuine developer-managed commit signing is rare, inconsistent, and marked by unrevoked expired keys.
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9 Pith papers cite this work, alongside 349 external citations. Polarity classification is still indexing.
9
Pith papers citing it
349
external citations · OpenAlex
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cs.SE 9roles
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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.
Re-evaluating four LLM code-efficiency benchmarks with 30-run statistical testing shows 93.89% of 'performant' implementations are indistinguishable from baselines; a multi-agent test-generation framework reveals hidden significant improvements in ~24% of previously non-significant tasks.
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.
The submission's abstract claims a CPG-based ML smell detector with 88.14% recall, but the full text implements an AST-only DSL detector with 88.89% recall, and no CPG experiment appears.
citing papers explorer
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Analysis of Commit Signing on Github
Most 'verified' commits on GitHub are automatically signed by the platform's web interface; genuine developer-managed commit signing is rare, inconsistent, and marked by unrevoked expired keys.
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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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Rethinking Code Performance Benchmarks for LLMs
Re-evaluating four LLM code-efficiency benchmarks with 30-run statistical testing shows 93.89% of 'performant' implementations are indistinguishable from baselines; a multi-agent test-generation framework reveals hidden significant improvements in ~24% of previously non-significant tasks.
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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.
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SpecDetect4ML: Detecting Non-Local ML Code Smells with Code Property Graphs
The submission's abstract claims a CPG-based ML smell detector with 88.14% recall, but the full text implements an AST-only DSL detector with 88.89% recall, and no CPG experiment appears.