EnCoDe enables design-time prediction of block-level energy consumption in Python code via static features and ML models trained on a dataset from 18,000 programs, achieving R²=0.75 and 80.6% hotspot classification accuracy.
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Developers using AI assistants exhibit more stable emotions and greater focus on code creation, evaluation, and verification, captured in a new four-dimensional S-IASE model from retrospective labeling of screen recordings, surveys, and interviews.
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.
Quasi-experiment finds modest impostor phenomenon reductions from coaching in early-career software engineers, yet similar gains in controls indicate contextual and temporal factors likely dominate.
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EnCoDe: Energy Estimation of Source Code At Design-Time
EnCoDe enables design-time prediction of block-level energy consumption in Python code via static features and ML models trained on a dataset from 18,000 programs, achieving R²=0.75 and 80.6% hotspot classification accuracy.
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How Do Developers Interact with AI? An Exploratory Study on Modeling Developer Programming Behavior
Developers using AI assistants exhibit more stable emotions and greater focus on code creation, evaluation, and verification, captured in a new four-dimensional S-IASE model from retrospective labeling of screen recordings, surveys, and interviews.
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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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A Quasi-Experimental Evaluation of Coaching to Mitigate the Impostor Phenomenon in Early-Career Software Engineers
Quasi-experiment finds modest impostor phenomenon reductions from coaching in early-career software engineers, yet similar gains in controls indicate contextual and temporal factors likely dominate.