8 papers · 1 filter
Correctness isnt Efficiency: Runtime Memory Divergence in LLM-Generated Code
Prateek Rajput, Yewei Song, Abdoul Aziz Bonkoungou +4
Large language models (LLMs) can generate programs that pass unit tests, but passing tests does not guarantee reliable runtime behavior. We find that different correct solutions to…
Characterizing Build Compromises Through Vulnerability Disclosure Analysis
Maimouna Tamah Diao, Moustapha Awwalou Diouf, Iyiola Emmanuel Olatunji +4
The software build process transforms source code into deployable artifacts, representing a critical yet vulnerable stage in software development. Build infrastructure security pos…
Human-Aligned Code Readability Assessment with Large Language Models
Wendkûuni C. Ouédraogo, Yinghua Li, Xueqi Dang +6
Code readability is crucial for software comprehension and maintenance, yet difficult to assess at scale. Traditional static metrics often fail to capture the subjective, context-s…
Boosting Open-Source LLMs for Program Repair via Reasoning Transfer and LLM-Guided Reinforcement Learning
Xunzhu Tang, Jacques Klein, Tegawendé F. Bissyandé
Several closed-source LLMs have consistently outperformed open-source alternatives in program repair tasks, primarily due to their superior reasoning capabilities and extensive pre…
Beyond Surface Similarity: Evaluating LLM-Based Test Refactorings with Structural and Semantic Awareness
Wendkûuni C. Ouédraogo, Yinghua Li, Xueqi Dang +5
Large Language Models (LLMs) are increasingly used to refactor unit tests, improving readability and structure while preserving behavior. Evaluating such refactorings, however, rem…
Rethinking Cognitive Complexity for Unit Tests: Toward a Readability-Aware Metric Grounded in Developer Perception
Wendkûuni C. Ouédraogo, Yinghua Li, Xueqi Dang +5
Automatically generated unit tests-from search-based tools like EvoSuite or LLMs-vary significantly in structure and readability. Yet most evaluations rely on metrics like Cyclomat…