16 papers
From Rookie to Expert: Manipulating LLMs for Automated Vulnerability Exploitation in Enterprise Software
Moustapha Awwalou Diouf, Maimouna Tamah Diao, Iyiola Emmanuel Olatunji +6
LLMs democratize software engineering by enabling non-programmers to create applications, but this same accessibility fundamentally undermines security assumptions that have guided…
Learned or Memorized ? Quantifying Memorization Advantage in Code LLMs
Djiré Albérick Euraste, Kaboré Abdoul Kader, Jordan Samhi +3
The lack of transparency about code datasets used to train large language models (LLMs) makes it difficult to detect, evaluate, and mitigate data leakage. We present a perturbation…
Large-scale, Independent and Comprehensive study of the power of LLMs for test case generation
Wendkûuni C. Ouédraogo, Kader Kaboré, Yinghua Li +5
Unit testing is essential for software reliability, yet manual test creation is time-consuming and often neglected. Search-based software testing improves efficiency but produces t…
Programming Language Confusion: When Code LLMs Can't Keep their Languages Straight
Micheline Bénédicte Moumoula, Serge Lionel Nikiema, Abdoul Kader Kabore +2
Large Language Models (LLMs) have achieved state-of-the-art performance across software engineering tasks, from code generation to translation. However, we identify and systematica…
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…
Dynamic Stability of LLM-Generated Code
Prateek Rajput, Abdoul Aziz Bonkoungou, Yewei Song +4
Current evaluations of LLMs for code generation emphasize functional correctness, overlooking the fact that functionally correct solutions can differ significantly in algorithmic c…