activity
20202026
most citedEvaluating Representation Learning of Code Changes for Predicting Patch Correctness in Program Repair

6 citations · 10 across the 15 of their papers we have counts for

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11 papers · 1 filter

cs.SE2026

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…

cs.SE2025

From Rookie to Pro: Social Engineering 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…

cs.SE2025

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…

cs.SE2025

Beyond Language Barriers: Multi-Agent Coordination for Multi-Language Code Generation

Micheline Bénédicte Moumoula, Serge Lionel Nikiema, Albérick Euraste Djire +3

Producing high-quality code across multiple programming languages is increasingly important as today's software systems are built on heterogeneous stacks. Large language models (LL…

cs.SE20252 cited

The Code Barrier: What LLMs Actually Understand?

Serge Lionel Nikiema, Jordan Samhi, Abdoul Kader Kaboré +2

Understanding code represents a core ability needed for automating software development tasks. While foundation models like LLMs show impressive results across many software engine…

cs.SE2025

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…