6 citations · 24 across the 22 of their papers we have counts for
24 papers
Trajectory-Level Security Debt in LLM Coding Agents
Prateek Kumar Rajput, Abdoul Kader Kabore, Yewei Song +4
LLM coding agents can traverse hundreds of intermediate code states before submitting a solution. Evaluating only the final artifact leaves the evolution of security findings unmea…
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…
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…
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…
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…
Multi-Agent Strategies for Bridging Programming Language Gaps in Code Generation
Micheline Bénédicte Moumoula, Serge Lionel Nikiema, Albérick Euraste Djire +3
Real-world software systems are inherently multilingual, but current large language models are not equally consistent across programming languages. This mismatch limits code genera…
Security Evaluation of Android apps in budget African Mobile Devices
Alioune Diallo, Anta Diop, Abdoul Kader Kabore +4
Android's open-source nature facilitates widespread smartphone accessibility, particularly in price-sensitive markets. System and vendor applications that come pre-installed on bud…
Detection of security smells in IaC scripts through semantics-aware code and language processing
Aicha War, Adnan A. Rawass, Abdoul K. Kabore +3
Infrastructure as Code (IaC) automates the provisioning and management of IT infrastructure through scripts and tools, streamlining software deployment. Prior studies have shown th…
How Small Transformation Expose the Weakness of Semantic Similarity Measures
Serge Lionel Nikiema, Albérick Euraste Djire, Abdoul Aziz Bonkoungou +5
This research examines how well different methods measure semantic similarity, which is important for various software engineering applications such as code search, API recommendat…
Using Contrastive Learning to Improve Two-Way Reasoning in Large Language Models: The Obfuscation Task as a Case Study
Serge Lionel Nikiema, Jordan Samhi, Micheline Bénédicte Moumoula +4
This research addresses a fundamental question in AI: whether large language models truly understand concepts or simply recognize patterns. The authors propose bidirectional reason…
Memorization or Interpolation ? Detecting LLM Memorization through Input Perturbation Analysis
Albérick Euraste Djiré, Abdoul Kader Kaboré, Earl T. Barr +2
While Large Language Models (LLMs) achieve remarkable performance through training on massive datasets, they can exhibit concerning behaviors such as verbatim reproduction of train…
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…
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…
On the Diffusion of Test Smells in LLM-Generated Unit Tests
Wendkûuni C. Ouédraogo, Yinghua Li, Xueqi Dang +5
LLMs promise to transform unit test generation from a manual burden into an automated solution. Yet, beyond metrics such as compilability or coverage, little is known about the qua…
The Struggles of LLMs in Cross-lingual Code Clone Detection
Micheline Bénédicte Moumoula, Abdoul Kader Kabore, Jacques Klein +1
With the involvement of multiple programming languages in modern software development, cross-lingual code clone detection has gained traction within the software engineering commun…
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…
Learning to Represent Patches
Xunzhu Tang, Haoye Tian, Zhenghan Chen +6
Patch representation is crucial in automating various software engineering tasks, like determining patch accuracy or summarizing code changes. While recent research has employed de…
Negative Results of Fusing Code and Documentation for Learning to Accurately Identify Sensitive Source and Sink Methods An Application to the Android Framework for Data Leak Detection
Jordan Samhi, Maria Kober, Abdoul Kader Kabore +3
Apps on mobile phones manipulate all sorts of data, including sensitive data, leading to privacy-related concerns. Recent regulations like the European GDPR provide rules for the p…
The Best of Both Worlds: Combining Learned Embeddings with Engineered Features for Accurate Prediction of Correct Patches
Haoye Tian, Kui Liu, Yinghua Li +7
A large body of the literature on automated program repair develops approaches where patches are automatically generated to be validated against an oracle (e.g., a test suite). Bec…
Early Detection of Security-Relevant Bug Reports using Machine Learning: How Far Are We?
Arthur D. Sawadogo, Quentin Guimard, Tegawendé F. Bissyandé +3
Bug reports are common artefacts in software development. They serve as the main channel for users to communicate to developers information about the issues that they encounter whe…
DexRay: A Simple, yet Effective Deep Learning Approach to Android Malware Detection based on Image Representation of Bytecode
Nadia Daoudi, Jordan Samhi, Abdoul Kader Kabore +3
Computer vision has witnessed several advances in recent years, with unprecedented performance provided by deep representation learning research. Image formats thus appear attracti…
Predicting Patch Correctness Based on the Similarity of Failing Test Cases
Haoye Tian, Yinghua Li, Weiguo Pian +5
Towards predicting patch correctness in APR, we propose a simple, but novel hypothesis on how the link between the patch behaviour and failing test specifications can be drawn: sim…
Evaluating Representation Learning of Code Changes for Predicting Patch Correctness in Program Repair
Haoye Tian, Kui Liu, Abdoul Kader Kaboreé +4
A large body of the literature of automated program repair develops approaches where patches are generated to be validated against an oracle (e.g., a test suite). Because such an o…
What You See is What it Means! Semantic Representation Learning of Code based on Visualization and Transfer Learning
Patrick Keller, Laura Plein, Tegawendé F. Bissyandé +2
Recent successes in training word embeddings for NLP tasks have encouraged a wave of research on representation learning for source code, which builds on similar NLP methods. The o…