15 papers · 1 filter
Evaluating Inference-Time Defenses Against Package Hallucination in LLM-Generated Code
Alberick Euraste Djire, Iyiola E. Olatunji, Melissa Tessa +3
LLMs are increasingly used for code generation, yet they frequently hallucinate non-existent software packages, creating exploitable entry points into the software supply chain. We…
Semantic Drift in Bug Resolution: How Behavioral Signals Propagate from Reports to Tests and Patches
Wendkûuni C. Ouédraogo, Yinghua Li, Xueqi Dang +7
Desc2Fix is a framework for measuring semantic alignment between bug reports, triggering tests, and developer-written fixes. Alignment is operationalized through structured behavio…
Can LLMs Test Terminal User Interfaces?
Chao Peng, Ruida Hu, Ajitha Rajan +3
Terminal User Interfaces (TUIs) combine the stateful, screen-oriented behaviour of GUIs with terminal deployment and are now common in developer tools. Yet they lack a dedicated te…
Humanizing Automatically Generated Unit Test Suites with LLM-Based Refactoring
Wendkûuni C. Ouédraogo, Yinghua Li, Xueqi Dang +7
Search-based test generation tools such as EvoSuite produce compilable and high-coverage unit tests at scale, but their suites are often hard to read and maintain. LLMs can generat…
Empirical Study for Structured Output Control in LLMs for Software Engineering
Yewei Song, Prateek Rajput, Tiezhu Sun +3
LLM-generated outputs in software engineering rarely exist in isolation. They must plug into toolchains, APIs, and data pipelines that impose strict, often organization-specific st…
GapFuzz: Cross-Plane Divergence Fuzzing for Distributed SDN Controllers
Moustapha Awwalou Diouf, Samuel Ouya, Jacques Klein +1
Distributed Software-Defined Networking (SDN) clusters replicate flow state asynchronously between a master node and its backups, leaving a window during which two backup nodes can…
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…
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…
DetectBERT: Towards Full App-Level Representation Learning to Detect Android Malware
Tiezhu Sun, Nadia Daoudi, Kisub Kim +3
Recent advancements in ML and DL have significantly improved Android malware detection, yet many methodologies still rely on basic static analysis, bytecode, or function call graph…