24 citations · 115 across the 57 of their papers we have counts for
29 papers · 1 filter
Robustness and Trade-offs for Code LLMs on Protected Code
Jin Wen, Yuejun Guo, Yujie Ma +2
Code large language models (LLMs) are increasingly used on software artifacts that may be intentionally obfuscated for intellectual-property protection, reverse-engineering resista…
Learning Generalizable Multimodal Representations for Software Vulnerability Detection
Zeming Dong, Yuejun Guo, Qiang Hu +5
Source code and its accompanying comments are complementary yet naturally aligned modalities-code encodes structural logic while comments capture developer intent. However, existin…
When Prompt Under-Specification Improves Code Correctness: An Exploratory Study of Prompt Wording and Structure Effects on LLM-Based Code Generation
Amal AKLI, Mike PAPADAKIS, Maxime CORDY +1
Large language models are increasingly used for code generation, yet the correctness of their outputs depends not only on model capability but also on how tasks are specified. Prio…
Defective Task Descriptions in LLM-Based Code Generation: Detection and Analysis
Amal Akli, Mike Papadakis, Maxime Cordy +1
Large language models are widely used for code generation, yet they rely on an implicit assumption that the task descriptions are sufficiently detailed and well-formed. However, in…
One Model, Many Skills: Parameter-Efficient Fine-Tuning for Multitask Code Analysis
Amal Akli, Maxime Cordy, Mike Papadakis +1
Large language models have recently surpassed specialized systems on code generation, yet their effectiveness on other code-analysis tasks remains less clear. At the same time, mul…
When Prompts Go Wrong: Evaluating Code Model Robustness to Ambiguous, Contradictory, and Incomplete Task Descriptions
Maya Larbi, Amal Akli, Mike Papadakis +4
Large Language Models (LLMs) have demonstrated impressive performance in code generation tasks under idealized conditions, where task descriptions are clear and precise. However, i…
Foundation Models for Autonomous Driving System: An Initial Roadmap
Xiongfei Wu, Mingfei Cheng, Xiaoning Ren +8
Recent advances in foundation models (FMs), including large language models (LLMs), vision-language models (VLMs), and world models, have opened new opportunities for autonomous dr…
Evaluation and Improvement of Fault Detection for Large Language Models
Qiang Hu, Jin Wen, Maxime Cordy +4
Large language models (LLMs) have recently achieved significant success across various application domains, garnering substantial attention from different communities. Unfortunatel…
GenCode: A Generic Data Augmentation Framework for Boosting Deep Learning-Based Code Understanding
Zeming Dong, Qiang Hu, Xiaofei Xie +4
Pre-trained code models lead the era of code intelligence, with multiple models designed with impressive performance. However, one important problem, data augmentation for code dat…
Hazards in Deep Learning Testing: Prevalence, Impact and Recommendations
Salah Ghamizi, Maxime Cordy, Yuejun Guo +2
Much research on Machine Learning testing relies on empirical studies that evaluate and show their potential. However, in this context empirical results are sensitive to a number o…
Active Code Learning: Benchmarking Sample-Efficient Training of Code Models
Qiang Hu, Yuejun Guo, Xiaofei Xie +4
The costly human effort required to prepare the training data of machine learning (ML) models hinders their practical development and usage in software engineering (ML4Code), espec…
ACoRe: Automated Goal-Conflict Resolution
Luiz Carvalho, Renzo Degiovanni, Matìas Brizzio +4
System goals are the statements that, in the context of software requirements specification, capture how the software should behave. Many times, the understanding of stakeholders o…
Boosting Source Code Learning with Text-Oriented Data Augmentation: An Empirical Study
Zeming Dong, Qiang Hu, Yuejun Guo +5
Recent studies have demonstrated remarkable advancements in source code learning, which applies deep neural networks (DNNs) to tackle various software engineering tasks. Similar to…
The Importance of Discerning Flaky from Fault-triggering Test Failures: A Case Study on the Chromium CI
Guillaume Haben, Sarra Habchi, Mike Papadakis +2
Flaky tests are tests that pass and fail on different executions of the same version of a program under test. They waste valuable developer time by making developers investigate fa…
Assertion Inferring Mutants
Aayush Garg, Renzo Degiovanni, Facundo Molina +4
Specification inference techniques aim at (automatically) inferring a set of assertions that capture the exhibited software behaviour by generating and filtering assertions through…
MIXCODE: Enhancing Code Classification by Mixup-Based Data Augmentation
Zeming Dong, Qiang Hu, Yuejun Guo +5
Inspired by the great success of Deep Neural Networks (DNNs) in natural language processing (NLP), DNNs have been increasingly applied in source code analysis and attracted signifi…
Learning from what we know: How to perform vulnerability prediction using noisy historical data
Aayush Garg, Renzo Degiovanni, Matthieu Jimenez +3
Vulnerability prediction refers to the problem of identifying system components that are most likely to be vulnerable. Typically, this problem is tackled by training binary classif…
What Made This Test Flake? Pinpointing Classes Responsible for Test Flakiness
Sarra Habchi, Guillaume Haben, Jeongju Sohn +4
Flaky tests are defined as tests that manifest non-deterministic behaviour by passing and failing intermittently for the same version of the code. These tests cripple continuous in…
Aries: Efficient Testing of Deep Neural Networks via Labeling-Free Accuracy Estimation
Qiang Hu, Yuejun Guo, Xiaofei Xie +4
Deep learning (DL) plays a more and more important role in our daily life due to its competitive performance in industrial application domains. As the core of DL-enabled systems, d…
CodeS: Towards Code Model Generalization Under Distribution Shift
Qiang Hu, Yuejun Guo, Xiaofei Xie +4
Distribution shift has been a longstanding challenge for the reliable deployment of deep learning (DL) models due to unexpected accuracy degradation. Although DL has been becoming…
Software Fairness: An Analysis and Survey
Ezekiel Soremekun, Mike Papadakis, Maxime Cordy +1
In the last decade, researchers have studied fairness as a software property. In particular, how to engineer fair software systems? This includes specifying, designing, and validat…
GraphCode2Vec: Generic Code Embedding via Lexical and Program Dependence Analyses
Wei Ma, Mengjie Zhao, Ezekiel Soremekun +6
Code embedding is a keystone in the application of machine learning on several Software Engineering (SE) tasks. To effectively support a plethora of SE tasks, the embedding needs t…
A Qualitative Study on the Sources, Impacts, and Mitigation Strategies of Flaky Tests
Sarra Habchi, Guillaume Haben, Mike Papadakis +2
Test flakiness forms a major testing concern. Flaky tests manifest non-deterministic outcomes that cripple continuous integration and lead developers to investigate false alerts. I…
Discerning Legitimate Failures From False Alerts: A Study of Chromium's Continuous Integration
Guillaume Haben, Sarra Habchi, Mike Papadakis +2
Flakiness is a major concern in Software testing. Flaky tests pass and fail for the same version of a program and mislead developers who spend time and resources investigating test…