220 citations · 1k across the 53 of their papers we have counts for
31 papers · 1 filter
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…
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…
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…
Vulnerability Mimicking Mutants
Aayush Garg, Renzo Degiovanni, Mike Papadakis +1
With the increasing release of powerful language models trained on large code corpus (e.g. CodeBERT was trained on 6.4 million programs), a new family of mutation testing tools has…
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…
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…