187 citations · 278 across the 29 of their papers we have counts for
22 papers · 1 filter
From Failing to Passing: Evolving Natural Language Prompt Optimization Rules for LLM Code Generation
Amal Akli, Melissa Akli, Cedric Richter +2
Large language models are known to be sensitive to prompt formulation. Even minor variations in wording can substantially degrade performance. This sensitivity reveals an opportuni…
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…
CodeLens: An Interactive Tool for Visualizing Code Representations
Yuejun Guo, Seifeddine Bettaieb, Qiang Hu +2
Representing source code in a generic input format is crucial to automate software engineering tasks, e.g., applying machine learning algorithms to extract information. Visualizing…
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…
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…
Efficient Mutation Testing via Pre-Trained Language Models
Ahmed Khanfir, Renzo Degiovanni, Mike Papadakis +1
Mutation testing is an established fault-based testing technique. It operates by seeding faults into the programs under test and asking developers to write tests that reveal these…
Predicting Flaky Tests Categories using Few-Shot Learning
Amal Akli, Guillaume Haben, Sarra Habchi +2
Flaky tests are tests that yield different outcomes when run on the same version of a program. This non-deterministic behaviour plagues continuous integration with false signals, w…
CodeBERT-nt: code naturalness via CodeBERT
Ahmed Khanfir, Matthieu Jimenez, Mike Papadakis +1
Much of software-engineering research relies on the naturalness of code, the fact that code, in small code snippets, is repetitive and can be predicted using statistical language m…
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…
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…
Mutation Testing in Evolving Systems: Studying the relevance of mutants to code evolution
Milos Ojdanic, Ezekiel Soremekun, Renzo Degiovanni +2
When software evolves, opportunities for introducing faults appear. Therefore, it is important to test the evolved program behaviors during each evolution cycle. We conduct an expl…
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…
Smells in System User Interactive Tests
Renaud Rwemalika, Sarra Habchi, Mike Papadakis +2
Test smells are known as bad development practices that reflect poor design and implementation choices in software tests. Over the last decade, test smells were heavily studied to…
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…
FixMiner: Mining Relevant Fix Patterns for Automated Program Repair
Anil Koyuncu, Kui Liu, Tegawendé F. Bissyandé +4
Patching is a common activity in software development. It is generally performed on a source code base to address bugs or add new functionalities. In this context, given the recurr…
Selecting Fault Revealing Mutants
Thierry Titcheu Chekam, Mike Papadakis, Tegawendé Bissyandé +2
Mutant selection refers to the problem of choosing, among a large number of mutants, the (few) ones that should be used by the testers. In view of this, we investigate the problem…
Mining Fix Patterns for FindBugs Violations
Kui Liu, Dongsun Kim, Tegawendé F. Bissyandé +2
In this paper, we first collect and track a large number of fixed and unfixed violations across revisions of software. The empirical analyses reveal that there are discrepancies in…
Weaving Rules into [email protected] for Embedded Smart Systems
Ludovic Mouline, Thomas Hartmann, François Fouquet +3
Smart systems are characterised by their ability to analyse measured data in live and to react to changes according to expert rules. Therefore, such systems exploit appropriate dat…
Assessing and Comparing Mutation-based Fault Localization Techniques
Thierry Titcheu Chekam, Mike Papadakis, Yves Le Traon
Recent research demonstrated that mutation-based fault localization techniques are relatively accurate and practical. However, these methods have never been compared and have only…
Assessing and Improving the Mutation Testing Practice of PIT
Thomas Laurent, Anthony Ventresque, Mike Papadakis +2
Mutation testing is used extensively to support the experimentation of software engineering studies. Its application to real-world projects is possible thanks to modern tools that…
Leveraging Time Distortion for seamless Navigation into Data Space-Time Continuum
Thomas Hartmann, Francois Fouquet, Yves Le Traon +1
Intelligent software systems continuously analyze their surrounding environment and accordingly adapt their internal state. Depending on the criticality index of the situation, the…
Artificial Mutation inspired Hyper-heuristic for Runtime Usage of Multi-objective Algorithms
Donia El Kateb, François Fouquet, Johann Bourcier +1
In the last years, multi-objective evolutionary algorithms (MOEA) have been applied to different software engineering problems where many conflicting objectives have to be optimize…