7 citations · 26 across the 15 of their papers we have counts for
16 papers
MIMIC-Py: An Extensible Tool for Personality-Driven Automated Game Testing with Large Language Models
Yifei Chen, Sarra Habchi, Lili Wei
Modern video games are complex, non-deterministic systems that are difficult to test automatically at scale. Although prior work shows that personality-driven Large Language Model…
A Unified Framework for Rethinking Policy Divergence Measures in GRPO
Qingyuan Wu, Yuhui Wang, Simon Sinong Zhan +6
Reinforcement Learning with Verified Reward (RLVR) has emerged as a critical paradigm for advancing the reasoning capabilities of Large Language Models (LLMs). Most existing RLVR m…
MIMIC: Integrating Diverse Personality Traits for Better Game Testing Using Large Language Model
Yifei Chen, Sarra Habchi, Lili Wei
Modern video games pose significant challenges for traditional automated testing algorithms, yet intensive testing is crucial to ensure game quality. To address these challenges, r…
Impact of LLM-based Review Comment Generation in Practice: A Mixed Open-/Closed-source User Study
Doriane Olewicki, Leuson Da Silva, Suhaib Mujahid +6
We conduct a large-scale empirical user study in a live setup to evaluate the acceptance of LLM-generated comments and their impact on the review process. This user study was perfo…
An Empirical Study on Code Review Activity Prediction and Its Impact in Practice
Doriane Olewicki, Sarra Habchi, Bram Adams
During code reviews, an essential step in software quality assurance, reviewers have the difficult task of understanding and evaluating code changes to validate their quality and p…
Time-based Repair for Asynchronous Wait Flaky Tests in Web Testing
Yu Pei, Jeongju Sohn, Sarra Habchi +1
Asynchronous waits are one of the most prevalent root causes of flaky tests and a major time-influential factor of web application testing. To investigate the characteristics of as…
On the Costs and Benefits of Adopting Lifelong Learning for Software Analytics -- Empirical Study on Brown Build and Risk Prediction
Doriane Olewicki, Sarra Habchi, Mathieu Nayrolles +3
Nowadays, software analytics tools using machine learning (ML) models to, for example, predict the risk of a code change are well established. However, as the goals of a project sh…
ImageNet-Hard: The Hardest Images Remaining from a Study of the Power of Zoom and Spatial Biases in Image Classification
Mohammad Reza Taesiri, Giang Nguyen, Sarra Habchi +2
Image classifiers are information-discarding machines, by design. Yet, how these models discard information remains mysterious. We hypothesize that one way for image classifiers 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…
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…
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…
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…
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…
On the Use of Mutation in Injecting Test Order-Dependency
Sarra Habchi, Maxime Cordy, Mike Papadakis +1
Background: Test flakiness is identified as a major issue that compromises the regression testing process of complex software systems. Flaky tests manifest non-deterministic behavi…
Android Code Smells: From Introduction to Refactoring
Sarra Habchi, Naouel Moha, Romain Rouvoy
Object-oriented code smells are well-known concepts in software engineering that refer to bad design and development practices commonly observed in software systems. With the emerg…