172 citations · 802 across the 171 of their papers we have counts for
28 papers · 1 filter
Continuously Learning Bug Locations
Paulina Stevia Nouwou Mindom, Leuson Da Silva, Amin Nikanjam +1
Automatically locating buggy changesets associated with bug reports is crucial in the software development process. Deep Learning (DL)-based techniques show promising results by le…
Leveraging Data Characteristics for Bug Localization in Deep Learning Programs
Ruchira Manke, Mohammad Wardat, Foutse Khomh +1
Deep Learning (DL) is a class of machine learning algorithms that are used in a wide variety of applications. Like any software system, DL programs can have bugs. To support bug lo…
An Efficient Model Maintenance Approach for MLOps
Forough Majidi, Foutse Khomh, Heng Li +1
In recent years, many industries have utilized machine learning (ML) models in their systems. Ideally, ML models should be trained on and applied to data from the same distribution…
Tracing Optimization for Performance Modeling and Regression Detection
Kaveh Shahedi, Heng Li, Maxime Lamothe +1
Software performance modeling plays a crucial role in developing and maintaining software systems. A performance model analytically describes the relationship between the performan…
Fault Localization in Deep Learning-based Software: A System-level Approach
Mohammad Mehdi Morovati, Amin Nikanjam, Foutse Khomh
Over the past decade, Deep Learning (DL) has become an integral part of our daily lives. This surge in DL usage has heightened the need for developing reliable DL software systems.…
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…
In-Simulation Testing of Deep Learning Vision Models in Autonomous Robotic Manipulators
Dmytro Humeniuk, Houssem Ben Braiek, Thomas Reid +1
Testing autonomous robotic manipulators is challenging due to the complex software interactions between vision and control components. A crucial element of modern robotic manipulat…
Towards Optimizing SQL Generation via LLM Routing
Mohammadhossein Malekpour, Nour Shaheen, Foutse Khomh +1
Text-to-SQL enables users to interact with databases through natural language, simplifying access to structured data. Although highly capable large language models (LLMs) achieve s…
Towards Understanding the Impact of Data Bugs on Deep Learning Models in Software Engineering
Mehil B Shah, Mohammad Masudur Rahman, Foutse Khomh
Deep learning (DL) techniques have achieved significant success in various software engineering tasks (e.g., code completion by Copilot). However, DL systems are prone to bugs from…
Toward Debugging Deep Reinforcement Learning Programs with RLExplorer
Rached Bouchoucha, Ahmed Haj Yahmed, Darshan Patil +4
Deep reinforcement learning (DRL) has shown success in diverse domains such as robotics, computer games, and recommendation systems. However, like any other software system, DRL-ba…
Understanding Web Application Workloads and Their Applications: Systematic Literature Review and Characterization
Roozbeh Aghili, Qiaolin Qin, Heng Li +1
Web applications, accessible via web browsers over the Internet, facilitate complex functionalities without local software installation. In the context of web applications, a workl…
Trimming the Risk: Towards Reliable Continuous Training for Deep Learning Inspection Systems
Altaf Allah Abbassi, Houssem Ben Braiek, Foutse Khomh +1
The industry increasingly relies on deep learning (DL) technology for manufacturing inspections, which are challenging to automate with rule-based machine vision algorithms. DL-pow…
What Information Contributes to Log-based Anomaly Detection? Insights from a Configurable Transformer-Based Approach
Xingfang Wu, Heng Li, Foutse Khomh
Log data are generated from logging statements in the source code, providing insights into the execution processes of software applications and systems. State-of-the-art log-based…
Protecting Privacy in Software Logs: What Should Be Anonymized?
Roozbeh Aghili, Heng Li, Foutse Khomh
Software logs, generated during the runtime of software systems, are essential for various development and analysis activities, such as anomaly detection and failure diagnosis. How…
DeepCodeProbe: Towards Understanding What Models Trained on Code Learn
Vahid Majdinasab, Amin Nikanjam, Foutse Khomh
Machine learning models trained on code and related artifacts offer valuable support for software maintenance but suffer from interpretability issues due to their complex internal…
TaskEval: Assessing Difficulty of Code Generation Tasks for Large Language Models
Florian Tambon, Amin Nikanjam, Cyrine Zid +2
Large Language Models (LLMs) excel in code-related tasks like code generation, but benchmark evaluations often overlook task characteristics, such as difficulty. Moreover, benchmar…
Diffusion-based Adversarial Purification for Intrusion Detection
Mohamed Amine Merzouk, Erwan Beurier, Reda Yaich +2
The escalating sophistication of cyberattacks has encouraged the integration of machine learning techniques in intrusion detection systems, but the rise of adversarial examples pre…
PathOCL: Path-Based Prompt Augmentation for OCL Generation with GPT-4
Seif Abukhalaf, Mohammad Hamdaqa, Foutse Khomh
The rapid progress of AI-powered programming assistants, such as GitHub Copilot, has facilitated the development of software applications. These assistants rely on large language m…
A Context-Driven Approach for Co-Auditing Smart Contracts with The Support of GPT-4 code interpreter
Mohamed Salah Bouafif, Chen Zheng, Ilham Ahmed Qasse +3
The surge in the adoption of smart contracts necessitates rigorous auditing to ensure their security and reliability. Manual auditing, although comprehensive, is time-consuming and…
Chain of Targeted Verification Questions to Improve the Reliability of Code Generated by LLMs
Sylvain Kouemo Ngassom, Arghavan Moradi Dakhel, Florian Tambon +1
LLM-based assistants, such as GitHub Copilot and ChatGPT, have the potential to generate code that fulfills a programming task described in a natural language description, referred…
Mining Action Rules for Defect Reduction Planning
Khouloud Oueslati, Gabriel Laberge, Maxime Lamothe +1
Defect reduction planning plays a vital role in enhancing software quality and minimizing software maintenance costs. By training a black box machine learning model and "explaining…
Introducing v0.5 of the AI Safety Benchmark from MLCommons
Bertie Vidgen, Adarsh Agrawal, Ahmed M. Ahmed +97
This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safe…
Machine Learning Robustness: A Primer
Houssem Ben Braiek, Foutse Khomh
This chapter explores the foundational concept of robustness in Machine Learning (ML) and its integral role in establishing trustworthiness in Artificial Intelligence (AI) systems.…
Bugs in Large Language Models Generated Code: An Empirical Study
Florian Tambon, Arghavan Moradi Dakhel, Amin Nikanjam +3
Large Language Models (LLMs) for code have gained significant attention recently. They can generate code in different programming languages based on provided prompts, fulfilling a…