172 citations · 802 across the 171 of their papers we have counts for
26 papers · 1 filter
Harnessing Pre-trained Generalist Agents for Software Engineering Tasks
Paulina Stevia Nouwou Mindom, Amin Nikanjam, Foutse Khomh
Nowadays, we are witnessing an increasing adoption of Artificial Intelligence (AI) to develop techniques aimed at improving the reliability, effectiveness, and overall quality of s…
Refining GPT-3 Embeddings with a Siamese Structure for Technical Post Duplicate Detection
Xingfang Wu, Heng Li, Nobukazu Yoshioka +2
One goal of technical online communities is to help developers find the right answer in one place. A single question can be asked in different ways with different wordings, leading…
Characterizing and Classifying Developer Forum Posts with their Intentions
Xingfang Wu, Eric Laufer, Heng Li +3
With the rapid growth of the developer community, the amount of posts on online technical forums has been growing rapidly, which poses difficulties for users to filter useful posts…
An empirical study of testing machine learning in the wild
Moses Openja, Foutse Khomh, Armstrong Foundjem +4
Recently, machine and deep learning (ML/DL) algorithms have been increasingly adopted in many software systems. Due to their inductive nature, ensuring the quality of these systems…
Assessing the Security of GitHub Copilot Generated Code -- A Targeted Replication Study
Vahid Majdinasab, Michael Joshua Bishop, Shawn Rasheed +3
AI-powered code generation models have been developing rapidly, allowing developers to expedite code generation and thus improve their productivity. These models are trained on lar…
An Empirical Study of Self-Admitted Technical Debt in Machine Learning Software
Aaditya Bhatia, Foutse Khomh, Bram Adams +1
The emergence of open-source ML libraries such as TensorFlow and Google Auto ML has enabled developers to harness state-of-the-art ML algorithms with minimal overhead. However, dur…
GIST: Generated Inputs Sets Transferability in Deep Learning
Florian Tambon, Foutse Khomh, Giuliano Antoniol
To foster the verifiability and testability of Deep Neural Networks (DNN), an increasing number of methods for test case generation techniques are being developed. When confronted…
Detection and Evaluation of bias-inducing Features in Machine learning
Moses Openja, Gabriel Laberge, Foutse Khomh
The cause-to-effect analysis can help us decompose all the likely causes of a problem, such as an undesirable business situation or unintended harm to the individual(s). This impli…
A Large-Scale Exploratory Study of Android Sports Apps in the Google Play Store
Bhagya Chembakottu, Heng Li, Foutse Khomh
Prior studies on mobile app analysis often analyze apps across different categories or focus on a small set of apps within a category. These studies either provide general insights…
Common Challenges of Deep Reinforcement Learning Applications Development: An Empirical Study
Mohammad Mehdi Morovati, Florian Tambon, Mina Taraghi +2
Machine Learning (ML) is increasingly being adopted in different industries. Deep Reinforcement Learning (DRL) is a subdomain of ML used to produce intelligent agents. Despite rece…
Data Cleaning and Machine Learning: A Systematic Literature Review
Pierre-Olivier Côté, Amin Nikanjam, Nafisa Ahmed +2
Context: Machine Learning (ML) is integrated into a growing number of systems for various applications. Because the performance of an ML model is highly dependent on the quality of…
Effective Test Generation Using Pre-trained Large Language Models and Mutation Testing
Arghavan Moradi Dakhel, Amin Nikanjam, Vahid Majdinasab +2
One of the critical phases in software development is software testing. Testing helps with identifying potential bugs and reducing maintenance costs. The goal of automated test gen…
Reinforcement Learning Informed Evolutionary Search for Autonomous Systems Testing
Dmytro Humeniuk, Foutse Khomh, Giuliano Antoniol
Evolutionary search-based techniques are commonly used for testing autonomous robotic systems. However, these approaches often rely on computationally expensive simulator-based mod…
An Intentional Forgetting-Driven Self-Healing Method For Deep Reinforcement Learning Systems
Ahmed Haj Yahmed, Rached Bouchoucha, Houssem Ben Braiek +1
Deep reinforcement learning (DRL) is increasingly applied in large-scale productions like Netflix and Facebook. As with most data-driven systems, DRL systems can exhibit undesirabl…
Deploying Deep Reinforcement Learning Systems: A Taxonomy of Challenges
Ahmed Haj Yahmed, Altaf Allah Abbassi, Amin Nikanjam +2
Deep reinforcement learning (DRL), leveraging Deep Learning (DL) in reinforcement learning, has shown significant potential in achieving human-level autonomy in a wide range of dom…
Exploring Security Practices in Infrastructure as Code: An Empirical Study
Alexandre Verdet, Mohammad Hamdaqa, Leuson Da Silva +1
Cloud computing has become popular thanks to the widespread use of Infrastructure as Code (IaC) tools, allowing the community to conveniently manage and configure cloud infrastruct…
On the Effectiveness of Log Representation for Log-based Anomaly Detection
Xingfang Wu, Heng Li, Foutse Khomh
Logs are an essential source of information for people to understand the running status of a software system. Due to the evolving modern software architecture and maintenance metho…
An Empirical Study on Bugs Inside PyTorch: A Replication Study
Sharon Chee Yin Ho, Vahid Majdinasab, Mohayeminul Islam +5
Software systems are increasingly relying on deep learning components, due to their remarkable capability of identifying complex data patterns and powering intelligent behaviour. A…
Bug Characterization in Machine Learning-based Systems
Mohammad Mehdi Morovati, Amin Nikanjam, Florian Tambon +3
Rapid growth of applying Machine Learning (ML) in different domains, especially in safety-critical areas, increases the need for reliable ML components, i.e., a software component…
Responsible Design Patterns for Machine Learning Pipelines
Saud Hakem Al Harbi, Lionel Nganyewou Tidjon, Foutse Khomh
Integrating ethical practices into the AI development process for artificial intelligence (AI) is essential to ensure safe, fair, and responsible operation. AI ethics involves appl…
Quality Issues in Machine Learning Software Systems
Pierre-Olivier Côté, Amin Nikanjam, Rached Bouchoucha +3
Context: An increasing demand is observed in various domains to employ Machine Learning (ML) for solving complex problems. ML models are implemented as software components and depl…
Leveraging Data Mining Algorithms to Recommend Source Code Changes
AmirHossein Naghshzan, Saeed Khalilazar, Pierre Poilane +3
Context: Recent research has used data mining to develop techniques that can guide developers through source code changes. To the best of our knowledge, very few studies have inves…
On Codex Prompt Engineering for OCL Generation: An Empirical Study
Seif Abukhalaf, Mohammad Hamdaqa, Foutse Khomh
The Object Constraint Language (OCL) is a declarative language that adds constraints and object query expressions to MOF models. Despite its potential to provide precision and conc…
Mutation Testing of Deep Reinforcement Learning Based on Real Faults
Florian Tambon, Vahid Majdinasab, Amin Nikanjam +2
Testing Deep Learning (DL) systems is a complex task as they do not behave like traditional systems would, notably because of their stochastic nature. Nonetheless, being able to ad…