91 citations · 250 across the 37 of their papers we have counts for
24 papers · 1 filter
ReCatcher: Towards LLMs Regression Testing for Code Generation
Altaf Allah Abbassi, Leuson Da Silva, Amin Nikanjam +1
Large Language Models (LLMs) for code generation evolve rapidly through fine-tuning, merging, or new model releases. However, such updates can introduce regressions, not only in co…
A Taxonomy of Inefficiencies in LLM-Generated Python Code
Altaf Allah Abbassi, Leuson Da Silva, Amin Nikanjam +1
Large Language Models (LLMs) are widely adopted for automated code generation with promising results. Although prior research has assessed LLM-generated code and identified various…
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…
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…
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.…
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…
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…
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…
Trained Without My Consent: Detecting Code Inclusion In Language Models Trained on Code
Vahid Majdinasab, Amin Nikanjam, Foutse Khomh
Code auditing ensures that the developed code adheres to standards, regulations, and copyright protection by verifying that it does not contain code from protected sources. The rec…
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…
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…
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…
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…
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…
Quality issues in Machine Learning Software Systems
Pierre-Olivier Côté, Amin Nikanjam, Rached Bouchoucha +1
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…
A Comparison of Reinforcement Learning Frameworks for Software Testing Tasks
Paulina Stevia Nouwou Mindom, Amin Nikanjam, Foutse Khomh
Software testing activities scrutinize the artifacts and the behavior of a software product to find possible defects and ensure that the product meets its expected requirements. Re…
GitHub Copilot AI pair programmer: Asset or Liability?
Arghavan Moradi Dakhel, Vahid Majdinasab, Amin Nikanjam +4
Automatic program synthesis is a long-lasting dream in software engineering. Recently, a promising Deep Learning (DL) based solution, called Copilot, has been proposed by OpenAI an…
Bugs in Machine Learning-based Systems: A Faultload Benchmark
Mohammad Mehdi Morovati, Amin Nikanjam, Foutse Khomh +2
The rapid escalation of applying Machine Learning (ML) in various domains has led to paying more attention to the quality of ML components. There is then a growth of techniques and…
Silent Bugs in Deep Learning Frameworks: An Empirical Study of Keras and TensorFlow
Florian Tambon, Amin Nikanjam, Le An +2
Deep Learning (DL) frameworks are now widely used, simplifying the creation of complex models as well as their integration to various applications even to non DL experts. However,…
The challenge of reproducible ML: an empirical study on the impact of bugs
Emilio Rivera-Landos, Foutse Khomh, Amin Nikanjam
Reproducibility is a crucial requirement in scientific research. When results of research studies and scientific papers have been found difficult or impossible to reproduce, we fac…
Design Smells in Deep Learning Programs: An Empirical Study
Amin Nikanjam, Foutse Khomh
Nowadays, we are witnessing an increasing adoption of Deep Learning (DL) based software systems in many industries. Designing a DL program requires constructing a deep neural netwo…
Automatic Fault Detection for Deep Learning Programs Using Graph Transformations
Amin Nikanjam, Houssem Ben Braiek, Mohammad Mehdi Morovati +1
Nowadays, we are witnessing an increasing demand in both corporates and academia for exploiting Deep Learning (DL) to solve complex real-world problems. A DL program encodes the ne…
Faults in Deep Reinforcement Learning Programs: A Taxonomy and A Detection Approach
Amin Nikanjam, Mohammad Mehdi Morovati, Foutse Khomh +1
A growing demand is witnessed in both industry and academia for employing Deep Learning (DL) in various domains to solve real-world problems. Deep Reinforcement Learning (DRL) is t…