Foutse Khomh
YOU?
Author Swipe
View article: One Size Does Not Fit All: Architecture-Aware Adaptive Batch Scheduling with DEBA
One Size Does Not Fit All: Architecture-Aware Adaptive Batch Scheduling with DEBA Open
Adaptive batch size methods aim to accelerate neural network training, but existing approaches apply identical adaptation strategies across all architectures, assuming a one-size-fits-all solution. We introduce DEBA (Dynamic Efficient Batc…
View article: RefAgent: A Multi-agent LLM-based Framework for Automatic Software Refactoring
RefAgent: A Multi-agent LLM-based Framework for Automatic Software Refactoring Open
Large Language Models (LLMs) have substantially influenced various software engineering tasks. Indeed, in the case of software refactoring, traditional LLMs have shown the ability to reduce development time and enhance code quality. Howeve…
View article: Correction to: Assessing the adoption of security policies by developers in terraform across different cloud providers
Correction to: Assessing the adoption of security policies by developers in terraform across different cloud providers Open
View article: FairFLRep: Fairness aware fault localization and repair of Deep Neural Networks
FairFLRep: Fairness aware fault localization and repair of Deep Neural Networks Open
Deep neural networks (DNNs) are being utilized in various aspects of our daily lives, including high-stakes decision-making applications that impact individuals. However, these systems reflect and amplify bias from the data used during tra…
View article: Adversarial attack classification and robustness testing for large language models for code
Adversarial attack classification and robustness testing for large language models for code Open
View article: ReCatcher: Towards LLMs Regression Testing for Code Generation
ReCatcher: Towards LLMs Regression Testing for Code Generation Open
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 correctness but also in code quality and performance. To addr…
View article: LLMs and Stack Overflow discussions: Reliability, impact, and challenges
LLMs and Stack Overflow discussions: Reliability, impact, and challenges Open
View article: Health data issues in Africa: time for digitization, standardization and harmonization
Health data issues in Africa: time for digitization, standardization and harmonization Open
This commentary discusses health data challenges in Africa, focusing on digitization, standardization, and harmonization as key solutions. It highlights how addressing these foundational issues can enable AI and data science to transform h…
View article: SDLog: A Deep Learning Framework for Detecting Sensitive Information in Software Logs
SDLog: A Deep Learning Framework for Detecting Sensitive Information in Software Logs Open
Software logs are messages recorded during the execution of a software system that provide crucial run-time information about events and activities. Although software logs have a critical role in software maintenance and operation tasks, p…
View article: Evaluating and Enhancing Segmentation Model Robustness with Metamorphic Testing
Evaluating and Enhancing Segmentation Model Robustness with Metamorphic Testing Open
Image segmentation is critical for applications such as medical imaging, augmented reality, and video surveillance. However, segmentation models often lack robustness, making them vulnerable to adversarial perturbations from subtle image d…
View article: Representation Improvement in Latent Space for Search-Based Testing of Autonomous Robotic Systems
Representation Improvement in Latent Space for Search-Based Testing of Autonomous Robotic Systems Open
Testing autonomous robotic systems, such as self-driving cars and unmanned aerial vehicles, is challenging due to their interaction with highly unpredictable environments. A common practice is to first conduct simulation-based testing, whi…
View article: MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation
MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation Open
Existing large language model (LLM) evaluation benchmarks primarily focus on English, while current multilingual tasks lack parallel questions that specifically assess cross-linguistic reasoning abilities. This dual limitation makes it cha…
View article: A Taxonomy of Inefficiencies in LLM-Generated Python Code
A Taxonomy of Inefficiencies in LLM-Generated Python Code Open
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 quality issues -- such as redundancy, poor maintainability…
View article: Assessing the adoption of security policies by developers in terraform across different cloud providers
Assessing the adoption of security policies by developers in terraform across different cloud providers Open
View article: Automated UML Visualization of Software Ecosystems: Tracking Versions, Dependencies, and Security Updates
Automated UML Visualization of Software Ecosystems: Tracking Versions, Dependencies, and Security Updates Open
View article: MAC: Multi-Agent LLM Coder is All You Need
MAC: Multi-Agent LLM Coder is All You Need Open
View article: MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation
MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation Open
View article: Diffusion-Based Adversarial Purification for Intrusion Detection
Diffusion-Based Adversarial Purification for Intrusion Detection Open
View article: Continuously Learning Bug Locations
Continuously Learning Bug Locations Open
Automatically locating buggy changesets associated with bug reports is crucial in the software development process. Deep Learning (DL)-based techniques show promising results by leveraging structural information from the code and learning …
View article: An Efficient Model Maintenance Approach for MLOps
An Efficient Model Maintenance Approach for MLOps Open
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 distributions. However, the data evolves over time in many application …
View article: Tracing Optimization for Performance Modeling and Regression Detection
Tracing Optimization for Performance Modeling and Regression Detection Open
Software performance modeling plays a crucial role in developing and maintaining software systems. A performance model analytically describes the relationship between the performance of a system and its runtime activities. This process typ…
View article: Towards Understanding the Impact of Data Bugs on Deep Learning Models in Software Engineering
Towards Understanding the Impact of Data Bugs on Deep Learning Models in Software Engineering Open
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 many sources, including training data. Existing literature…
View article: Fault Localization in Deep Learning-based Software: A System-level Approach
Fault Localization in Deep Learning-based Software: A System-level Approach Open
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. Given that fault localization is a critical task in reliab…
View article: Impact of LLM-based Review Comment Generation in Practice: A Mixed Open-/Closed-source User Study
Impact of LLM-based Review Comment Generation in Practice: A Mixed Open-/Closed-source User Study Open
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 performed in two organizations, Mozilla (which has its codebase …
View article: Towards Optimizing SQL Generation via LLM Routing
Towards Optimizing SQL Generation via LLM Routing Open
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 strong accuracy for complex queries, they incur unnecessary …
View article: Trained without My Consent: Detecting Code Inclusion in Language Models Trained on Code
Trained without My Consent: Detecting Code Inclusion in Language Models Trained on Code Open
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 recent advent of Large Language Models (LLMs) as coding assist…
View article: In-Simulation Testing of Deep Learning Vision Models in Autonomous Robotic Manipulators
In-Simulation Testing of Deep Learning Vision Models in Autonomous Robotic Manipulators Open
Testing autonomous robotic manipulators is challenging due to the complex software interactions between vision and control components. A crucial element of modern robotic manipulators is the deep learning based object detection model. The …
View article: What Information Contributes to Log-based Anomaly Detection? Insights from a Configurable Transformer-Based Approach
What Information Contributes to Log-based Anomaly Detection? Insights from a Configurable Transformer-Based Approach Open
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 anomaly detection approaches typically leverage deep learni…
View article: Understanding Web Application Workloads and Their Applications: Systematic Literature Review and Characterization
Understanding Web Application Workloads and Their Applications: Systematic Literature Review and Characterization Open
Web applications, accessible via web browsers over the Internet, facilitate complex functionalities without local software installation. In the context of web applications, a workload refers to the number of user requests sent by users or …
View article: Protecting Privacy in Software Logs: What Should Be Anonymized?
Protecting Privacy in Software Logs: What Should Be Anonymized? Open
Software logs, generated during the runtime of software systems, are essential for various development and analysis activities, such as anomaly detection and failure diagnosis. However, the presence of sensitive information in these logs p…