20 citations · 51 across the 20 of their papers we have counts for
21 papers
When Model Release Meets Model Reuse: Producer-Consumer Misalignment in Hugging Face
Adekunle Ajibode, Oussama Ben Sghaier, Bram Adams +1
Pre-trained Language Models (PTLMs) are increasingly reused as dependencies in modern software systems, even though prior work has documented persistent structural problems in PTLM…
Towards Imputation of Pre-Trained Language Model Metadata using Semantic Fingerprinting
Adekunle Ajibode, Oussama Ben Sghaier, Keheliya Gallaba +2
Pre-trained language models (PTLMs) hosted on platforms such as Hugging Face form complex lineage structures similar to software dependency graphs. However, unlike traditional soft…
Towards Evaluation Engineering: An Empirical Study of ML Evaluation Harnesses in the Wild
Zhimin Zhao, Zehao Wang, Abdul Ali Bangash +2
Evaluation harnesses are software systems that orchestrate model evaluation by managing model invocation, data loading, metric computation, and result reporting. Despite their crit…
Do AI Coding Agents Log Like Humans? An Empirical Study
Youssef Esseddiq Ouatiti, Mohammed Sayagh, Hao Li +1
Software logging is essential for maintaining and debugging complex systems, yet it remains unclear how AI coding agents handle this non-functional requirement. While prior work ch…
Assessing and Improving the Representativeness of Code Generation Benchmarks Using Knowledge Units (KUs) of Programming Languages -- An Empirical Study
Md Ahasanuzzaman, Bram Adams, Emad Fallahzadeh +2
Large Language Models (LLMs) such as GPT-4, Claude and LLaMA have shown impressive performance in code generation, typically evaluated using benchmarks (e.g., HumanEval). However,…
HAFixAgent: History-Aware Agentic Program Repair
Yu Shi, Hao Li, Bram Adams +1
Automated program repair (APR) has recently shifted toward large language models and agent-based systems, while prior work shows that repository history can help repair single-line…
OmniLLP: Enhancing LLM-based Log Level Prediction with Context-Aware Retrieval
Youssef Esseddiq Ouatiti, Mohammed Sayagh, Bram Adams +1
Developers insert logging statements in source code to capture relevant runtime information essential for maintenance and debugging activities. Log level choice is an integral, yet…
On the synchronization between Hugging Face pre-trained language models and their upstream GitHub repository
Adekunle Ajibode, Abdul Ali Bangash, Oussama Ben Sghaier +2
Pre-trained language models (PTLMs) have transformed natural language processing (NLP), enabling major advances in tasks such as text generation and translation. Similar to softwar…
HAFix: History-Augmented Large Language Models for Bug Fixing
Yu Shi, Abdul Ali Bangash, Emad Fallahzadeh +2
Recent studies have explored the performance of Large Language Models (LLMs) on various Software Engineering (SE) tasks, such as code generation and bug fixing. However, these appr…
A Large-Scale Exploratory Study on the Proxy Pattern in Ethereum
Amir M. Ebrahimi, Bram Adams, Gustavo A. Oliva +1
The proxy pattern is a well-known design pattern with numerous use cases in several sectors of the software industry. As such, the use of the proxy pattern is also a common approac…
UPC Sentinel: An Accurate Approach for Detecting Upgradeability Proxy Contracts in Ethereum
Amir M. Ebrahimi, Bram Adams, Gustavo A. Oliva +1
Software applications that run on a blockchain platform are known as DApps. DApps are built using smart contracts, which are immutable after deployment. Just like any real-world so…
On the Impact of White-box Deployment Strategies for Edge AI on Latency and Model Performance
Jaskirat Singh, Bram Adams, Ahmed E. Hassan
To help MLOps engineers decide which operator to use in which deployment scenario, this study aims to empirically assess the accuracy vs latency trade-off of white-box (training-ba…
Towards Semantic Versioning of Open Pre-trained Language Model Releases on Hugging Face
Adekunle Ajibode, Abdul Ali Bangash, Filipe Roseiro Cogo +2
The proliferation of open Pre-trained Language Models (PTLMs) on model registry platforms like Hugging Face (HF) presents both opportunities and challenges for companies building p…
On the Workflows and Smells of Leaderboard Operations (LBOps): An Exploratory Study of Foundation Model Leaderboards
Zhimin Zhao, Abdul Ali Bangash, Filipe Roseiro Côgo +2
Foundation models (FM), such as large language models (LLMs), which are large-scale machine learning (ML) models, have demonstrated remarkable adaptability in various downstream so…
Predicting long time contributors with knowledge units of programming languages: an empirical study
Md Ahasanuzzaman, Gustavo A. Oliva, Ahmed E. Hassan
Predicting potential long-time contributors (LTCs) early allows project maintainers to effectively allocate resources and mentoring to enhance their development and retention. Mapp…
Does Using Bazel Help Speed Up Continuous Integration Builds?
Shenyu Zheng, Bram Adams, Ahmed E. Hassan
A long continuous integration (CI) build forces developers to wait for CI feedback before starting subsequent development activities, leading to time wasted. In addition to a varie…
A State-of-the-practice Release-readiness Checklist for Generative AI-based Software Products
Harsh Patel, Dominique Boucher, Emad Fallahzadeh +2
This paper investigates the complexities of integrating Large Language Models (LLMs) into software products, with a focus on the challenges encountered for determining their readin…
On the Impact of Black-box Deployment Strategies for Edge AI on Latency and Model Performance
Jaskirat Singh, Emad Fallahzadeh, Bram Adams +1
Deciding what combination of operators to use across the Edge AI tiers to achieve specific latency and model performance requirements is an open question for MLOps engineers. This…
An Empirical Study of Challenges in Machine Learning Asset Management
Zhimin Zhao, Yihao Chen, Abdul Ali Bangash +2
In machine learning (ML), efficient asset management, including ML models, datasets, algorithms, and tools, is vital for resource optimization, consistent performance, and a stream…
Why Not Mitigate Vulnerabilities in Helm Charts?
Yihao Chen, Jiahuei Lin, Bram Adams +1
[Context]: Containerization ensures the resilience of distributed applications by Kubernetes. Helm is a package manager for Kubernetes applications. A Helm package, namely "Chart''…
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…