172 citations · 254 across the 21 of their papers we have counts for
15 papers · 1 filter
One Documentation Does Not Fit All: Case Study of TensorFlow Documentation
Sharuka Promodya Thirimanne, Elim Yoseph Lemango, Giulio Antoniol +1
Software documentation guides the proper use of tools or services. With the rapid growth of machine learning libraries, individuals from various fields are incorporating machine le…
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…
Data-access performance anti-patterns in data-intensive systems
Biruk Asmare Muse, Kawser Wazed Nafi, Foutse Khomh +1
Data-intensive systems handle variable, high volume, and high-velocity data generated by human and digital devices. Like traditional software, data-intensive systems are prone to t…
A Probabilistic Framework for Mutation Testing in Deep Neural Networks
Florian Tambon, Foutse Khomh, Giuliano Antoniol
Context: Mutation Testing (MT) is an important tool in traditional Software Engineering (SE) white-box testing. It aims to artificially inject faults in a system to evaluate a test…
Do Developers Refactor Data Access Code? An Empirical Study
Biruk Asmare Muse, Foutse Khomh, Giuliano Antoniol
Developers often refactor code to improve the maintainability and comprehension of the software. There are many studies on refactoring activities in traditional software systems. H…