6 papers
On the risk of coding before testing: An empirical study on LLM-based test generation workflow
Michael Konstantinou, Florian Tambon, Mike Papadakis
Large Language Models (LLMs) are increasingly used in software engineering workflows to generate both source code and test suites. This dual capability has enabled emerging develop…
Flow-A11y: Flow-Aware Accessibility Testing
Nasr Eddine Fliti, Leisan Kokorina, Florian Tambon +1
Modern web applications increasingly expose accessibility barriers through interaction flows rather than static page snapshots. Keyboard traps, focus loss, modal leakage, delayed s…
Prompt Coverage Adequacy
Florian Tambon, Michael Konstantinou, Cedric Richter +3
In recent years, it has become increasingly evident that large language models (LLMs) and autonomous agents raise the level of abstraction in software development by shifting the f…
Inferring Code Correctness from Specification
Tambon Florian, Papadakis Mike
Large language models (LLMs) have become integral to modern software development, enabling automated code generation at scale. However, validating the correctness of LLM-generated…
On the Robustness of Tabular Foundation Models: Test-Time Attacks and In-Context Defenses
Mohamed Djilani, Thibault Simonetto, Karim Tit +4
Recent tabular Foundational Models (FM) such as TabPFN and TabICL, leverage in-context learning to achieve strong performance without gradient updates or fine-tuning. However, thei…
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…