Computational Intelligence for Software Engineering Lab

Our research focuses on the exciting intersection between software engineering and machine intelligence. We try to improve developer productivity with optimisation and automation. COINSE has world-leading expertise in automated debugging, automated testing, and testing of DNN models.

Recent Updates

Our paper "A Quantitative and Qualitative Evaluation of LLM-based Explainable Fault Localization" has been accepted to FSE 2024

Our paper about using LLMs to automatically locate which part of the code is responsible for a bug was accepted to FSE'24. [more...]

Our paper "Intent-Driven Mobile GUI Testing with Autonomous Large Language Model Agents" has been accepted to ICST 2024

Our paper proposing an LLM-based autonomous agent for GUI testing was accepted to ICST 2024. [more...]

Our paper "A Bayesian Framework for Automated Debugging" has been accepted to ISSTA 2023

Our paper proposing a Bayesian framework to understand automated debugging techniques was accepted to ISSTA'23. [more...]

Our paper "Fonte: Finding Bug Inducing Commits from Failures" has been accepted at ICSE 2023

This paper is about finding bug-inducing commits by combining fault localisation and commit history mining. [more...]

Our paper "Large Language Models are Few-shot Testers: Exploring LLM-based General Bug Reproduction" has been accepted to ICSE 2023

Our paper about generating bug-reproducing tests from bug reports was accepted to ICSE'23. [more...]

Congratulations, Dr. Jinhan Kim!

Jinhan Kim successfully defended his PhD - the third to do so from COINSE. [more...]