2 citations · 3 across the 9 of their papers we have counts for
9 papers
CASPER-Change-Aware Slice Prioritization for Efficient Regression Testing of LLM-based systems
Biruk Asmare Muse, Lionel Briand, Yiwei Lu +1
Regression testing for LLM-based systems poses unique challenges because individual regression instances provide limited information about system-level regressions. A failure in a…
TATG: Tracking-Aware Testing Objective for LLM-based Test Generation
Guancheng Wang, Qinghua Xu, Lionel C. Briand
Complex Java methods remain challenging for automated unit test generation because achieving high coverage and fault detection often requires satisfying branch-specific testing req…
BeSpec: Behavior-Level Specification Alignment for Code Generation
Qinghua Xu, Guancheng Wang, Boxi Yu +1
LLMs have made substantial progress on automated code generation from natural-language descriptions of desired behavior (intent). Most existing methods improve generated programs t…
Efficient Black-Box Fault Localization for System-Level Test Code Using Large Language Models
Ahmadreza Saboor Yaraghi, Golnaz Gharachorlu, Sakina Fatima +3
Fault localization (FL) is a critical step in debugging, which typically relies on repeated executions to pinpoint faulty code regions. However, repeated executions can be impracti…
Beyond Strict Rules: Assessing the Effectiveness of Large Language Models for Code Smell Detection
Saymon Souza, Amanda Santana, Eduardo Figueiredo +3
Code smells are symptoms of potential code quality problems that may affect software maintainability, thus increasing development costs and impacting software reliability. Large la…
Mutation-Guided Unit Test Generation with a Large Language Model
Guancheng Wang, Qinghua Xu, Lionel Briand +1
Unit tests play a vital role in uncovering potential faults in software. While tools like EvoSuite focus on maximizing code coverage, recent advances in large language models (LLMs…