87 citations · 467 across the 46 of their papers we have counts for
5 papers · 1 filter
On the need to perform comprehensive evaluations of automated program repair benchmarks: Sorald case study
Sumudu Liyanage, Sherlock A. Licorish, Markus Wagner +1
In supporting the development of high-quality software, especially necessary in the era of LLMs, automated program repair (APR) tools aim to improve code quality by automatically a…
Static Analysis as a Feedback Loop: Enhancing LLM-Generated Code Beyond Correctness
Scott Blyth, Sherlock A. Licorish, Christoph Treude +1
Large language models (LLMs) have demonstrated impressive capabilities in code generation, achieving high scores on benchmarks such as HumanEval and MBPP. However, these benchmarks…
Predictive Analytics for Collaborators Answers, Code Quality, and Dropout on Stack Overflow
Elijah Zolduoarrati, Sherlock A. Licorish, Nigel Stanger
Previous studies that used data from Stack Overflow to develop predictive models often employed limited benchmarks of 3-5 models or adopted arbitrary selection methods. Despite bei…
GBM Returns the Best Prediction Performance among Regression Approaches: A Case Study of Stack Overflow Code Quality
Sherlock A. Licorish, Brendon Woodford, Lakmal Kiyaduwa Vithanage +1
Practitioners are increasingly dependent on publicly available resources for supporting their knowledge needs during software development. This has thus caused a spotlight to be pa…
Comparing Human and LLM Generated Code: The Jury is Still Out!
Sherlock A. Licorish, Ansh Bajpai, Chetan Arora +2
Much is promised in relation to AI-supported software development. However, there has been limited evaluation effort in the research domain aimed at validating the true utility of…