33 citations · 43 across the 5 of their papers we have counts for
5 papers
Harden and Catch for Just-in-Time Assured LLM-Based Software Testing: Open Research Challenges
Mark Harman, Peter O'Hearn, Shubho Sengupta
Despite decades of research and practice in automated software testing, several fundamental concepts remain ill-defined and under-explored, yet offer enormous potential real-world…
Mutation-Guided LLM-based Test Generation at Meta
Christopher Foster, Abhishek Gulati, Mark Harman +5
This paper describes Meta's ACH system for mutation-guided LLM-based test generation. ACH generates relatively few mutants (aka simulated faults), compared to traditional mutation…
Automated Unit Test Improvement using Large Language Models at Meta
Nadia Alshahwan, Jubin Chheda, Anastasia Finegenova +6
This paper describes Meta's TestGen-LLM tool, which uses LLMs to automatically improve existing human-written tests. TestGen-LLM verifies that its generated test classes successful…
Assured LLM-Based Software Engineering
Nadia Alshahwan, Mark Harman, Inna Harper +3
In this paper we address the following question: How can we use Large Language Models (LLMs) to improve code independently of a human, while ensuring that the improved code - does…
Large Language Models for Software Engineering: Survey and Open Problems
Angela Fan, Beliz Gokkaya, Mark Harman +4
This paper provides a survey of the emerging area of Large Language Models (LLMs) for Software Engineering (SE). It also sets out open research challenges for the application of LL…