5 citations · 8 across the 3 of their papers we have counts for
4 papers
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…
Observation-based unit test generation at Meta
Nadia Alshahwan, Mark Harman, Alexandru Marginean +2
TestGen automatically generates unit tests, carved from serialized observations of complex objects, observed during app execution. We describe the development and deployment of Tes…
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…
Indexing Operators to Extend the Reach of Symbolic Execution
Earl T. Barr, David Clark, Mark Harman +1
Traditional program analysis analyses a program language, that is, all programs that can be written in the language. There is a difference, however, between all possible programs t…