Exploring the Effectiveness of LLM based Test-driven Interactive Code Generation: User Study and Empirical Evaluation

Large language models (LLMs) have shown great potential in automating significant aspects of coding by producing natural code from informal natural language (NL) intent. However, given NL is informal, it does not lend easily to checking that the generated ...

Testing aims at detecting (regression) bugs in production code. However, testing code is just as likely to contain bugs as the code it tests. Buggy test cases can silently miss bugs in the production code or loudly ring false alarms when the production ...

Researchers have recently leveraged transformer-based test code generation models to improve testing-related tasks (e.g., assert completion and test method generation). One such model, AthenaTest, has been recently introduced, and it has been well ...