Towards Interpreting the Behavior of Large Language Models on Software Engineering Tasks

Large Language Models (LLMs) have ushered in a significant breakthrough within the field of Natural Language Processing. Building upon this achievement, analogous language models have been developed specifically for code-related tasks, commonly referred to as Large Language Models for Code (LLMsC). Notable examples of LLMsC include CodeBERT, UnixCoder, CoPilot, among others. These models have demonstrated exceptional performance across various Software Engineering (SE) tasks, encompassing code summarization, test case generation, natural language to code conversion, bug triaging, malware detection, program repair, and more.