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

Large Language Models (LLMs) have ushered in a significant break-through 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. Despite the promising results achieved by LLMsC in SE tasks, there remains fundamental questions regarding their decision-making processes. Understanding these model decision mechanisms is crucial for further enhancing the performance of LLMsC. In pursuit of this objective, my PhD dissertation aims to pioneer novel methodologies for interpreting and comprehending the behavior of LLMsC.