Understanding Code Semantics: An Evaluation of Transformer Models in Summarization
This work addresses the challenge of improving code understanding for software developers, but it is incremental as it focuses on evaluation rather than new methods.
The paper tackled the problem of evaluating whether transformer models truly understand code semantics in summarization by altering names and introducing adversaries like dead code across three programming languages, finding insights into model limitations.
This paper delves into the intricacies of code summarization using advanced transformer-based language models. Through empirical studies, we evaluate the efficacy of code summarization by altering function and variable names to explore whether models truly understand code semantics or merely rely on textual cues. We have also introduced adversaries like dead code and commented code across three programming languages (Python, Javascript, and Java) to further scrutinize the model's understanding. Ultimately, our research aims to offer valuable insights into the inner workings of transformer-based LMs, enhancing their ability to understand code and contributing to more efficient software development practices and maintenance workflows.