Beena Kumari


2023

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Robust Code Summarization
Debanjan Mondal | Abhilasha Lodha | Ankita Sahoo | Beena Kumari
Proceedings of the 1st GenBench Workshop on (Benchmarking) Generalisation in NLP

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.