Can we predict useful comments in source codes? - Analysis of findings from Information Retrieval in Software Engineering Track @ FIRE 2022
Srijoni Majumdar, Ayan Bandyopadhyay, Partha Pratim Das, Paul David Clough, Samiran Chattopadhyay, Prasenjit Majumder · 2022
The Information Retrieval in Software Engineering (IRSE) track aims to develop solutions for automated evaluation of code comments in a machine learning framework. In this track, there is a binary classification task to classify comments as useful and not useful. The dataset consists of 9048 code comments and surrounding code snippet pairs extracted from open source github C based projects. Overall 34 experiments have been submitted by 11 teams from various universities and software companies. The submissions have been evaluated quantitatively using the F1-Score and qualitatively based on the type of features developed, the supervised learning model used and their corresponding hyper-parameters. The best performing architectures mostly have employed transformer architectures coupled with a software development related embedding space.