Leveraging machine learning for software redocumentation

Authors Verena Geist
Michael Moser
Josef Pichler
Stefanie Beyer
Martin Pinzger
Editors
Title Leveraging machine learning for software redocumentation
Booktitle Proceedings of the 2020 IEEE 27th International Conference on Software Analysis, Evolution, and Reengineering (SANER'20)
Type in proceedings
Publisher IEEE
ISBN 978-1-7281-5143-4
Month February
Year 2020
Pages 622-626
SCCH ID# 19096
Abstract

Source code comments contain key information about the underlying software system. Many redocumentation approaches, however, cannot exploit this valuable source of information. This is mainly due to the fact that not all comments have the same goals and target audience and can therefore only be used selectively for redocumentation. Performing a required classification manually, e.g. in the form of heuristic rules, is usually time-consuming and error-prone and strongly dependent on programming languages and guidelines of concrete software systems. By leveraging machine learning, it should be possible to classify comments and thus transfer valuable information from the source code into documentation with less effort but the same quality. We applied different machine learning techniques to a COBOL legacy system and compared the results with industry-strength heuristic classification. As a result, we found that machine learning outperforms the heuristics in number of errors and less effort.