Leveraging Machine Learning for Software Redocumentation

Verena Geist, Michael Moser, Josef Pichler, Stefanie Beyer, Martin Pinzger

Publikation: Beitrag in Buch/Bericht/TagungsbandKonferenzbeitrag

4 Zitate (Scopus)

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.

OriginalspracheEnglisch
TitelSANER 2020 - Proceedings of the 2020 IEEE 27th International Conference on Software Analysis, Evolution, and Reengineering
Redakteure/-innenKostas Kontogiannis, Foutse Khomh, Alexander Chatzigeorgiou, Marios-Eleftherios Fokaefs, Minghui Zhou
Herausgeber (Verlag)IEEE
Seiten622-626
Seitenumfang5
ISBN (elektronisch)9781728151434
DOIs
PublikationsstatusVeröffentlicht - Feb 2020
Veranstaltung27th International Conference on Software Analysis, Evolution and Reengineering - London, Ontario, Kanada
Dauer: 19 Feb 202021 Feb 2020
http://saner2020.csd.uwo.ca/

Publikationsreihe

NameSANER 2020 - Proceedings of the 2020 IEEE 27th International Conference on Software Analysis, Evolution, and Reengineering

Konferenz

Konferenz27th International Conference on Software Analysis, Evolution and Reengineering
Land/GebietKanada
OrtLondon, Ontario
Zeitraum19.02.202021.02.2020
Internetadresse

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