Leveraging Machine Learning for Software Redocumentation
| Autor: | Josef Pichler, Verena Geist, Martin Pinzger, Michael Moser, Stefanie Beyer |
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| Rok vydání: | 2020 |
| Předmět: |
Source code
Exploit business.industry Heuristic Computer science media_common.quotation_subject Legacy system 020207 software engineering 02 engineering and technology COBOL Machine learning computer.software_genre Software 0202 electrical engineering electronic engineering information engineering Software system Artificial intelligence Heuristics business computer media_common computer.programming_language |
| Zdroj: | SANER |
| DOI: | 10.1109/saner48275.2020.9054838 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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