COMET: A conceptual coupling based metrics suite for software defect prediction
| Autor: | Diana-Lucia Miholca, Vlad-Ioan Tomescu, Gabriela Czibula |
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| Rok vydání: | 2020 |
| Předmět: |
Source code
Computer science business.industry media_common.quotation_subject Software development 020206 networking & telecommunications 02 engineering and technology computer.software_genre Software metric Software quality Coupling (computer programming) Software bug Component-based software engineering 0202 electrical engineering electronic engineering information engineering General Earth and Planetary Sciences 020201 artificial intelligence & image processing Comet (programming language) Data mining business computer General Environmental Science media_common |
| Zdroj: | KES |
| ISSN: | 1877-0509 |
| DOI: | 10.1016/j.procs.2020.08.004 |
| Popis: | Identifying defective software components is an essential activity during software development which contributes to continuously improving the software quality. Since relatively numerous defects are due to violated software dependencies, coupling metrics could increase the performance of software defect prediction. Among various measures expressing the coupling between software components, the conceptual coupling metrics capture similarities based on the semantic information contained in the source code. We are introducing a new conceptual coupling based metric suite, named COMET, for software defect prediction. Experiments conducted on publicly available data sets, using both unsupervised and supervised learning models, emphasize that COMET metrics suite is superior to the software metrics widely used in the defect prediction literature. |
| Databáze: | OpenAIRE |
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