DC Field | Value | Language |
---|---|---|
dc.contributor.author | Choi, Jiwon | ko |
dc.contributor.author | Manikandan, Saranya | ko |
dc.contributor.author | Ryu, Duksan | ko |
dc.contributor.author | Baik, Jongmoon | ko |
dc.date.accessioned | 2022-11-15T02:00:40Z | - |
dc.date.available | 2022-11-15T02:00:40Z | - |
dc.date.created | 2022-11-13 | - |
dc.date.created | 2022-11-13 | - |
dc.date.issued | 2022-07-08 | - |
dc.identifier.citation | 2nd International Workshop on Big data driven Edge Cloud Services (BECS 2022), pp.34 - 45 | - |
dc.identifier.issn | 1865-0929 | - |
dc.identifier.uri | http://hdl.handle.net/10203/299610 | - |
dc.description.abstract | Just-in-time (JIT) defect prediction has been used to predict whether a code change is defective or not. Existing JIT prediction has been applied to different kind of open-source software platform for cloud computing, but JIT defect prediction has never been applied in self-driving software. Unlike other software systems, self-driving system is an AI-enabled system and is a representative system to which edge cloud service is applied. Therefore, we aim to identify whether the existing JIT defect prediction models for traditional software systems also work well for self-driving software. To this end, we collect and label the dataset of open-source self-driving software project using SZZ (Śliwerski, Zimmermann and Zeller) algorithm. And we select four traditional machine learning methods and state-of-the-art research (i.e., JIT-Line) as our baselines and compare their prediction performance. Our experimental results show that JITLine and logistic regression produce superior performance, however, there exists a room to be improved. Through XAI (Explainable AI) analysis it turned out that the prediction performance is mainly affected by experience and history-related features among change-level metrics. Our study is expected to provide important insight for practitioners and subsequent researchers performing defect prediction in AI-enabled system. | - |
dc.language | English | - |
dc.publisher | International Society for Web Engineering | - |
dc.title | An Empirical Analysis on Just-In-Time Defect Prediction Models for Self-Driving Software Systems | - |
dc.type | Conference | - |
dc.identifier.scopusid | 2-s2.0-85149617905 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 34 | - |
dc.citation.endingpage | 45 | - |
dc.citation.publicationname | 2nd International Workshop on Big data driven Edge Cloud Services (BECS 2022) | - |
dc.identifier.conferencecountry | IT | - |
dc.identifier.conferencelocation | Bari | - |
dc.identifier.doi | 10.1007/978-3-031-25380-5_3 | - |
dc.contributor.localauthor | Baik, Jongmoon | - |
dc.contributor.nonIdAuthor | Choi, Jiwon | - |
dc.contributor.nonIdAuthor | Manikandan, Saranya | - |
dc.contributor.nonIdAuthor | Ryu, Duksan | - |
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