Investigating the Practicality of Just-in-time Defect Prediction with Semi-supervised Learning on Industrial Commit Data
Some of the challenges faced with Just-in-time defect (JIT) prediction are achieving high performing models and obtaining large quantities of labelled data. There is also a limited number of studies that actually test the effectiveness of software defect prediction models in practice. In this thesis...
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| Format: | Others |
| Language: | English |
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KTH, Skolan för elektroteknik och datavetenskap (EECS)
2019
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| Online Access: | http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254955 |