Abstract
Defect prediction aims to estimate software reliability via learning from historical defect data. Cross-company defect prediction (CCDP) is a practical way that trains a prediction model by exploiting one or multiple projects of a source company and then applies the model to the target company. Unfortunately, larger irrelevant cross-company (CC) data usually makes it difficult to build a CCDP model with high performance. To address such issues, this paper proposes a data filtering method based on agglomerative clustering (DFAC) for CCDP. First, DFAC combines within-company (WC) instances and CC instances and uses agglomerative clustering algorithm to group these instances. Second, DFAC selects subclusters which consist of at least one WC instance, and collects the CC instances in the selected subclusters into a new CC data. Compared with existing data filter methods, the experiment results from 15 public PROMISE datasets show that DFAC increases the pd value, reduces the pf value and achieves higher [Formula: see text]-measure value.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Software Engineering and Knowledge Engineering
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
