caa a22 4500 605495807 CHVBK 20210128100533.0 cr unu---uuuuu 210128e20150601xx s 000 0 eng 10.1007/s11219-014-9230-x doi (NATIONALLICENCE)springer-10.1007/s11219-014-9230-x Predicting defective modules in different test phases [Elektronische Daten] [Bora Caglayan, Ayse Tosun Misirli, Ayse Bener, Andriy Miranskyy] Defect prediction is a well-established research area in software engineering . Prediction models in the literature do not predict defect-prone modules in different test phases. We investigate the relationships between defects and test phases in order to build defect prediction models for different test phases. We mined the version history of a large-scale enterprise software product to extract churn and static code metrics. We used three testing phases that have been employed by our industry partner, namely function, system and field, to build a learning-based model for each testing phase. We examined the relation of different defect symptoms with the testing phases. We compared the performance of our proposed model with a benchmark model that has been constructed for the entire test phase (benchmark model). Our results show that building a model to predict defect-prone modules for each test phase significantly improves defect prediction performance and shortens defect detection time. The benefit analysis shows that using the proposed model, the defects are detected on the average 7months earlier than the actual. The outcome of prediction models should lead to an action in a software development organization. Our proposed model gives a more granular outcome in terms of predicting defect-prone modules in each testing phase so that managers may better organize the testing teams and effort. Springer Science+Business Media New York, 2014 Software testing nationallicence Testing phase nationallicence Defect prediction nationallicence Caglayan Bora Department of Computer Engineering, Bogazici University, P.K. 2 TR-34342, Bebek, Istanbul, Turkey aut Tosun Misirli Ayse Department of Information Processing Science, Oulu University, Oulu, Finland aut Bener Ayse Data Science Lab, Department of Mechanical and Industrial Engineering, Ryerson University, 350 Victoria Street, M5B 2K3, Toronto, ON, Canada aut Miranskyy Andriy Department of Computer Science, Ryerson University, 350 Victoria Street, M5B 2K3, Toronto, ON, Canada aut Software Quality Journal Springer US; http://www.springer-ny.com 23/2(2015-06-01), 205-227 0963-9314 23:2<205 2015 23 11219 https://doi.org/10.1007/s11219-014-9230-x text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s11219-014-9230-x text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Caglayan Bora Department of Computer Engineering, Bogazici University, P.K. 2 TR-34342, Bebek, Istanbul, Turkey aut NATIONALLICENCE 700 1- Tosun Misirli Ayse Department of Information Processing Science, Oulu University, Oulu, Finland aut NATIONALLICENCE 700 1- Bener Ayse Data Science Lab, Department of Mechanical and Industrial Engineering, Ryerson University, 350 Victoria Street, M5B 2K3, Toronto, ON, Canada aut NATIONALLICENCE 700 1- Miranskyy Andriy Department of Computer Science, Ryerson University, 350 Victoria Street, M5B 2K3, Toronto, ON, Canada aut NATIONALLICENCE 773 0- Software Quality Journal Springer US; http://www.springer-ny.com 23/2(2015-06-01), 205-227 0963-9314 23:2<205 2015 23 11219