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Software mining and fault prediction
Author(s) -
Catal Cagatay
Publication year - 2012
Publication title -
wiley interdisciplinary reviews: data mining and knowledge discovery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.506
H-Index - 47
eISSN - 1942-4795
pISSN - 1942-4787
DOI - 10.1002/widm.1067
Subject(s) - software deployment , computer science , software , fault (geology) , data mining , software engineering , data science , operating system , seismology , geology
Abstract Mining software repositories (MSRs) such as source control repositories, bug repositories, deployment logs, and code repositories provide useful patterns for practitioners. Instead of using these repositories as record‐keeping ones, we need to transform them into active repositories that can guide the decision processes inside the company. By MSRs with several data mining algorithms, effective software fault prediction models can be built and error‐prone modules can be detected prior to the testing phase. We discuss numerous real‐world challenges in building accurate fault prediction models and present some solutions to these challenges. © 2012 Wiley Periodicals, Inc. This article is categorized under: Application Areas > Science and Technology