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Class level fault prediction using software clustering
2013
2013 28th IEEE/ACM International Conference on Automated Software Engineering (ASE)
Defect prediction approaches use software metrics and fault data to learn which software properties associate with faults in classes. Existing techniques predict fault-prone classes in the same release (intra) or in a subsequent releases (inter) of a subject software system. We propose a intrarelease fault prediction technique, which learns from clusters of related classes, rather than from the entire system. Classes are clustered using structural information and fault prediction models are
doi:10.1109/ase.2013.6693126
dblp:conf/kbse/ScannielloGMM13
fatcat:2fkvmxqnlfgpdd433yl2pbenxm