In this paper we apply a machine learning approach to the problem of estimating the number of defects called Regression via Classification (RvC). RvC initially automatically discr...
Stamatia Bibi, Grigorios Tsoumakas, Ioannis Stamel...
Early estimation of defect density of a product is an important step towards the remediation of the problem associated with affordably guiding corrective actions in the software d...
Mark Sherriff, Nachiappan Nagappan, Laurie A. Will...
We use 63 features extracted from sources such as versioning and issue tracking systems to predict defects in short time frames of two months. Our multivariate approach covers aspe...
Defect prediction is an important task in the mining of software repositories, but the quality of predictions varies strongly within and across software projects. In this paper we...
Jayalath Ekanayake, Jonas Tappolet, Harald Gall, A...
During software development it is helpful to obtain early estimates of the defect density of software components. Such estimates identify fault-prone areas of code requiring furth...