We propose an intra-release fault prediction technique, which learns from clusters of related classes, rather than from the entire system. Classes are clustered ...
Abstract—Defect prediction approaches use software metrics and fault data to learn which software properties associate with faults in classes.
We propose an intra-release fault prediction technique, which learns from clusters of related classes, rather than from the entire system. Classes are clustered ...
An intra-release fault prediction technique, which learns from clusters of related classes, rather than from the entire system, is proposed, which indicates ...
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We propose an intra-release fault prediction technique, which learns from clusters of related classes, rather than from the entire system. Classes are clustered ...
In this paper, a framework towards subspace grouping of large data set was pro-posed at class level to minimize the error. We composed an iterative calculation ...
Earlier the authors introduced a new fault prediction model based on subspace clustering and Step- wise Linear Regression (SWLR). Though the prediction of ...
... Mikyeong Park et all [9] developed an unsupervised fault prediction model using clustering algorithms which select the number of clusters without experts.
In this paper we our focus is at classification level rather than attribute level. We have proposed a new model to predict the software fault more accurately.
Abstract: Detection of software defective modules is important for reducing the time and resources consumed by soft- ware testing. Software defect data sets ...