An Empirical Study for Software Fault-Proneness Prediction with Ensemble Learning Models on Imbalanced Data Sets

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Provided by: Academy Publisher
Topic: Big Data
Format: PDF
Software faults could cause serious system errors and failures, leading to huge economic losses. But currently none of inspection and verification technique is able to find and eliminate all software faults. Software testing is an important way to inspect these faults and raise software reliability, but obviously it is a really expensive job. The estimation of a module's fault-proneness is important to minimize the software testing resources required by guiding the resource allocation on the high-risk modules. Consequently the efficiency of software testing and the reliability of the software are improved.
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