A Novel Software Reliability Prediction Algorithm Using Fuzzy Attribute Clustering and Naïve Bayesian Classification - Rastogi, Neeta; Rastogi, Shishir; Darbari, Manuj (International Journal of Computer Sciences and Engineering, 2019)
In current times, ubiquitous computing has given massive rise to research work in artificial intelligence, machine learning, software engineering and to research development in telecommunication, medicine, and image / audio / video processing. Due to the vastness of software being developed, software fault prediction is a very pertinent area for ensuring software quality and has so much scope to work. Machine learning now a days is one of the most promising way to deal with software fault prediction problems. The assumptions considered in a testing case need to be different from those in other testing cases because of the varying complexity of software testing. Although, there are software fault prediction models who can effectively assess software reliability in specific testing scenarios, no single model can accurately predict the fault numbers in a software in all testing conditions due to the fact that the modern software being developed are bigger and complex in both size and functions and thus, assessing the software reliability is a daunting task. Some popular approaches of Software fault prediction models use General Bayesian network and Augmented Naive Bayes classifiers, which do not impose any restriction on network architecture and are able to learn appropriate network architecture. An algorithm combining Fuzzy Attribute Clustering with Naive Bayes Classification has been worked out in this paper. The proposed Fuzzy Attribute Cluster Net Bayes (FACNB) algorithm is a machine learning-based prediction algorithm for software reliability prediction (using soft computing methods). It focusses on all data types in the area of software analytics. The prediction accuracy of the proposed algorithm shows improvement over other such algorithms.