Stochastic Embedded Probit Regressive Reweight Boost Classifier for Software Quality Examination
Noor Ayesha1, Yethiraj N G2
1Noor Ayesha, Research Scholar, Department of Computer Science, Bharathiar University, Coimbatore (Tamil Nadu), India.
2Yethiraj N G, Assistant Professor, Department of Computer Science, Maharani’s Science College for Women, Bengaluru (Karnataka), India.
Manuscript received on 24 November 2019 | Revised Manuscript received on 05 December 2019 | Manuscript Published on 16 December 2019 | PP: 111-120 | Volume-8 Issue-3S3 November 2019 | Retrieval Number: C10401183S319/2019©BEIESP | DOI: 10.35940/ijrte.C1040.1183S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In software development, Software quality analysis plays a considerable process. Through the software testing, the quality analysis is performed for efficient prediction of defects in the code. Due to the complicated structure of software projects, code examination has become a demanding issue that has to be addressed at the initial stage of testing for achieving the quality improved results. In order to resolve these issues, the Stochastic Gaussian Neighbor Embedding based Probit Regressive Reweight Boost Classification (SGNE-PRRBC) is introduced for accurate quality prediction system through code examination proficient system. The SGNE-PRRBC technique considers the number of program files as input for software quality analysis through feature selection and classification. Initially, the number of program files is taken from the dataset (DS). After collecting the files, the Gaussian distributive stochastic neighbor embedding technique choose the features (i.e. code metrics) based on the distance similarity. With the assist of Pearson correlative probit regressed reweight boost technique, the classification of program files is performed. The boosting algorithm creates ‘m’ number of weak classifiers i.e. Pearson correlative probit regression to categorize the input program files as normal or defected through analyze the source codes and chosen metrics. After that, the weak learners results are combined into strong through minimizing the out of sample error with gradient descent function. This enhances the accuracy of quality prediction and lessens the false positive rate (FPR). Experimental analysis is performed with various metrics namely accuracy, FPR and computation time (CT) with number of program files. Experimental results evident that the SGNE-PRRBC technique achieves better performance in terms of accuracy, CT and FPR as compared to the conventional methods.
Keywords: Software Quality Analysis, Software Testing, Software Metrics, Gaussian Distributive Stochastic Neighbor Embedding Technique, Feature Selection, Pearson Correlative Probit Regressed Reweight Boosting, Classification.
Scope of the Article: Systems and Software Engineering