Multicriteria Cuckoo search optimized latent Dirichlet allocation based Ruzchika indexive regression for software quality management

R. Chennappan, Vidyaa Thulasiraman


The paper presents the software quality management is a highly significant one to ensure the quality and to review the reliability of software products. To improve the software quality by predicting software failures and enhancing the scalability, in this paper, we present a novel reinforced Cuckoo search optimized latent Dirichlet allocation based Ruzchika indexive regression (RCSOLDA-RIR) technique. At first, Multicriteria reinforced Cuckoo search optimization is used to perform the test case selection and find the most optimal solution while considering the multiple criteria and selecting the optimal test cases for testing the software quality. Next, the generative latent Dirichlet allocation model is applied to predict the software failure density with selected optimal test cases with minimum time. Finally, the Ruzchika indexive regression is applied for measuring the similarity between the preceding versions and the new version of software products. Based on the similarity estimation, the software failure density of the new version is also predicted. With this, software error prediction is made in a significant manner, therefore, improving the reliability of software code and service provisioning time between software versions in software systems is also minimized. An experimental assessment of the RCSOLDA-RIR technique achieves better reliability and scalability than the existing methods.


Generative latent dirichlet allocation model; Multicriteria reinforced cuckoo search optimization; Ruzchika indexive regression; Software quality management;

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