Enhancing Software Quality through Dynamic Optimization-Based Latent Dirichlet Allocation and Indexive Regression (DOLDIR)

Nasurulla I; Chennappan  Rajendran; Subash Chandra  Bose; Ramadevi K

doi:10.30572/2018/KJE/170209

Authors

Nasurulla I Department of MCA, VEMU Institute of Technology, Chittoor District 517112, Andhra Pradesh, India
Chennappan Rajendran Department of Computer Applications, Karpagam Academy of Higher Education, Coimbatore, India
Subash Chandra Bose Department of Computer Applications, Karpagam Academy of Higher Education, Coimbatore, India
Ramadevi K Department of Information technology, Panimalar engineering college, Chennai, India

DOI:

https://doi.org/10.30572/2018/KJE/170209

Keywords:

Software quality, Scalability, Software Reliability, Optimization

Abstract

This paper proposes DOLDIR, a Dynamic Online Learning, based Defect Identification and Reliability framework, that helps to predict software quality and risk much more effectively. Positioned as an alternative to existing static online reliability models, DOLDIR is able to take into account that the changes in software version through detection of feature drift, optimal selection of test cases, latent topic modelling and combination of reliability scores is possible. The algorithm devised used cosine similarity to trace feature drift in consecutive software versions, Latent Dirichlet Allocation (LDA) to model the failure density and a combined reliability score which is a function of previous reliability, structural attributes of the software modules and training failure indices. Extensive experiments have been carried out using standard benchmark datasets like NASA PROMISE and SoftRel Bench. The effectiveness of the proposed framework has been tested using DOLDER and Reduckum, and experimental results in terms of RMSE, MAE and prediction accuracy proved that DOLDIR outperform the models. The classification accuracy for failure risk, reached 92.8%. DOLDIR has proved to be better than six recently published models in a cross examination

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