Software Quality Management for open Source Software Based on Louvain Parallelization Heuristic and Greedy Discritized Optimization
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Abstract
Designing reliable software product is becoming more difficult as software becomes ubiquitous and is deployed on software quality management. Software quality detection has become the fundamental operation. Any changes made in the source lines of codes of software products has an adverse effect on the long run and therefore compromising the scalability and reliability of software product users. Few research works have been introduced in existing work for software quality prediction using various data mining techniques. But, scalability and reliability of software quality management was not enough. Besides to that, the time required by the conventional research work for performing the service provisioning was too high. For that reason, the proposed research work is concentrated to address these issues by providing the higher scalability and reliability with minimum amount of time consumption for service provisioning during the software quality management process. Thus, the research work introduces the proposed Louvain Parallelization Heuristic Based Greedy Discritized Optimization (LPH-GDO) Model for performing fast software quality prediction in a significant manner. In this present work, the experimental evaluation of LPH-GDO Model has been conducted on metrics such as scalability, service provisioning time and software reliability with respect to different size of software program code.
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