Optimum Time Schedule and Staff Prediction Model for SQA

Abstract

Software Quality Assurance is the process of ensuring the explicitly stated functional and performance requirements of the software. In this paper, an efficient Optimum Time Schedule and Staff Prediction model is proposed, for optimizing the SQA process. Initially, two datasets, such as, product modules dataset and cost driver dataset are provided as input. The suggested model pre-processes these inputs based on the complexity parameters and driver parameters. The complexity factors derived from the cost driver input and the function count derived from the product modules input are used for estimating the Function Point. Based on the estimated FP, the project effort, project schedule, and the number of staffs are determined.  To prove the superiority of the suggested model, it is compared against the existing models for the metrics, such as, effort, prediction accuracy, Mean Magnitude of Relative Error, and Balanced MMRE. The comparison results prove that, when compared to the existing models, the suggested model provides optimal results for all the metrics.
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