An improved analogy-rule based software effort estimation using HTRR-RNN in software project management

Abstract

To complete a software project, the most realistic effort required is predicted by the process called Software Effort Estimation (SEE). Effort Estimation (EE) in software development is considered a complicated task owing to the variability of variables. Thus, numerous Software Development EE (SDEE) models have been developed; nevertheless, these models' accuracy rates are not good enough, and the variations among software functionalities were not focused for SEE. Therefore, by considering it as the research gap in Software Project Management (SPM), an enhanced analogy-rule-centric SEE utilizing Hyperbolic Tangent Recurrent Radial − Recurrent Neural Networks (HTRR-RNN) algorithm is proposed in this work. Initially, regarding the project size, the projects are partitioned. Next, the highly vital features are extracted as of the separated projects. By employing Levy Flight-Rock Hyraxes Swarm (LH-RHS) optimization, the selective quantity of features is chosen from the extracted features. By deploying the Mamdani Method − Fuzzy (MM-Fuzzy) algorithm, the rules are created regarding the selected features. Lastly, the number of efforts needed for the software development process is determined by the classification algorithm regarding the rules being created. COCOMO81, MAXWELL, and China are the openly accessible datasets utilized in this methodology. Then, the proposed method’s results are contrasted with the prevailing algorithms. As per the experimental results, the proposed methodology performs SEE with a better accuracy rate; thus, it surpassed the other existing methodologies.