Software Project Effort Duration and Cost Estimation using Regression Testing and Adaptive Firefly Algorithm (AFA)
B.M.G.Prasad1, P.V.S. Sreenivas2, C.Veena3
1B.M. G. Prasad, Research Scholar, Department of Computer Science and Engineering, PP.COMP.SCI&ENG.0064C Rayalaseema University Kurnool  (Andhra Pradesh), India.
2Dr. P.V.S. Sreenivas, Professor, Department of Computer Science and Engineering, Sreenidhi Institute of Science, & Technology Hyderabad (Telangana), India.
3C. Veena, Assistant Professor, Department of Computer Science and Engineering, Holy Mary Institute of Technology & Science Hyderabad (Telangana), India.
Manuscript received on 10 January 2019 | Revised Manuscript received on 20 January 2019 | Manuscript Published on 30 January 2019 | PP: 104-111 | Volume-8 Issue-2S2, January 2019 | Retrieval Number: B10230182S219/19©BEIESP
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Abstract: Software project estimation is the process of defining the quality of the software projects in terms of effort, duration and cost. The better software construction should take less effort with ensured short duration and cost. Estimating the software project EDC estimation is a more difficult task which is focused by various researchers. COCOMO is found to be most successful software project cost estimation model. However this research work requires complete information for the decision making. And also this research method would lead to more computational overhead for the decision making process. This is focused and resolved in the proposed research methodology by introducing the new software EDC estimation process namely Regression Testing based Software EDC Estimation Technique (RTSEDCET). The experimental analysis is carried out on two datasets namely NASA 93 and COCOMO datasets. The proposed regressing testing model would generate the various test cases by comparing the attribute values of these datasets to predict the quality of the software in terms of effort, duration and cost. Based on these test case values, software project estimation can be done efficiently. In this work, adaptive firefly algorithm is utilized for the efficient test case generation which would combine the multiple attributes of the dataset to generation optimal test cases with the concern of ranking. The overall evaluation of the research work is conducted on the java simulation environment from which it is proved that the proposed research technique leads to ensure the optimal outcome than the existing research techniques.
Keywords: Project Quality, Effort, Duration, Cost, Regression Testing, Multi Attribute Merging.
Scope of the Article: Systems and Software Engineering