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Authors
Affiliations
1 Department of Computer Science and Engineering, Annamalai University, IN
2 Department of Computer Science and Engineering, Shirdi Sai Engineering College, IN
Source
ICTACT Journal on Soft Computing, Vol 3, No 3 (2013), Pagination: 524-533
Abstract
The Authors have proposed a model that first captures the fundamentals of software metrics in the phase 1 consisting of three primitive primary software engineering metrics; they are person-months (PM), function-points (FP), and lines of code (LOC). The phase 2 consists of the proposed function point which is obtained by grouping the adjustment factors to simplify the process of adjustment and to ensure more consistency in the adjustments. In the proposed method fuzzy logic is used for quantifying the quality of requirements and is added as one of the adjustment factor, thus a fuzzy based approach for the Enhanced General System Characteristics to Estimate Effort of the Software Projects using productivity has been obtained. The phase 3 takes the calculated function point from our work and is given as input to the static single variable model (i.e. to the Intermediate COCOMO and COCOMO II) for cost estimation. The Authors have tailored the cost factors in intermediate COCOMO and both; cost and scale factors are tailored in COCOMO II to suite to the individual development environment, which is very important for the accuracy of the cost estimates. The software performance indicators are project duration, schedule predictability, requirements completion ratio and post-release defect density, are also measured for the software projects in my work. A comparative study for effort, performance measurement and cost estimation of the software project is done between the existing model and the authors proposed work. Thus our work analyzes the interactional process through which the estimation tasks were collectively accomplished.
Keywords
Effort Adjustment Factor (EAF), Kilo Lines of Code (KLOC), Total Effort Multiplier (TEM), Scale Factors, Cost Drivers, Value Added Factors.
