These methods are Genetic algorithm13, Genetic programming14, Differential Evolution15, Particle Swarm Optimization16 and many others. These methods can be used in the software effort estimation also. Various heuristic optimization methods are used in optimization problems. Heuristic techniques are used to overcome the limitation of these methods and improve the applicability. Currently, many issues have arisen regarding the applicability of these methods to solve the software effort estimation. Some researchers used Cognitive Information Complexity Measure (CICM)11'12 for effort estimation. Early stage effort estimation methods are Improved Requirement Based Complexity (IRBC)8, Requirement Based Software Development Effort Estimation (RBDEE)9'10 and many others. The analogy based method is applied at a very early phase of SDLC. Algorithmic methods are Constrictive Cost Model (COCOMO)4, Function Point (FP)5 method, Software Life Cycle Management (SLIM)6 and Software Evaluation and Estimation of Resources-Software Estimating Model (SEER-SEM)7. Work Breakdown Structure (WBS) method and Delphi technique3. Organic Model Semi-Detached Model Embedded ModelÄ® = 2.4 (KLOC) E = 3.0 (KLOC) E = 3.6 (KLOC) Peer-review under responsibility of organizing committee of the Organizing Committee of IMCIP-2016 doi:10.1016/j.procs.2016.06.107
BASIC COCOMO MODEL IN SOFTWARE ENGINEERING LICENSE
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These methods are categorised into Expert judgement, Algorithmic method and Analogy based method. Several effort estimation methods have been proposed and improved by many researchers in the past. Hence, there is a requirement of a more realistic and reliable software effort estimation model. According to another report2, 70-80% software projects were above their estimated plan and that average overrun is about 30-40%. According to Standish Group Report1, in UK during the period of 2002-2003, out of 13522 projects only 33% projects were completed within time and budget, 82% projects were late, 43% projects were overrun their financial plan and 20% projects were cancelled. This effort is used for project plans, project budgets, investment analysis, resource allocation schemes, pricing process, etc. It is calculated in term of person-month. It is a method for computing the most realistic and reliable value of required effort for developing or developed project. It is also used for on time and within budget delivery of software. Effort estimation is used for planning, budgeting and monitoring the activities of software development. Software effort estimation (SEE) has been an essential and crucial activity for the software development life cycle (SDLC).
Keywords: COCOMO Model Effort Estimation Genetic Algorithm Nature-inspired Algorithm Optimization. Peer-reviewunder responsibilityof organizing committee of the Organizing Committee of IMCIP-2016 Motilal Nehru National Institute of Technology Allahabad, Allahabad 211 004, India Rohit Kumar Sachan*, Ayush Nigam, Avinash Singh, Sharad Singh, Manjeet Choudhary, Avinash Tiwari and Dharmender Singh Kushwaha Optimizing Basic COCOMO Model using Simplified Genetic Twelfth International Multi-Conference on Information Processing-2016 (IMCIP-2016) Experimental results show better realistic estimation over the basic COCOMO. The proposed approach is applied on NASA software project dataset. A simplified genetic algorithm is used for optimizing the parameters of the basic COCOMO model. In this research paper, a simplified genetic algorithm based model is proposed. Many researchers have proposed various models for software effort estimation, such as statistical models, algorithmic models, machine learning based models and nature inspired models in the past. Therefore a realistic effort estimation is required. In industry, effort is used for planning, budgeting and development time calculation. In recent years, many researchers and software industries have given significant attention on the estimation of software effort. The estimation of software effort is an essential and crucial activity for the software development life cycle. Abstract of research paper on Economics and business, author of scientific article - Rohit Kumar Sachan, Ayush Nigam, Avinash Singh, Sharad Singh, Manjeet Choudhary, et al.