A NOVEL ESTIMATION MODEL FOR AGILE DEVELOPMENT: META HEURISTIC APPROACH

Abstract

Effective software project estimation is one of the most challenging and important activities in software development. Proper project planning and control is not possible without a sound and reliable estimate. As a whole, the software industry doesn’t estimate projects well and doesn’t use estimates appropriately. We suffer far more than we should as a result and we need to focus some effort on improving the situation. Like any other Software paradigm Project estimation in Agile is also a tedious but important task for the software. A large number of formal and informal methods have been proposed for software estimation. The major factor for the project failure is unrealistic estimates which also affects the quality of software. Agile software processes try to minimize the impact of insufficient estimation accuracy by ensuring that the most important functionality is developed first. This is achieved through a flexible development process with short iterations. However, there is still a need for accurate estimates, as these are the basis for staffing, planning, prioritization and contract negotiations. In agile development many methods are proposed in the literature for the cost estimations of software projects. These methods use a large number of factors to estimate the development cost. In this thesis we apply Binary Cuckoo Search so that we can significantly reduce the number of attributes required. The identified attributes have maximum correlation to the development cost of the project. It is also evident from the literature that project estimation must be handled using an evolving system, hence Binary Cuckoo Search is one of the suitable technique for it. For the cost estimation, the concept of Binary Cuckoo Search is adopted where search follows quasi-random manner. The optimal solution obtained from the Binary Cuckoo Search shows that it is far efficient than other metaheuristic techniques like Genetic Algorithm and Particle Swarm optimization.