Software projects that are over-budget, delivered late, and fall short of usersa��A�expectations have been a challenge in software engineering for decades. TheA�success or failure of a software project heavily depends on the accuracy of effortA�estimation. The software project cost is primarily estimated based on effort whichA�is defined as the time taken by the software development team members forA�individual tasks completion. Therefore, accurate effort estimation has gainedA�highest importance due to exponential growth of large scale software applications.
This research contributes by presenting a novel approach for effort estimation inA�a�?Agile Software developmenta�� (ASD). In ASD, changes in customer requirementsA�are proactively incorporated while delivering software projects within budget andA�time. We shall formulate effort estimation as the search-based problem and useA�computational intelligence techniques, such as evolutionary algorithms, to addressA�following limitations in the current research for agile effort estimation.
- Datasets used for effort estimation contain single company projects data. WeA�will use cross-company data to validate our model.
- Other than scrum and XP no other agile method was investigated. We will useA�KANBAN agile method in our research.
- We will be first to use line of code (LOC) as size metric.A�The benefit of this research is that it will reduce the risk of software project fallingA�behind schedules by providing realistic estimation figures