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