Genetic algorithm based hybrid imputation model for software effort estimation

Abstract

One of the most exceptionally important tasks in software development life cycle is to produce precise effort estimations and schedule Developers may use effort estimate as input to project planning, budgeting pricing policies and investment analyses Missing or Incomplete data are exasperation to a certain extent everywhere but especially in effort estimation it is a greater hindrance Over estimation of the software effort can result in newlinelosing the chance to win a bid and underestimation can lead to detrimental effect on the quality of software or monetary loss Accuracy in effort estimation is decisive for developers and customers For a developer it is required to do preplanning budgeting risk analysis and productivity assessment and customers requires estimation for taking place in contract negotiation process The importance of effort estimation is seen on different newlinephases of SDLC in the initial phase it provides some insights on whether to proceed forward or not Rough validation and progress of a project are done in the intermediate phase At the completion phase cost estimates helps to assess productivity The major challenge in Software estimation is mainly concerned about quality of data High degree of inaccuracy is mainly because of missing values The common measure usually adopted in missing values is to ignore the whole project data or its feature newline newline