A Study Of Software Project Schedule Monitoring Based On Improved Earned Value Method

Although the software industry has been developing rapidly worldwide, the problem of the high failure rate is also growing behind the rapid development. As one of the main reason for the failure of software projects, schedule slippage has been widely criticized. The project manager has an increasingly high demand on the validity and accuracy of the schedule monitoring method. Earned value management is a very important method in monitoring project schedule and cost. It has made a good application effect in many industries. However, the application of the method in software projects has not yet been generally recognized. Based on the in-depth analysis of the obstacles in using traditional earned value method and the improved method of software projects, this paper gives a solution and proposes a method suitable for software projects. This method can provide more in-depth and reliable schedule information for project manager to make quantitative decision and extended application range of earned value method.First, this paper analyzes the limitation of the earned value method and ES method as well as the barriers of the application in software project based on its characteristics. Second, this paper proposes specific processes combined MK II Function Point Analysis with earned value method to solve the source of basic data. This paper also introduces the coefficient of the implementation of the project schedule to amend the performance indices in solving the problem of high rework and frequent changes in the demand of software project. Then, this paper amends the prediction formula of schedule combined with Network Planning Technology, and uses it to monitor the critical path of the project. Finally, combined with management reserve, this paper proposes a quantitative decision-making model. It can help project manager to take proper action to correct schedule and cost variance. And taking into account the cost of the corrective action, this paper also proposes an adjustment cost optimization model.