Software Effort Estimation and Risk Analysis using Artificial Intelligence Technique

Software effort estimations are based on prediction properties of system with attention to develop methodologies. Many organizations follow the risk management but the risk identification techniques will differ. In this paper, we focus on two effort estimation techniques such as use case point and function point are used to estimate the effort in the software development. The decision table is used to compare these two methods to analyze which method will produce the accurate result. There are number of artificial intelligence techniques useful for training such as neural network, fuzzy logic, ant colony optimization, knowledge based system and genetic algorithm. In this paper, we used the neural network to train the decision table with the use of back propagation training algorithm and compare these two effort estimation methods (use case point and function point) with the actual effort. By using the past project data, the estimation methods are compared. Similarly risk will be evaluated by using the summary of questionnaire received from the various software developers. Based on the report, we can also mitigate the risk in the future process.