Clustering and Artificial Neural Network Ensembles Based Effort Estimation

Accurate effort estimation of software development projects plays a key role in project success. However, it is still a challenge activity to researchers and practitioners because of the nature of software products and dynamics in software industry and development environment. Artificial neural network (ANN) is as an effective method and has been widely used in various areas of software engineering. This paper proposes a new effort estimation method based on clustering and ANN ensembles. The contribution of the paper is twofold. First, the impact of clustering projects on the estimation accuracy is investigated. Second, the impact of using ANN ensembles instead of a single ANN is also investigated. The proposed method includes three phases called pre-processing, k-means clustering, and ANN ensembles effort estimation. The method starts with exploring the historical projects dataset. Afterward, k-means is used to cluster the projects. Finally, the proposed method as well as two other estimation methods (i.e. a single ANN and expert-based) were applied to the created clusters and results were compared using MMRE and PRED measures. The simulation results show that the proposed method significantly outperforms the two other estimation methods. Keywords-component; Artifical Neural Network Ensembles; Clustering; K-means; Effort Estimation; Mean Magnitude Relative Error; Percentage of/Predictions.