Empirical evaluation of fuzzy analogy for Software Development Effort Estimation

Software Development Effort Estimation (SDEE) plays a primary role in software project management. Among several techniques suggested for estimating software development effort, analogybased software effort estimation approaches stand out as promising techniques. In this paper, the performance of Fuzzy Analogy is compared with that of six other SDEE techniques (Linear Regression, Support Vector Regression, Multi-Layer Perceptron, M5P and Classical Analogy). The first step of the evaluation aimed to ensure that the SDEE techniques outperformed random guessing by using the Standardized Accuracy (SA). Then, we used a set of reliable performance measures and Borda count to rank them and identify which techniques are the most accurate. The results suggest that Fuzzy Analogy statistically outperformed the other SDEE techniques regardless of the dataset used. © 2017 ACM.