Date of Award
Master of Engineering Science
Electrical and Computer Engineering
Dr. Luiz F. Capretz
Mr. Danny Ho
Software effort estimation is a critical part of software engineering. Although many techniques and algorithmic models have been developed and implemented by practitioners, accurate software effort prediction is still a challenging endeavor. In order to address this issue, a novel soft computing framework was developed by previous researchers. Our study utilizes this novel framework to describe an approach combining the neuro-fuzzy technique and the System Evaluation and Estimation of Resource Software Estimation Model (SEER-SEM) effort estimation algorithm. By introducing the neuro-fuzzy technique, this proposed model utilizes positive characteristics such as learning ability, decreased sensitivity, effective generalization, and knowledge integration. Moreover, our study assesses the performance of the proposed model by designing and conducting evaluation with published project and industrial data. After analyzing the performance of our model in comparison to the SEER-SEM effort estimation algorithm alone, the proposed model demonstrates the ability of improving the estimation accuracy, especially in its ability to reduce the large Magnitude of Relative Error (MRE). Furthermore, the results of this research also indicate that the general neuro-fuzzy framework can function with various algorithmic models for improving the performance of software effort estimation.
Du, Wei Lin, "A NEURO-FUZZY MODEL WITH SEER-SEM FOR SOFTWARE EFFORT ESTIMATION" (2009). Digitized Theses. 3777.