A Novel UCP Model Based on Artificial Neural Networks and Orthogonal Arrays
Source abstract
Abstract
Adequate estimation is a crucial factor for the implementation of software projects within set customer requirements. The use of Case Point Analysis (UCP) is the latest and most accurate method for estimating the effort and cost of realizing software products. This paper will present a new, improved UCP model constructed based on two different artificial neural network (ANN) architectures based on Taguchi Orthogonal Vector Plans. ANNs are an exceptional artificial intelligence tool that have been proven to be reliable and stable in this area of software engineering. The Taguchi method of Orthogonal Vector Plans is an optimization method that reduces the number of iterations required, which significantly shortens estimation time. The goal is to construct models that give a minimum magnitude relative error (MRE) value concerning previous approaches and techniques. A minimum number of iterations (less than six) and a minimum value of MMRE (less than 10%) have been achieved. The obtained results significantly improve the accuracy and reliability of estimating the effort and cost involved in the implementation of software projects.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Manufacturing Process and Optimization · Software Engineering Research · Software Reliability and Analysis Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Computing, Union University, 11000 Belgrade, Serbia
Faculty of Sciences, University of Novi Sad, 21000 Novi Sad, Serbia
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