Artificial Neural Networks and Fuzzy Logic for Software Maintenanc Cost Estimation: A Comparative Review Study

Abstract

Maintenance is the last stage of software development life cycle. A software cost estimation model is an indirect measure, which is used to estimate the cost of a project. Maintenance cost is directly determined by the number of people involved in the maintenance process and hours each person invests in the maintenance tasks. Artificial Neural Network is used in cost estimation due to its ability to learn from previous data. In a fuzzy logic tool, values are given as input and output is calculated by using a set of rules defined in rule base and fuzzy operators. This research aims to analyze neural networks and fuzzy logic machine learning techniques for estimating software maintenance cost between the period 2010- 2020 and compare the techniques based on magnitude of relative error (MRE), Mean magnitude of relative error (MMRE) and percentage relative error deviation within x PRED(X) accuracy estimators. Millions of companies expend huge financial resources for development and maintenance of software yet still many projects result in failure causing heavy financial losses. Major reason is the inefficient effort estimation techniques which are not suitable for the current development methods. This paper presents a comparative literature review on software cost estimation for neural networks and fuzzy logic techniques. The evaluation consists of comparing the accuracy of the estimated effort with the actual effort based on Magnitude of Relative Error (MRE), Mean Magnitude of Relative Error (MMRE and PRED(x). The findings show artificial neural networks provide efficient results when dealing with problems of complex relationship between inputs and outputs. Fuzzy logic-based cost estimation models are more appropriate when vague and imprecise information is to be accounted for. Neither neural networks nor fuzzy logic techniques should be used in isolation but rather a combination of the two technique should be used to arrive at accurate cost estimate.