Predictive accuracy comparison of fuzzy models for software development effort of small programs
Regression analysis to generate predictive equations for software development effort estimation has recently been complemented by analyses using less common methods such as fuzzy logic models. On the other hand, unless engineers have the capabilities provided by personal training, they cannot properly support their teams or consistently and reliably produce quality products. In this paper, an investigation aimed to compare personal Fuzzy Logic Models (FLM) with a Linear Regression Model (LRM) is presented. The evaluation criteria were based mainly upon the magnitude of error relative to the estimate (MER) as well as to the mean of MER (MMER). One hundred five small programs were developed by thirty programmers. From these programs, three FLM were generated to estimate the effort in the development of twenty programs by seven programmers. Both the verification and validation of the models were made. Results show a slightly better predictive accuracy amongst FLM and LRM for estimating the development effort at personal level when small programs are developed.
fuzzy logic linear regression software engineering education software effort estimation personal software process