Abstract
Statistical and genetic programming techniques have been used to predict the software development effort of large software projects. In this paper, a genetic programming model was used for predicting the effort required in individually developed projects. Accuracy obtained from a genetic programming model was compared against one generated from the application of a statistical regression model. A sample of 219 projects developed by 71 practitioners was used for generating the two models, whereas another sample of 130 projects developed by 38 practitioners was used for validating them. The models used two kinds of lines of code as well as programming language experience as independent variables. Accuracy results from the model obtained with genetic programming suggest that it could be used to predict the software development effort of individual projects when these projects have been developed in a disciplined manner within a development-controlled environment.
Highlights
The software process perspectives can be classified as follows [1]: organizations, teams and people
The hypothesis to be investigated in this paper is the following: H1: Effort prediction accuracy of a model based on genetic programming is statistically equal or better than that obtained by a multiple linear regression, when new and changed code, reused code, and programming language experience of developers data obtained from individually developed projects with personal practices are used as independent variables
Estimation and prediction of the software development effort were done based on an accuracy comparison between the models obtained with multiple linear regression (MLR) and genetic programming
Summary
The software process perspectives can be classified as follows [1]: organizations, teams and people. Software development prediction techniques could be classified into the following two general categories: 1) Expert judgment, which implies a lack of analytical argumentation and aims at deriving estimates based on the experience of experts on similar projects; this technique is based on a tacit (intuition-based) quantification step [4]. B) Models based on machine learning techniques such as genetic programming, case-based reasoning, artificial neural networks, decision trees, Bayesian networks, support vector regression, genetic algorithms, and association rules. Among these methods, the application of genetic programming represents only the 3% of the techniques in the software effort prediction field [6]
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