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Genetic programming as alternative for predicting development effort of individual software projects.
Arturo Chavoya1, Cuauhtemoc Lopez-Martin, Irma R Andalon-Garcia
1Department of Information Systems, CUCEA, Universidad de Guadalajara, Zapopan, Jalisco, Mexico. achavoya@cucea.udg.mx
Plos One
|December 11, 2012
Summary
Genetic programming effectively predicts software development effort for individual projects, outperforming traditional statistical regression. This method is reliable for disciplined, controlled development environments.
Area of Science:
- Software Engineering
- Computational Intelligence
- Data Science
Background:
- Accurate software development effort prediction is crucial for project management.
- Existing statistical models have limitations in predicting effort for individual projects.
Purpose of the Study:
- To evaluate the effectiveness of genetic programming (GP) for predicting software development effort in individual projects.
- To compare the accuracy of a GP model against a statistical regression model.
Main Methods:
- A genetic programming model was developed using data from 219 projects.
- A statistical regression model was also generated using the same dataset.
- Both models were validated on an independent dataset of 130 projects.
- Independent variables included lines of code and programming language experience.
Main Results:
- The genetic programming model demonstrated higher accuracy in predicting software development effort compared to the statistical regression model.
- The GP model's performance indicates its potential for effort prediction in individual projects.
Conclusions:
- Genetic programming offers a viable and accurate approach for predicting software development effort in individual projects.
- The predictive accuracy of the GP model is particularly strong in disciplined, development-controlled environments.
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