Use of a Feedforward Neural Network for Predicting the Development Duration of Software Projects
| Autor: | Cuauhtemoc Lopez-Martin, M. E. Meda-Campaña, Arturo Chavoya |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
business.industry
Computer science Software development Context (language use) Benchmarking Machine learning computer.software_genre Function point Software Feedforward neural network Artificial intelligence Data mining Project management Duration (project management) business computer Statistical hypothesis testing |
| Zdroj: | ICMLA (2) |
| DOI: | 10.1109/icmla.2013.182 |
| Popis: | Context: In the software engineering field, only 20 percent of software projects finish on time relative to their original plan. A software project can be classified as a new development, an enhanced development or a re-development. Goal: To propose a feed forward neural network (FFNN) for predicting the duration of new software development projects. Hypothesis: The accuracy of duration prediction for an FFNN is statistically better than the accuracy obtained from a statistical regression (SR) when an adjusted function points (AFPs) value, obtained from new software development projects, is used as the independent variable. Method: A sample obtained from the International Software Benchmarking Standards Group (ISBSG) Release 11 corresponding to new development projects was used. The accuracy of the FFNN was compared against that of an SR model. The criteria for evaluating the accuracy of these two models were the Mean Magnitude of Relative Error (MMRE) and an ANOVA statistical test. Results: Prediction accuracy of an FFNN was statistically better than that of an SR model at the 90% confidence level. Conclusion: An FFNN could be applied for predicting the duration of new software development projects when AFPs were used as independent variable. |
| Databáze: | OpenAIRE |
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