A Novel Data Analytics Method for Predicting the Delivery Speed of Software Enhancement Projects

Elías Ventura-Molina, Cuauhtémoc López-Martín, Itzamá López-Yáñez and Cornelio Yáñez-Márquez
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Elías Ventura-Molina: Centro de Innovación y Desarrollo Tecnológico en Cómputo, Instituto Politécnico Nacional, Ciudad de México 07700, Mexico
Cuauhtémoc López-Martín: Department of Information Systems, Universidad de Guadalajara, Zapopan, Jalisco 45100, Mexico
Itzamá López-Yáñez: Centro de Innovación y Desarrollo Tecnológico en Cómputo, Instituto Politécnico Nacional, Ciudad de México 07700, Mexico
Cornelio Yáñez-Márquez: Centro de Investigación en Computación, Instituto Politécnico Nacional, Ciudad de México 07738, Mexico

Mathematics, 2020, vol. 8, issue 11, 1-22

Abstract: A fundamental issue of the software engineering economics is productivity. In this regard, one measure of software productivity is delivery speed. Software productivity prediction is useful to determine corrective activities, as well as to identify improvement alternatives. A type of software maintenance is enhancement. In this paper, we propose a data analytics-based software engineering algorithm called search method based on feature construction (SMFC) for predicting the delivery speed of software enhancement projects. The SMFC belongs to the minimalist machine learning paradigm, and as such it always generates a two-dimensional model. Unlike the usual data analytics methods, SMFC includes an original algorithmic training procedure, in which both the independent and dependent variables are considered for transformation. SMFC prediction performance is compared to those of statistical regression, neural networks, support vector regression, and fuzzy regression. To do this, seven datasets of software enhancement projects obtained from the International Software Benchmarking Standards Group (ISBSG) Release 2017 were used. The validation method is leave-one-out cross validation, whereas absolute residuals have been chosen as the performance measure. The results indicate that the SMFC is statistically better than statistical regression. This fact represents an obvious advantage in favor of SMFC, because the other two methods are not statistically better than SMFC.

Keywords: data analytics; software enhancement projects; delivery speed prediction; feature construction; search methods; simulated annealing; ISBSG (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
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