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Source code size prediction using use case metrics: an empirical comparison with use case points.

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Název: Source code size prediction using use case metrics: an empirical comparison with use case points.
Autoři: Badri, Mourad, Badri, Linda, Flageol, William, Toure, Fadel
Zdroj: Innovations in Systems & Software Engineering; Sep2017, Vol. 13 Issue 2/3, p143-159, 17p
Abstrakt: Software source code size, in terms of source lines of code (SLOC), is an important parameter of many parametric software development effort estimation methods. In this paper, we investigate empirically the early prediction of SLOC for object-oriented software using use case metrics. We used different modeling techniques to build the prediction models. We used the univariate logistic regression and the simple linear regression methods to evaluate the individual effect of each use case metric on SLOC, and the multivariate logistic regression and the multiple linear regression methods to explore the combined effect of the use case metrics on SLOC. We also used in the study different machine learning methods ( k-NN, naïve Bayes, C4.5, random forest, and multilayer perceptron neural network). The prediction models were evaluated using the receiver operating characteristic analysis, particularly the area under the curve measure, and leave-one-out cross validation. An empirical study, using data collected from five open source Java projects, is reported in the paper. The use case metrics have been compared to the well-known use case points method. Results provide evidence that the use case metrics-based approach gives a more accurate prediction of SLOC than the use case points-based approach. [ABSTRACT FROM AUTHOR]
Copyright of Innovations in Systems & Software Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Source code size prediction using use case metrics: an empirical comparison with use case points.
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  Data: Innovations in Systems & Software Engineering; Sep2017, Vol. 13 Issue 2/3, p143-159, 17p
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Software source code size, in terms of source lines of code (SLOC), is an important parameter of many parametric software development effort estimation methods. In this paper, we investigate empirically the early prediction of SLOC for object-oriented software using use case metrics. We used different modeling techniques to build the prediction models. We used the univariate logistic regression and the simple linear regression methods to evaluate the individual effect of each use case metric on SLOC, and the multivariate logistic regression and the multiple linear regression methods to explore the combined effect of the use case metrics on SLOC. We also used in the study different machine learning methods ( k-NN, naïve Bayes, C4.5, random forest, and multilayer perceptron neural network). The prediction models were evaluated using the receiver operating characteristic analysis, particularly the area under the curve measure, and leave-one-out cross validation. An empirical study, using data collected from five open source Java projects, is reported in the paper. The use case metrics have been compared to the well-known use case points method. Results provide evidence that the use case metrics-based approach gives a more accurate prediction of SLOC than the use case points-based approach. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Innovations in Systems & Software Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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