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Measuring the Utility of Functional-Based Software Using Centroid-Adjusted Class Labelling

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Computational Intelligence and Quantitative Software Engineering

Part of the book series: Studies in Computational Intelligence ((SCI,volume 617))

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Abstract

The functional programming paradigm involves stateless computation on immutable data constructs. While this paradigm’s historical context dates back to the early twentieth century with lambda calculus and a formal study of computability and function definition, there has been a resurgence in functional programming, especially in the area of predictive analytics. New, purely functional, languages have recently emerged, and functional extensions have been added to several popular programming languages. It is sometimes difficult to estimate the overall utility and extensibility of functional programming software components. At the same time, many software metrics exist that attempt to quantify various qualitative attributes of software components. Here, we use a computational intelligence strategy that uses a set of software metrics to predict the qualitative utility of a software system’s underlying components. Centroid-adjusted class labelling is a pattern classification preprocessing method that compensates for the possible imprecision of an established external reference test (gold standard) by adjusting, when necessary, design pattern class labels while maintaining the reference test’s discriminatory power. The adjusted design labels incorporate within-class centroid information using robust measures of location and dispersion. This method is applied to a biomedical data analysis software system written in a functional programming style. It is shown that significant improvement to the discriminatory power of the classifier is obtained when using this preprocessing method.

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Acknowledgment

The Natural Sciences and Engineering Research Council of Canada (NSERC) is gratefully acknowledged for its support of this investigation.

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Authors and Affiliations

  1. InfoMagnetics Technologies Corporation, Research and Technology Development, Winnipeg, MB, R3C 3Z5, Canada

    Nick J. Pizzi

  2. Department of Computer Science, University of Manitoba, Winnipeg, MB, R3T 2N2, Canada

    Nick J. Pizzi

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Correspondence to Nick J. Pizzi .

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  1. Department of Electrical and Computer En, University of Alberta, Edmonton, Alberta, Canada

    Witold Pedrycz

  2. Innopolis University, Bolzano, Italy

    Giancarlo Succi

  3. Center for Applied Software Engineering, Bolzano, Italy

    Alberto Sillitti

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Pizzi, N.J. (2016). Measuring the Utility of Functional-Based Software Using Centroid-Adjusted Class Labelling. In: Pedrycz, W., Succi, G., Sillitti, A. (eds) Computational Intelligence and Quantitative Software Engineering. Studies in Computational Intelligence, vol 617. Springer, Cham. https://doi.org/10.1007/978-3-319-25964-2_6

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  • DOI: https://doi.org/10.1007/978-3-319-25964-2_6

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