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Accuracy Comparison of Empirical Studies on Software Product Maintainability Prediction

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Trends and Advances in Information Systems and Technologies (WorldCIST'18 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 746))

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Abstract

Software maintainability is a very broad activity which ensures that the software product fulfills its changing requirements and enhancement capabilities once on the client side. Predicting software product maintainability contributes to the reduction of software product maintenance costs. In this perspective, many software product maintainability prediction (SPMP) techniques have been proposed in the literature. Some studies have empirically validated their proposed techniques while others have compared the accuracy of the SPMP techniques. This paper reviews a set of 29 studies, which are identified from eight digital libraries and collected from 2000 to 2017. The present paper is targeted to present the various SPMP techniques used and reveals all about the experimental design of these studies.

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Authors and Affiliations

  1. Software Project Management Research Team, ENSIAS, Mohammed V University, Rabat, Morocco

    Sara Elmidaoui, Laila Cheikhi & Ali Idri

Authors
  1. Sara Elmidaoui
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  2. Laila Cheikhi
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  3. Ali Idri
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Corresponding author

Correspondence to Laila Cheikhi .

Editor information

Editors and Affiliations

  1. Departamento de Engenharia Informática, Universidade de Coimbra, Coimbra, Portugal

    Álvaro Rocha

  2. College of Engineering, The Ohio State University, Columbus, Ohio, USA

    Hojjat Adeli

  3. DSI/EEUM, Universidade do Minho, Guimarães, Portugal

    Luís Paulo Reis

  4. DIMES, Universita della Calabria, Arcavacata di Rende, Italy

    Sandra Costanzo

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Elmidaoui, S., Cheikhi, L., Idri, A. (2018). Accuracy Comparison of Empirical Studies on Software Product Maintainability Prediction. In: Rocha, Á., Adeli, H., Reis, L., Costanzo, S. (eds) Trends and Advances in Information Systems and Technologies. WorldCIST'18 2018. Advances in Intelligent Systems and Computing, vol 746. Springer, Cham. https://doi.org/10.1007/978-3-319-77712-2_3

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  • DOI: https://doi.org/10.1007/978-3-319-77712-2_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77711-5

  • Online ISBN: 978-3-319-77712-2

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