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Measuring the Extent of Source Code Readability Using Regression Analysis

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Computational Science and Its Applications – ICCSA 2018 (ICCSA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10963))

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Abstract

Software maintenance accounts for a large portion of the software life cycle cost. In the software maintenance phase, comprehending the legacy source code is inevitable, which takes most of the time. Source code readability is a metric of the extent of source code comprehension. The better the code is readable, the easier it is for code readers to comprehend the system based on the source code. This paper proposes an enhanced source code readability metric to quantitative measure the extent of code readability, which is more enhanced measurement method than previous research that dichotomously judges whether the source code was readable or not. As an evaluation, we carried out a survey and analyzed them with two-way linear regression analysis to measure the extent of source code readability.

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Acknowledgement

This research was supported by Next-Generation Information Computing Development Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science and ICT (NRF-2014M3C4A7030503).

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Authors and Affiliations

  1. Department of Software Engineering, CAIIT, Chonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju-si, Jeollabuk-do, Republic of Korea

    Sangchul Choi, Suntae Kim & Jeong-Hyu Lee

  2. Department of Computer Education, Catholic Kwandong University, 24, Beomil-ro 579 beon-gil, Gangneung-si, Gangwon-do, Republic of Korea

    JeongAh Kim

  3. Department of Computer Engineering, SungKyunKwan University, 2066 Seobu-ro, Jangan-gu, Suwon, Gyeonggi-do, Republic of Korea

    Jae-Young Choi

Authors
  1. Sangchul Choi
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  2. Suntae Kim
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  3. Jeong-Hyu Lee
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  4. JeongAh Kim
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  5. Jae-Young Choi
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Corresponding author

Correspondence to Suntae Kim .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  5. Polytechnic University of Bari, Bari, Italy

    Carmelo M. Torre

  6. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  7. Monash University, Clayton, Victoria, Australia

    David Taniar

  8. Kyushu Sangyo University, Fukuoka shi, Fukuoka, Japan

    Bernady O. Apduhan

  9. Politecnico di Bari, Bari, Italy

    Eufemia Tarantino

  10. Myongji University, Yongin, Korea (Republic of)

    Yeonseung Ryu

Appendices

Appendix 1

See Table 6.

Table 6. Result of nonlinear regression
Full size table

Appendix 2

See Table 7.

Table 7. Result of step-wise technique to nonlinear regression
Full size table

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Choi, S., Kim, S., Lee, JH., Kim, J., Choi, JY. (2018). Measuring the Extent of Source Code Readability Using Regression Analysis. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2018. ICCSA 2018. Lecture Notes in Computer Science(), vol 10963. Springer, Cham. https://doi.org/10.1007/978-3-319-95171-3_32

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  • DOI: https://doi.org/10.1007/978-3-319-95171-3_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-95170-6

  • Online ISBN: 978-3-319-95171-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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