Skip to main content
SpringerLink
Log in

Measuring the Extent of Source Code Readability Using Regression Analysis

  • Conference paper
  • First Online:
Computational Science and Its Applications – ICCSA 2018 (ICCSA 2018)

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

Included in the following conference series:

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Boehm, B., Basili, V.R.: Software defect reduction top 10 list. IEEE Comput. 34(1), 135–137 (2001). https://doi.org/10.1109/2.962984

    Article  Google Scholar 

  2. Buse, R.P.L., Weimer, W.R.: A metric for software readability. IEEE Trans. Softw. Eng. 36(4), 546–558 (2010). https://doi.org/10.1145/1390630.1390647

    Article  Google Scholar 

  3. Halstead, M.: Elements of Software Science. Elsevier Science Inc., New York (1997)

    MATH  Google Scholar 

  4. Posnett, D., Hindle, A., Devanbu, P.: A simpler model of software readability. In: The 8th Working Conference on Mining Software Repositories (MSR), vol. 11, pp. 73–82 (2011). https://doi.org/10.1145/1985441.1985454

  5. Ramcharan, R.: Regressions: why are economists obsessed with them? Financ. Dev. 43, 36–37 (2006)

    Google Scholar 

  6. Freedman, D.A.: Statistical Models: Theory and Practice. University of California, Berkeley (2009)

    Book  Google Scholar 

  7. Choi, S.-H., Sun, H.-S.: A nonlinear regression analysis method for frame erasure concealment in VoIP network. Inst. Internet Broadcast. Commun. 9(5), 129–132 (2009)

    Google Scholar 

Download references

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).

Author information

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
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Suntae Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jeong-Hyu Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. JeongAh Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jae-Young Choi
    View author publications

    You can also search for this author in PubMed Google Scholar

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

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

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

Download citation

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

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-95170-6

  • Online ISBN: 978-3-319-95171-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation