Skip to main content
SpringerLink
Log in

Evaluating Software Maintenance Effort: The COME Matrix

  • Conference paper
Software Engineering, Business Continuity, and Education (ASEA 2011)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 257))

Abstract

If effort estimates are not easily assessed upfront by software maintainers we may have serious problems with large maintenance projects, or when we make repeated maintenance changes to software. This is particularly problematic when inaccurate estimates of the required resources leads to serious negotiation issues. The development of a Categorisation of Maintenance Effort (COME) matrix enables an overall summary of software maintenance changes and maintenance effort to be shown, upfront, to software practitioners. This can occur without any consideration or use of other effort estimation techniques whose results, when used to estimate effort, can appear complicated and it may not be clear how accurate their estimates may be.

We use a simple approach to categorizing maintenance effort data using five steps. We use regression analysis with Jorgensen’s 81 datasets to evaluate the selected variables to find out the true efficacy of our approach: 1) adaptive changes and functional changes with maintenance effort predicted from low to high, 2) high predicted effort when updating KSLOC for software maintenance changes, 3) find that more lines of source codes do not imply that more software maintenance effort is needed, 4) find no significant relationship when we consider the age of the application and 5) find that at least 20 application sizes between KSLOC of 100, 200, 400 and 500 have a low predicted software maintenance effort.

Our experiment shows that using the COME matrix is an alternative approach to other cost estimation techniques for estimating effort for repeated requirement changes in large software maintenance projects.

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 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Song, Q.B., Shepperd, J.M.: Predicting software effort A grey relational analysis based method. Journal of Expert System and Application 38(6), 7302–7316 (2011)

    Article  Google Scholar 

  2. Jørgensen, M., Halkjelsvik, T.: The effects of request formats on judgment-based effort estimation. Journal of Systems and Software 83(1), 29–36 (2010)

    Article  Google Scholar 

  3. Jørgensen, M., Gruschke, T.M.: The Impact of Lessons-Learned Sessions on Effort Estimation and Uncertainty Assessments. IEEE Trans. Software Eng. 35(3), 368–383 (2009)

    Article  Google Scholar 

  4. Stark, G.E., Oman, P., Skillicorn, A., Ameele, A.: An examination of the effects of requirements changes on software maintenance releases. Journal of Software Maintenance: Research and Practice 11(5), 293–309 (1999)

    Article  Google Scholar 

  5. Yang, Y., Li, Q., Li, M.S., Wang, Q.: An Empirical Analysis on Distribution Patterns of Software Maintenance Effort. In: Proceedings of International Conference on Software Maintenance (ICSM), pp. 456–459 (2008)

    Google Scholar 

  6. Jorgensen, M.: Experience With the Accuracy of Software Maintenance Task Effort Prediction Models. IEEE Transactions on Software Engineering 21(8), 674–681 (1995)

    Article  Google Scholar 

  7. Osborne, W.M., Chikofsky, E.J.: Fitting Pieces to the Maintenance Puzzle. Proceedings of the IEEE Software 7(1), 11–12 (1990)

    Google Scholar 

  8. Milicic, D., Wohlin, C.: Distribution Patterns of Effort Estimations. In: Proceedings of EUROMICRO conference, pp. 422–429 (2004)

    Google Scholar 

  9. Ahn, Y., Suh, J., Kim, S., Kim, H.: The software maintenance project effort estimation model based on function points. Journal of software Maintenance and Evolution Research and Practice 15(2), 71–85 (2003)

    Article  Google Scholar 

  10. Boehm, B.W., Clark, B., Horowitz, E., Westland, C., Madachy, R., Selby, R.: Cost models for future software life cycle process:COCOMO 2.0. Annals of Software Engineering Special Volume on Software Process and Product Measurement (1995)

    Google Scholar 

  11. Boehm, B.W.: Software Engineering Economic, pp. 596–599. Prentice-Hall, Englewood Cliff (1981)

    Google Scholar 

  12. Lientz, B.P., Swanson, E.B.: Software Maintenance Management: A Study of the Maintenance of Computer Application Software in 487 Data Processing Organizations. Addison-Wesley, Reading (1980)

    Google Scholar 

  13. Leintz, B.P., Swanson, E.B.: Problems in application software maintenance. Communications of the ACM 24(11), 763–769 (1981)

    Article  Google Scholar 

  14. Martin, J., McClure, C.: Software Maintenance The Problems and its Solutions. Prentice-Hall, Englewood Cliffs (1983)

    Google Scholar 

  15. Nosek, J.T., Prashant, P.: Software Maintenance Management: Changes in the last Decade. Journal of Software Maintenance Research and Practice 2(3), 157–174 (1990)

    Article  Google Scholar 

  16. Albrecht, A.J.: Measuring Application Development Productivity. In: Proceedings Share/ Guide IBM Applications Development Symposium, Monterey, CA (1979)

    Google Scholar 

  17. Low, G.C., Jeffery, D.R.: Function points in the estimation and evaluation of the software process. IEEE Transactions on Software Engineering 16(1), 64–71 (1990)

    Article  Google Scholar 

  18. Jones, T.C.: Measuring programming quality and productivity. IBM System Journal 17(1), 39–63 (1978)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Technology, Sydney, Australia

    Bee Bee Chua

  2. Keele University, UK

    June Verner

Authors
  1. Bee Bee Chua
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. June Verner
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Multimedia Engineering Department, Hannam University, 133 Ojeong-dong, Daeduk-gu, Daejeon, Korea

    Tai-hoon Kim

  2. The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, 43210-1275, Columbus, OH, USA

    Hojjat Adeli

  3. Catholic University of Daegu, South Korea

    Haeng-kon Kim

  4. Dept. of Computer Engineering, Mokwon University, 800, Doan-dong, Seo-gu, 302-729, Daejeon, Korea

    Heau-jo Kang

  5. Woosuk University, 565-701, Jeollabuk-do, Korea

    Kyung Jung Kim

  6. University of Michigan-Dearborn, Dearborn, MI, USA

    Akingbehin Kiumi

  7. School of Computing and Information Systems, University of Tasmania, Hobart, TAS, Australia

    Byeong-Ho Kang

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Chua, B.B., Verner, J. (2011). Evaluating Software Maintenance Effort: The COME Matrix. In: Kim, Th., et al. Software Engineering, Business Continuity, and Education. ASEA 2011. Communications in Computer and Information Science, vol 257. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27207-3_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-27207-3_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-27206-6

  • Online ISBN: 978-3-642-27207-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation