Skip to main content
SpringerLink
Log in

How to Gauge Repair Risk?

  • Conference paper
  • First Online:
Book cover Fuzzy Information Processing (NAFIPS 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 831))

Included in the following conference series:

  • 751 Accesses

Abstract

At present, there exist several automatic tools that, given a software, find locations of possible defects. A general tool does not take into account a specificity of a given program. As a result, while many defects discovered by this tool can be truly harmful, many uncovered alleged defects are, for this particular software, reasonably (or even fully) harmless. A natural reaction is to repair all the alleged defects, but the problem is that every time we correct a program, we risk introducing new faults. From this viewpoint, it is desirable to be able to gauge the repair risk. This will help use decide which part of the repaired code is most likely to fail and thus, needs the most testing, and even whether repairing a probably harmless defect is worth an effort at all – if as a result, we increase the probability of a program malfunction. In this paper, we analyze how repair risk can be gauged.

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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. Almossawi, A., Lim, K., Sinha, T.: Analysis tool evaluation: coverity prevent. Final report, Carnegie Mellon University, Pittsubrgh, Pannsylvania (2006). http://www.cs.cmu.edu/~aldrich/courses/654-sp09/tools/cure-coverity-06.pdf

  2. Belohlavek, R., Dauben, J.W., Klir, G.J.: Fuzzy Logic and Mathematics: A Historical Perspective. Oxford University Press, New York (2017)

    MATH  Google Scholar 

  3. Emanuelsson, P., Nilsson, U.: A comparative study of industrial static analysis tools. Electron. Notes Theor. Comput. Sci. 217, 5–21 (2008)

    Article  Google Scholar 

  4. Klir, G., Yuan, B.: Fuzzy Sets and Fuzzy Logic. Prentice Hall, Upper Saddle River (1995)

    MATH  Google Scholar 

  5. Mendel, J.M.: Uncertain Rule-Based Fuzzy Systems. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-51370-6

    Book  MATH  Google Scholar 

  6. Nguyen, H.T., Kreinovich, V., Wojciechowski, P.: Strict Archimedean t-norms and t-conorms as universal approximators. Int. J. Approx. Reason. 18(3–4), 239–249 (1998)

    Article  MathSciNet  Google Scholar 

  7. Nguyen, H.T., Walker, E.A.: A First Course in Fuzzy Logic. Chapman and Hall/CRC, Boca Raton (2006)

    Google Scholar 

  8. Novák, V., Perfilieva, I., Močkoř, J.: Mathematical Principles of Fuzzy Logic. Kluwer, Boston, Dordrecht (1999)

    Book  Google Scholar 

  9. Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–353 (1965)

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported in part by the US National Science Foundation grant HRD-1242122.

Author information

Authors and Affiliations

  1. Department of Industrial, Manufacturing, and Systems Engineering, University of Texas at El Paso, El Paso, TX, 79968, USA

    Francisco Zapata

  2. Department of Computer Science, University of Texas at El Paso, El Paso, TX, 79968, USA

    Vladik Kreinovich

Authors
  1. Francisco Zapata
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vladik Kreinovich
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vladik Kreinovich .

Editor information

Editors and Affiliations

  1. Department of Teleinformatics Engineering, Federal University of Ceará, Fortaleza, Ceará, Brazil

    Guilherme A. Barreto

  2. Department of Statistics & Applied Mathematics, Federal University of Ceará, Fortaleza, Ceará, Brazil

    Ricardo Coelho

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

Zapata, F., Kreinovich, V. (2018). How to Gauge Repair Risk?. In: Barreto, G., Coelho, R. (eds) Fuzzy Information Processing. NAFIPS 2018. Communications in Computer and Information Science, vol 831. Springer, Cham. https://doi.org/10.1007/978-3-319-95312-0_48

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-95312-0_48

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-95311-3

  • Online ISBN: 978-3-319-95312-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation