Fast, Accurate Creation of Data Validation Formats by End-User Developers | SpringerLink
Skip to main content

Fast, Accurate Creation of Data Validation Formats by End-User Developers

  • Conference paper
End-User Development (IS-EUD 2009)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 5435))

Included in the following conference series:

Abstract

Inputs to web forms often contain typos or other errors. However, existing web form design tools require end-user developers to write regular expressions (“regexps”) or even scripts to validate inputs, which is slow and error-prone because of the poor match between common data types and the regexp notation. We present a new technique enabling end-user developers to describe data as a series of constrained parts, and we have incorporated our technique into a prototype tool. Using this tool, end-user developers can create validation code more quickly and accurately than with existing techniques, finding 90% of invalid inputs in a lab study. This study and our evaluation of the technique’s generality have motivated several tool improvements, which we have implemented and now evaluate using the Cognitive Dimensions framework.

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

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Blackwell, A.: SWYN: A Visual Representation for Regular Expressions. In: Your Wish Is My Command: Programming by Example, pp. 245–270. Morgan Kaufmann, San Francisco (2001)

    Chapter  Google Scholar 

  2. Burnett, M., et al.: End-User Software Engineering with Assertions in the Spreadsheet Paradigm. In: Proc. 25th Intl. Conf. on Software Engineering, pp. 93–103 (2003)

    Google Scholar 

  3. Chakrabarti, S.: Mining the Web: Discovering Knowledge from Hypertext Data. Morgan Kaufmann, San Francisco (2002)

    Google Scholar 

  4. Fisher II, M., Rothermel, G.: The EUSES Spreadsheet Corpus: A Shared Resource for Supporting Experimentation with Spreadsheet Dependability Mechanisms. Tech. Rpt. 04-12-03, Univ. of Nebraska (2004)

    Google Scholar 

  5. Green, T., Petre, M.: Usability Analysis of Visual Programming Environments: A “Cognitive Dimensions” Framework. J. Visual Lang. and Computing 7, 131–174 (1996)

    Article  Google Scholar 

  6. Koesnandar, A., et al.: Using Assertions to Help End-User Programmers Create Dependable Web Macros. In: Proc. 16th ACM SIGSOFT Intl. Symp. on Foundations of Software Engineering (to appear) (2008)

    Google Scholar 

  7. Lerman, K., Minton, S., Knoblock, C.: Wrapper Maintenance: A Machine Learning Approach. J. Artificial Intelligence Research 18, 149–181 (2003)

    MATH  Google Scholar 

  8. Lieberman, H., Nardi, B., Wright, D.: Training Agents to Recognize Text by Example. J. Auton. Agents and Multi-Agent Systems 4(1), 79–92 (2001)

    Article  Google Scholar 

  9. Miller, R., Myers, B.: Outlier Finding: Focusing Human Attention on Possible Errors. In: Proc. 14th Symp. on User Interface Software and Technology, pp. 81–90 (2001)

    Google Scholar 

  10. Mosteller, F., Youtz, C.: Quantifying Probabilistic Expressions. Statistical Science 5(1), 2–12 (1990)

    MathSciNet  MATH  Google Scholar 

  11. Myers, B., Pane, J., Ko, A.: Natural Programming Languages and Environments. Comm. ACM 47(9), 47–52 (2004)

    Article  Google Scholar 

  12. Nardi, B.: A Small Matter of Programming: Perspectives on End User Computing. MIT Press, Cambridge (1993)

    Google Scholar 

  13. Nardi, B., Miller, J., Wright, D.: Collaborative, Programmable Intelligent Agents. Comm. ACM 41(3), 96–104 (1998)

    Article  Google Scholar 

  14. Raz, O., Koopman, P., Shaw, M.: Semantic Anomaly Detection in Online Data Sources. In: Proc. 24th Intl. Conf. on Software Engineering, pp. 302–312 (2002)

    Google Scholar 

  15. Safonov, A.: Web Macros By Example: Users Managing the WWW of Applications. In: CHI 1999 Extended Abstracts on Human Factors in Computing Sys., pp. 71–72 (1999)

    Google Scholar 

  16. Scaffidi, C., Myers, B., Shaw, M.: Topes: Reusable Abstractions for Validating Data. In: Proc. 30th Intl. Conf. on Software Engineering, pp. 1–10 (2008)

    Google Scholar 

  17. Scaffidi, C.: Unsupervised Inference of Data Formats in Human-Readable Notation. In: Proc. 9th Intl. Conf. on Enterprise Information Systems-HCI Volume, pp. 236–241 (2007)

    Google Scholar 

  18. Scaffidi, C., et al.: Using Topes to Validate and Reformat Data in End-User Programming Tools. In: Proc. 4th Workshop on End-User Software Engineering, pp. 11–15 (2008)

    Google Scholar 

  19. Tomita, M.: An Efficient Augmented-Context-Free Parsing Algorithm. J. Computational Linguistics 13(1-2), 31–46 (1987)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Scaffidi, C., Myers, B., Shaw, M. (2009). Fast, Accurate Creation of Data Validation Formats by End-User Developers. In: Pipek, V., Rosson, M.B., de Ruyter, B., Wulf, V. (eds) End-User Development. IS-EUD 2009. Lecture Notes in Computer Science, vol 5435. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00427-8_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-00427-8_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-00425-4

  • Online ISBN: 978-3-642-00427-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics