skip to main content
10.1145/2166966.2167005acmconferencesArticle/Chapter ViewAbstractPublication PagesiuiConference Proceedingsconference-collections
research-article

Towards automatic functional test execution

Authors Info & Claims
Published:14 February 2012Publication History

ABSTRACT

As applications are developed, functional tests ensure they continue to function as expected. Nowadays, functional testing is mostly done manually, with human testers verifying a system's functionality themselves, following hand-written instructions. While there exist tools supporting functional test automation, in practice they are hard to use, require programming skills, and do not provide good support for test maintenance. In this paper, we take an alternative approach: we semi-automatically convert hand-written instructions into automated tests. Our approach consists of two stages: first, employing machine learning and natural language processing to compute an intermediate representation from test steps; and second, interactively disambiguating that representation to create a fully automated test. These two stages comprise a complete system for converting hand-written functional tests into automated tests. We also present a quantitative study analyzing the effectiveness of our approach. Our results show that 70% of manual test steps can be automatically converted to automated test steps with no user intervention.

References

  1. Abney, S. P. Parsing by chunks. In Principle-Based Parsing: Computation and Psycholinguistics (1991), 257--278.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Branavan, S. R. K., Zettlemoyer, L. S., and Barzilay, R. Reading between the lines: learning to map high-level instructions to commands. In Proc. of the 48th Annual Meeting of the Association for Computational Linguistics, ACL '10 (2010), 1268--1277. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Brill, E. A simple rule-based part of speech tagger. In Proc. of the third conference on Applied natural language processing, ANLC '92 (1992), 152--155. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Chen, D. L., and Mooney, R. J. Learning to interpret natural language navigation instructions from observations. In Proc. of the Twenty-Fifth AAAI Conference on Artificial Intelligence (2011).Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Cortes, C., and Vapnik, V. Support-vector networks. Mach. Learn. 20 (1995), 273--297. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Dustin, E., Rashka, J., and Paul, J. Automated software testing: introduction, management, and performance. Addison-Wesley Longman Publishing Co., Inc., 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Fantechi, A., Gnesi, S., Lami, G., and Maccari, A. Application of linguistic techniques for use case analysis. In Proc. of the 10th Anniversary IEEE Joint intl. conf. on Requirements engineering, RE '02 (2002), 157--164. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Gouveia, D., Davis, C., Saracevic, F., Bocarsly, J., Chirillo, D., and Quesada, L. Software Test Engineering with IBM Rational Functional Tester: The Definitive Resource. IBM Press, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Kit, E., and Finzi, S. Software testing in the real world: improving the process. ACM Press/Addison-Wesley Publishing Co., 1995. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Kollar, T., Tellex, S., Roy, D., and Roy, N. Toward understanding natural language directions. In Proc. of the 5th ACM/IEEE intl. conf. on Human-robot interaction, HRI '10 (2010), 259--266. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Lafferty, J. D., McCallum, A., and Pereira, F. C. N. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proc. of the 18th intl. conf. on Machine learning, ICML '01 (2001), 282--289. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Lau, T., Drews, C., and Nichols, J. Interpreting written how-to instructions. In Proc. of the 21st intl. joint conf. on Artificial intelligence (2009), 1433--1438. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Leshed, G., Haber, E. M., Matthews, T., and Lau, T. CoScripter: automating & sharing how-to knowledge in the enterprise. In Proc. of the 25th annual SIGCHI conf. on Human factors in computing systems, CHI '08 (2008), 1719--1728. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Manber, U., and Myers, G. Suffix arrays: a new method for on-line string searches. SIAM J. Comput. 22 (1993), 935--948. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Ramshaw, L. A., and Marcus, M. P. Text chunking using transformation-based learning. In Proc. of the Third Annual Workshop on Very Large Corpora (1995), 82--94.Google ScholarGoogle Scholar
  16. Shimizu, N. Semantic discourse segmentation and labeling for route instructions. In Proc. of the 21st intl. conf. on Computational linguistics, COLING ACL '06 (2006), 31--36. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Shimizu, N., and Haas, A. Learning to follow navigational route instructions. In Proc. of the 21st intl. joint conf. on Artificial intelligence (2009), 1488--1493. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Sinha, A., Jr., S. M. S., and Paradkar, A. Text2Test: Automated inspection of natural language use cases. In Proc. of the 2010 3rd intl. conf. on Software Testing, Verification and Validation, ICST '10 (2010), 155--164. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Sinha, A., Paradkar, A. M., Kumanan, P., and Boguraev, B. A linguistic analysis engine for natural language use case description and its application to dependability analysis in industrial use cases. In DSN (2009), 327--336.Google ScholarGoogle Scholar
  20. Thummalapenta, S., Sinha, S., Mukherjee, D., and Chandra, S. Automating test automation. Tech. Rep. RI11015, IBM Research, 2011.Google ScholarGoogle Scholar
  21. Tichy, W. F., and Koerner, S. J. Text to software: developing tools to close the gaps in software engineering. In Proc. of the FSE/SDP workshop on Future of software engineering research, FoSER '10 (2010), 379--384. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Tjong Kim Sang, E. F., and Buchholz, S. Introduction to the CoNLL-2000 shared task: chunking. In Proc. of the 2nd workshop on Learning language in logic and the 4th conf. on Computational natural language learning Volume 7, ConLL '00 (2000), 127--132. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Towards automatic functional test execution

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      IUI '12: Proceedings of the 2012 ACM international conference on Intelligent User Interfaces
      February 2012
      436 pages
      ISBN:9781450310482
      DOI:10.1145/2166966

      Copyright © 2012 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 14 February 2012

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article

      Acceptance Rates

      Overall Acceptance Rate746of2,811submissions,27%

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader