Test data generation for web applications: A constraint and knowledge-based approach

Hibiki Saito, Shingo Takada, Haruto Tanno, Morihide Oinuma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Software testing is an important part of the software development process. Much work been has done on automating various parts of testing. In previous work, we had proposed a knowledge-based approach to generate test scenarios for Web applications. However, our previous work did not account for generation of actual test data. Thus, in order to execute the test scenarios, the user would need to (manually) create the test data. This paper proposes an approach to generate test data for our previously proposed test scenario generation tool. Our approach can generate two types of test data: constraint-based test data and database-based test data. Our tool can now automatically execute the combined test scenario and test data. We confirmed the usefulness of our approach through a case study.

Original languageEnglish
Title of host publicationProceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE
PublisherKnowledge Systems Institute Graduate School
Pages110-114
Number of pages5
Volume2014-January
EditionJanuary
Publication statusPublished - 2014
Event26th International Conference on Software Engineering and Knowledge Engineering, SEKE 2014 - Vancouver, Canada
Duration: 2014 Jul 12014 Jul 3

Other

Other26th International Conference on Software Engineering and Knowledge Engineering, SEKE 2014
CountryCanada
CityVancouver
Period14/7/114/7/3

    Fingerprint

Keywords

  • Test data generation
  • Test scenario
  • Web applications

ASJC Scopus subject areas

  • Software

Cite this

Saito, H., Takada, S., Tanno, H., & Oinuma, M. (2014). Test data generation for web applications: A constraint and knowledge-based approach. In Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE (January ed., Vol. 2014-January, pp. 110-114). Knowledge Systems Institute Graduate School.