Conference proceeding
An empirical comparison of test suite reduction techniques for user-session-based testing of Web applications
21st IEEE International Conference on Software Maintenance (ICSM'05), v 2005, pp 587-596
2005
Abstract
Automated cost-effective test strategies are needed to provide reliable, secure, and usable Web applications. As a software maintainer updates an application, test cases must accurately reflect usage to expose faults that users are most likely to encounter. User-session-based testing is an automated approach to enhancing an initial test suite with real user data, enabling additional testing during maintenance as well as adding test data that represents usage as operational profiles evolve. Test suite reduction techniques are critical to the cost effectiveness of user-session-based testing because a key issue is the cost of collecting, analyzing, and replaying the large number of test cases generated from user-session data. We performed an empirical study comparing the test suite size, program coverage, fault detection capability, and costs of three requirements-based reduction techniques and three variations of concept analysis reduction applied to two Web applications. The statistical analysis of our results indicates that concept analysis-based reduction is a cost-effective alternative to requirements-based approaches.
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Details
- Title
- An empirical comparison of test suite reduction techniques for user-session-based testing of Web applications
- Creators
- S Sprenkle - University of DelawareSreedevi Sampath - University of DelawareE Gibson - University of DelawareL Pollock - University of DelawareA Souter - Drexel UniversityIEEE Comp Soc
- Publication Details
- 21st IEEE International Conference on Software Maintenance (ICSM'05), v 2005, pp 587-596
- Publisher
- IEEE
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Communication
- Web of Science ID
- WOS:000234333200058
- Scopus ID
- 2-s2.0-33646950015
- Other Identifier
- 991019173738804721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Software Engineering