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An approach for selective state machine based regression testing

Published: 09 July 2007 Publication History

Abstract

Model-based regression testing is an important activity that ensures the reliability of evolving software. One of the major issues in this type of testing is the optimal selection of test-cases to test the affected portion of the software. In this paper, we present a UML based selective regression testing strategy that uses state machines and class diagrams for change identification. We identify the changes using the UML 2.1 semantics of state machines and class diagram. The changes are classified as Class-driven (obtained from class diagram) and State-driven (obtained from state machine). The Class-driven changes are important as these changes are not reflected on the state machines and they might be helpful in identifying some fault-revealing test cases. With the help of the identified changes, we classify the test cases of the test suite as Obsolete, Reusable, and Retestable. We apply the approach on a case study to demonstrate its validity.

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cover image ACM Conferences
A-MOST '07: Proceedings of the 3rd international workshop on Advances in model-based testing
July 2007
127 pages
ISBN:9781595938503
DOI:10.1145/1291535
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]

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Published: 09 July 2007

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Author Tags

  1. UML
  2. model based testing
  3. regression testing

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  • (2023)Efficient regression testing of distributed real-time reactive systems in the context of model-driven developmentSoftware and Systems Modeling10.1007/s10270-023-01086-522:5(1565-1587)Online publication date: 6-Mar-2023
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