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Scenario-based functional regression testing

Authors: Raymond A. Paul; Lian Yu; Wei-Tek Tsai; Xiaoying Bai;

Scenario-based functional regression testing

Abstract

Regression testing has been a popular quality-assurance technique. Most regression testing techniques are based on code or software design. This paper proposes a scenario-based functional regression testing, which is based on end-to-end (E2E) integration test scenarios. The test scenarios are first represented in a template model that embodies both test dependency and traceability. By using test dependency information, one can obtain a test slicing algorithm to detect the scenarios that are affected and thus they are candidates for regression testing. By using traceability information, one can find affected components and their associated test scenarios and test cases for regression testing. With the same dependency and traceability information one can use the ripple effect analysis to identify all affected, including directly or indirectly, scenarios and thus the set of test cases can be selected for regression testing. This paper also provides several alternative test-case selection approaches and a hybrid approach to meet various requirements. A web-based tool has been developed to support these regression testing tasks.

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    influence
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Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
7
Average
Top 10%
Average
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