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An optimized change-driven regression testing selection strategy for binary Java applications
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Symposium on Applied Computing archive
Proceedings of the 2009 ACM symposium on Applied Computing table of contents
Honolulu, Hawaii
SESSION: Software verification and testing track table of contents
Pages 558-565  
Year of Publication: 2009
ISBN:978-1-60558-166-8
Authors
Sheng Huang  IBM China Research Lab, Pudong New District Shanghai, China
Yang Chen  Tsinghua University Beijing, China
Jun Zhu  IBM China Research Lab, Beijing, China
Zhong Jie Li  IBM China Research Lab, Beijing, China
Hua Fang Tan  IBM China Research Lab, Beijing, China
Sponsor
SIGAPP: ACM Special Interest Group on Applied Computing
Publisher
ACM  New York, NY, USA
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ABSTRACT

Selective regression testing involves re-testing of software systems with a subset of the whole test suite to verify that modifications have not caused adverse impacts to existing functions complied with the requirements specifications. With the growing of globalization and individual testing services providers, many testing and development teams belong to different organizations, and often the testing teams only get a binary release of the application without access to its source code. This makes source code analysis based regression test selection strategy not applicable. Meanwhile source code analysis based approach has scalability problem for large applications, which hinders its wide application in industry. This paper presents an optimized regression testing selection strategy based on binary java file change analysis, through which the problems around binary java applications are avoided. Besides static regression test suite reduction and test prioritization factors, continuous and real time testing execution information are incorporated as fault-proneness indicator of the selected test cases to dynamically select and prioritize the regression test suites. In addition, the whole strategy is lightweight, making the regression test selection process more automated and effective. Experiments show that this strategy can guarantee the change point coverage, reveal faults quickly, and scale to industry-size regression testing scenarios under resource and time constraints.


REFERENCES

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Collaborative Colleagues:
Sheng Huang: colleagues
Yang Chen: colleagues
Jun Zhu: colleagues
Zhong Jie Li: colleagues
Hua Fang Tan: colleagues