Data driven testing of open source software

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Abstract

The increasing adoption of open source software (OSS) components in software systems introduces new quality risks and testing challenges. OSS components are developed and maintained by open communities and the fluctuation of community members and structures can result in instability of the software quality. Hence, an investigation is necessary to analyze the impact open community dynamics and the quality of the OSS, such as the level and trends in internal communications and content distribution. The analysis results provide inputs to drive selective testing for effective validation and verification of OSS components. The paper suggests an approach for monitoring community dynamics continuously, including communications like email and blogs, and repositories of bugs and fixes. Detection of patterns in the monitored behavior such as changes in traffic levels within and across clusters can be used in turn to drive testing efforts. Our proposal is demonstrated in the case of the XWiki OSS, a Java-based environment that allows for the storing of structured data and the execution of server side scripts within the wiki interface. We illustrate our concepts, methods and approach behind this approach for risk based testing of OSS.

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APA

Yahav, I., Kenett, R. S., & Bai, X. (2014). Data driven testing of open source software. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8803, pp. 309–321). Springer Verlag. https://doi.org/10.1007/978-3-662-45231-8_22

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