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Investigating the Effect of Sensitivity and Severity Analysis on Fault Proneness in Open Source Software

Investigating the Effect of Sensitivity and Severity Analysis on Fault Proneness in Open Source Software

D. Jeya Mala
Copyright: © 2017 |Volume: 8 |Issue: 1 |Pages: 25
ISSN: 1942-3926|EISSN: 1942-3934|EISBN13: 9781522512677|DOI: 10.4018/IJOSSP.2017010103
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MLA

Mala, D. Jeya. "Investigating the Effect of Sensitivity and Severity Analysis on Fault Proneness in Open Source Software." IJOSSP vol.8, no.1 2017: pp.42-66. http://doi.org/10.4018/IJOSSP.2017010103

APA

Mala, D. J. (2017). Investigating the Effect of Sensitivity and Severity Analysis on Fault Proneness in Open Source Software. International Journal of Open Source Software and Processes (IJOSSP), 8(1), 42-66. http://doi.org/10.4018/IJOSSP.2017010103

Chicago

Mala, D. Jeya. "Investigating the Effect of Sensitivity and Severity Analysis on Fault Proneness in Open Source Software," International Journal of Open Source Software and Processes (IJOSSP) 8, no.1: 42-66. http://doi.org/10.4018/IJOSSP.2017010103

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Abstract

Fault prone components in open source software leads to huge loss and inadvertent effects if not properly identified and rigorously tested. Most of the reported studies in the literature have applied design metrics alone, to identify such critical components. But in reality, some of the components' criticality level can be identified only by means of dynamic code analysis; as some of the components seem to be normal but still have higher level of impact on the other components. This leads to an insight on the need of a rigorous analysis based on how sensitive a component is and how severe will be the impact of it on other components in the system. To achieve this, an efficient mechanism of evaluating the criticality index of each component by means of sensitivity and severity analysis using the static design metrics and dynamic source code metrics has been proposed. Then, testing is conducted rigorously on these components using both unit testing and pair-wise integration testing.

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