Text mining in supporting software systems risk assurance

Authors:
LiGuo Huang;Daniel Port;Liang Wang;Tao Xie;Tim Menzies
Affiliations:
Southern Methodist University, Dallas, TX, USA;California Institute of Technology, Pasadena, CA, USA;Southern Methodist University, Dallas, TX, USA;North Carolina State University, Raleigh, NC, USA;West Virginia University, Morgantown, WV, USA
Venue:
Proceedings of the IEEE/ACM international conference on Automated software engineering
Year:
2010

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Abstract

Insufficient risk analysis often leads to software system design defects and system failures. Assurance of software risk documents aims to increase the confidence that identified risks are complete, specific, and correct. Yet assurance methods rely heavily on manual analysis that requires significant knowledge of historical projects and subjective, perhaps biased judgment from domain experts. To address the issue, we have developed RARGen, a text mining-based approach based on well-established methods aiming to automatically create and maintain risk repositories to identify usable risk association rules (RARs) from a corpus of risk analysis documents. RARs are risks that have frequently occurred in historical projects. We evaluate RARGen on 20 publicly available e-service projects. Our evaluation results show that RARGen can effectively reason about RARs, increase confidence and cost-effectiveness of risk assurance, and support difficult-to-perform activities such as assuring complete-risk identification.