Text Mining in Supporting Software Systems Risk Assurance
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, the authors 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.