Wang, Yiqiao; Mcilraith, Sheila A.; Yu, Yijun and Mylopoulos, John
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|DOI (Digital Object Identifier) Link:||http://dx.doi.org/10.1007/s10515-008-0042-8|
|Google Scholar:||Look up in Google Scholar|
We propose a framework adapted from Artificial Intelligence theories of action and diagnosis for monitoring and diagnosing failures of software requirements. Software requirements are specified using goal models where they are associated with preconditions and postconditions. The monitoring component generates log data that contains the truth values of specified pre/post-conditions, as well as system action executions. Such data can be generated at different levels of granularity, depending on diagnostic feedback. The diagnostic component diagnoses the denial of requirements using the log data, and identifies problematic components. To support diagnostic reasoning, we transform the diagnostic problem into a propositional satisfiability (SAT) problem that can be solved by existing SAT solvers. The framework returns sound and complete diagnoses accounting for observed aberrant system behaviours. Our solution is illustrated with two medium-sized publicly available case studies: a Web-based email client and an ATM simulation. Our experimental results demonstrate the scalability of our approach.
|Item Type:||Journal Article|
|Copyright Holders:||2009 Springer|
|Academic Unit/Department:||Mathematics, Computing and Technology > Computing & Communications|
|Interdisciplinary Research Centre:||Centre for Research in Computing (CRC)|
|Depositing User:||Yijun Yu|
|Date Deposited:||06 Jan 2010 12:48|
|Last Modified:||03 Dec 2012 17:44|
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