Monitoring and diagnosing software requirements

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 behaviors. 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.

[1]  Raymond Reiter,et al.  A Theory of Diagnosis from First Principles , 1986, Artif. Intell..

[2]  John Mylopoulos,et al.  Simple and Minimum-Cost Satisfiability for Goal Models , 2004, CAiSE.

[3]  William G. Griswold,et al.  An Overview of AspectJ , 2001, ECOOP.

[4]  Sheila A. McIlraith,et al.  Planning with complex actions , 2002, NMR.

[5]  Raymond Reiter,et al.  Characterizing Diagnoses and Systems , 1992, Artif. Intell..

[6]  Martin S. Feather,et al.  Requirements monitoring in dynamic environments , 1995, Proceedings of 1995 IEEE International Symposium on Requirements Engineering (RE'95).

[7]  Sheila A. McIlraith.  Explanatory Diagnosis: Conjecturing Actions to Explain Observations , 1998, KR.

[8]  Donald W. Loveland,et al.  A machine program for theorem-proving , 2011, CACM.

[9]  Eugene Goldberg,et al.  BerkMin: A Fast and Robust Sat-Solver , 2002, Discret. Appl. Math..

[10]  Thomas A. Alspaugh,et al.  An Automated Approach for Goal-driven, Specification-based Testing , 2006, 21st IEEE/ACM International Conference on Automated Software Engineering (ASE'06).

[11]  Raymond Reiter,et al.  The Frame Problem in the Situation Calculus: A Simple Solution (Sometimes) and a Completeness Result for Goal Regression , 1991, Artificial and Mathematical Theory of Computation.

[12]  M.S. Feather,et al.  Reconciling system requirements and runtime behavior , 1998, Proceedings Ninth International Workshop on Software Specification and Design.

[13]  John Mylopoulos,et al.  Representing and Using Nonfunctional Requirements: A Process-Oriented Approach , 1992, IEEE Trans. Software Eng..

[14]  William N. Robinson,et al.  Implementing Rule-Based Monitors within a Framework for Continuous Requirements Monitoring , 2005, Proceedings of the 38th Annual Hawaii International Conference on System Sciences.

[15]  Sharad Malik,et al.  Chaff: engineering an efficient SAT solver , 2001, Proceedings of the 38th Design Automation Conference (IEEE Cat. No.01CH37232).

[16]  Stephen Fickas,et al.  Goal-Directed Requirements Acquisition , 1993, Sci. Comput. Program..

[17]  Yijun Yu,et al.  An automated approach to monitoring and diagnosing requirements , 2007, ASE.

[18]  Yijun Yu,et al.  Reverse engineering goal models from legacy code , 2005, 13th IEEE International Conference on Requirements Engineering (RE'05).

[19]  Lawrence Ryan.  Efficient algorithms for clause-learning SAT solvers , 2004 .

[20]  Jeffrey O. Kephart,et al.  The Vision of Autonomic Computing , 2003, Computer.

[21]  Axel van Lamsweerde,et al.  Handling Obstacles in Goal-Oriented Requirements Engineering , 2000, IEEE Trans. Software Eng..

[22]  Gero Iwan,et al.  History-based diagnosis templates in the framework of the situation calculus , 2001, AI Commun..

[23]  John Mylopoulos,et al.  Reasoning with Goal Models , 2002, ER.