Web-FIM: Automated Framework for the Inference of Business Software Models

We present an automated framework for the inference of behavioral models from the execution traces of a web-based business application (WBA). The model inference framework consists of a formal approach to infer automata models from traces of WBA`s and an advanced prototype tool set implemented around the data mining engine Weka, the model checker SPIN, the formal language manipulation framework ANTLR and the graph visualization software GraphViz. The traces of a WBA are collected by monitoring the communications in client-server architectures, where a client can be an Internet browser or a service accessing the server side of the application. The inferred models depict both the control and data flow (showing data variations) of the WBA and can be used for its visualization and verification. Finally, we discuss Web-FIM an online deployment of the model inference framework and illustrate the use of the tools with an example.

[1]  Raman Kazhamiakin,et al.  Formal verification of requirements using SPIN: a case study on Web services , 2004, Proceedings of the Second International Conference on Software Engineering and Formal Methods, 2004. SEFM 2004..

[2]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[3]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[4]  Manfred Broy,et al.  From MSCs to Statecharts , 1998, DIPES.

[5]  Jeffrey D. Ullman,et al.  Introduction to Automata Theory, Languages and Computation , 1979 .

[6]  Edmund M. Clarke,et al.  Model Checking , 1999, Handbook of Automated Reasoning.

[7]  Bengt Jonsson,et al.  Regular Inference for State Machines with Parameters , 2006, FASE.

[8]  Olivier Ridoux,et al.  Data mining and cross-checking of execution traces: a re-interpretation of Jones, Harrold and Stasko test information , 2005, ASE.

[9]  Gerard J. Holzmann.  Formal methods and software reliability , 2004, Proceedings. Second ACM and IEEE International Conference on Formal Methods and Models for Co-Design, 2004. MEMOCODE '04..

[10]  Hendrik Blockeel,et al.  Top-Down Induction of First Order Logical Decision Trees , 1998, AI Commun..

[11]  Aiko M. Hormann,et al.  Programs for Machine Learning. Part I , 1962, Inf. Control..

[12]  Amir Pnueli,et al.  Synthesis Revisited: Generating Statechart Models from Scenario-Based Requirements , 2005, Formal Methods in Software and Systems Modeling.

[13]  Dana Angluin,et al.  Learning Regular Sets from Queries and Counterexamples , 1987, Inf. Comput..

[14]  Alexandre Petrenko,et al.  Inferring Behavioural Models from Traces of Business Applications , 2009, 2009 IEEE International Conference on Web Services.

[15]  Roderick Bloem,et al.  Optimizations for LTL Synthesis , 2006, 2006 Formal Methods in Computer Aided Design.

[16]  Leonardo Mariani,et al.  Dynamic Detection of COTS Component Incompatibility , 2007, IEEE Software.

[17]  R. P. Jagadeesh Chandra Bose,et al.  Data mining approaches to software fault diagnosis , 2005, 15th International Workshop on Research Issues in Data Engineering: Stream Data Mining and Applications (RIDE-SDMA'05).

[18]  Alexandre Petrenko,et al.  A formal approach to property testing in causally consistent distributed traces , 2006, Formal Aspects of Computing.

[19]  May Haydar,et al.  Formal Verification of Web Applications Modeled by Communicating Automata , 2004, FORTE.

[20]  Gerard J. Holzmann,et al.  The SPIN Model Checker , 2003 .

[21]  Luc De Raedt,et al.  Top-down induction of logical decision trees , 1997 .

[22]  Chao Liu,et al.  Mining past-time temporal rules from execution traces , 2008, WODA '08.

[23]  Chao Liu,et al.  Mining Temporal Rules from Program Execution Traces , 2007 .