Compilers: principles, techniques, and tools
Compilers: principles, techniques, and tools
Discovering models of software processes from event-based data
ACM Transactions on Software Engineering and Methodology (TOSEM)
A practical method for verifying event-driven software
Proceedings of the 21st international conference on Software engineering
Concurrency: state models & Java programs
Concurrency: state models & Java programs
Model checking
Proceedings of the Conference on The Future of Software Engineering
Bandera: extracting finite-state models from Java source code
Proceedings of the 22nd international conference on Software engineering
Symbolic execution and program testing
Communications of the ACM
The SLAM project: debugging system software via static analysis
POPL '02 Proceedings of the 29th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
POPL '02 Proceedings of the 29th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Automatic generation of program specifications
ISSTA '02 Proceedings of the 2002 ACM SIGSOFT international symposium on Software testing and analysis
Efficient path conditions in dependence graphs
Proceedings of the 24th International Conference on Software Engineering
Automated Software Engineering
ACSD '01 Proceedings of the Second International Conference on Application of Concurrency to System Design
Dynamic analysis of java applications for multithreaded antipatterns
WODA '05 Proceedings of the third international workshop on Dynamic analysis
An algebraic definition of simulation between programs
IJCAI'71 Proceedings of the 2nd international joint conference on Artificial intelligence
Architecting Dependable Systems V
Towards faithful model extraction based on contexts
FASE'08/ETAPS'08 Proceedings of the Theory and practice of software, 11th international conference on Fundamental approaches to software engineering
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This work describes a new approach for behaviour model extraction which combines static and dynamic information. We exploit context information as a way of merging these types of information. Contexts are defined by evaluated control predicates and values of attributes. They create a nested structure that can facilitate the extraction of causal relations between system actions. We show how context information can guide the process of constructing LTS models that are good approximations of the actual behaviour of the systems they describe. These models can be used for automated analysis and property verification. Augmentation of the values of attributes recorded in contexts produces further refined models and leads towards correct models. Completeness of the extracted models depends on the coverage achieved by samples of executions. Our approach is partially automated by a tool called LTSE. Results of one of our case studies are presented and discussed.