Dynamically Discovering Likely Program Invariants to Support Program Evolution
IEEE Transactions on Software Engineering - Special issue on 1999 international conference on software engineering
POPL '02 Proceedings of the 29th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Dynamic inference of abstract types
Proceedings of the 2006 international symposium on Software testing and analysis
Static specification mining using automata-based abstractions
Proceedings of the 2007 international symposium on Software testing and analysis
The Daikon system for dynamic detection of likely invariants
Science of Computer Programming
Automatic generation of software behavioral models
Proceedings of the 30th international conference on Software engineering
Extending dynamic constraint detection with disjunctive constraints
WODA '08 Proceedings of the 2008 international workshop on dynamic analysis: held in conjunction with the ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2008)
Process Mining: Discovery, Conformance and Enhancement of Business Processes
Process Mining: Discovery, Conformance and Enhancement of Business Processes
Mining Software Specifications: Methodologies and Applications
Mining Software Specifications: Methodologies and Applications
BPM'06 Proceedings of the 4th international conference on Business Process Management
Using dynamic analysis to discover polynomial and array invariants
Proceedings of the 34th International Conference on Software Engineering
Discovering data-aware declarative process models from event logs
BPM'13 Proceedings of the 11th international conference on Business Process Management
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Process mining is a family of techniques to discover business process models and other knowledge of business processes from event logs. Existing process mining techniques are geared towards discovering models that capture the order of execution of tasks, but not the conditions under which tasks are executed --- also called branching conditions. One existing process mining technique, namely ProM's Decision Miner, applies decision tree learning techniques to discover branching conditions composed of atoms of the form "v op c" where "v" is a variable, "op" is a comparison predicate and "c" is a constant. This paper puts forward a more general technique to discover branching conditions where the atoms are linear equations or inequalities involving multiple variables and arithmetic operators. The proposed technique combine invariant discovery techniques embodied in the Daikon system with decision tree learning techniques.