Dynamically discovering likely program invariants to support program evolution
Proceedings of the 21st international conference on Software engineering
Temporal logics for real-time system specification
ACM Computing Surveys (CSUR)
Symbolic execution and program testing
Communications of the ACM
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Sequential PAttern mining using a bitmap representation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth
ICDE '01 Proceedings of the 17th International Conference on Data Engineering
Transaction level modeling: an overview
Proceedings of the 1st IEEE/ACM/IFIP international conference on Hardware/software codesign and system synthesis
IODINE: a tool to automatically infer dynamic invariants for hardware designs
Proceedings of the 42nd annual Design Automation Conference
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
SMArTIC: towards building an accurate, robust and scalable specification miner
Proceedings of the 14th ACM SIGSOFT international symposium on Foundations of software engineering
Hybrid, Incremental Assertion-Based Verification for TLM Design Flows
IEEE Design & Test
Formal verification of SystemC by automatic hardware/software partitioning
MEMOCODE '05 Proceedings of the 2nd ACM/IEEE International Conference on Formal Methods and Models for Co-Design
The temporal logic of programs
SFCS '77 Proceedings of the 18th Annual Symposium on Foundations of Computer Science
Simulation-directed invariant mining for software verification
Proceedings of the conference on Design, automation and test in Europe
A Tractable and Fast Method for Monitoring SystemC TLM Specifications
IEEE Transactions on Computers
A temporal language for SystemC
Proceedings of the 2008 International Conference on Formal Methods in Computer-Aided Design
Scalable specification mining for verification and diagnosis
Proceedings of the 47th Design Automation Conference
GoldMine: automatic assertion generation using data mining and static analysis
Proceedings of the Conference on Design, Automation and Test in Europe
Automatic assertion extraction via sequential data mining of simulation traces
Proceedings of the 2010 Asia and South Pacific Design Automation Conference
Logic of constraints: a quantitative performance and functional constraint formalism
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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We automatically generate assertions from Transaction Level Model (TLM) simulation traces. The generated assertions express design specifications in the form of linear temporal logic with quantitative temporal constraints [4]. We first generate the assertions without regard to the quantitative time constraints. They are mined in the form of frequent patterns in the simulation traces. We mine simulation traces using episode mining to identify frequent episodes comprising function calls and events. We then annotate the episodes with real time parameters to express quantitative time constraints among the function calls or events in the episode. When mining such TLM assertions, we employ symbolic execution to generalize the parameters and return values of function calls in the traces to help the mining engine generate high quality assertions. We have constructed a realistic AXI-based interconnection network platform that we demonstrate experimental results on. We show that our technique efficiently generates high quality performance and functional assertions on the AXI-based platform as well as a transaction level AMBA-based DMA controller. We demonstrate that episode mining is more scalable and able to generate a more compact set of high quality TLM assertions than previous efforts using sequential pattern mining. The number of generated assertions using episode mining can be reduced by up to 228 times, and the time interval between two events/function calls in each assertion is smaller than 50 time units.