MINTS Accepted at QRS 2021

Congratulations! Pradeep for the paper accepted @ QRS 2021

MINTS: Unsupervised Temporal Specifications Miner

Abstract Specifications for software systems are quite often missing or are obsolete given the evolutionary nature of these systems. Lack of precise software specifications makes the task of debugging and detecting a malfunction of system behavior challenging. Moreover, the absence of well-defined specifications for software used in safety-critical systems inhibits the deployment of such software in practice with fear of the catastrophic outcome. Prior works have primarily focused on extracting system specifications in the form of template-based mining frameworks or interactive simulation models. In safety-critical systems where the time of occurrence of events is of prime importance extracting specifications with a quantitative notion of time seems a daunting task.

This work presents an unsupervised approach to mine timed temporal properties in the form of deterministic finite state machines with a custom-designed trie data structure. Our framework, MINTS learns dominant system specification from their system traces, in the form of a timed trie, that is subsequently pruned and represented as a timed deterministic finite state machine. MINTS is shown to be sound and complete. MINTS scalability and correctness is validated using real-world industry strength traces.

IDSL Lab News

Highlights - 09 February 2022
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