Predictive Monitoring with Strong Trace Prefixes

TL;DR

This paper introduces strong trace prefixes to improve predictive monitoring of concurrent programs, enhancing detection capabilities while maintaining algorithmic efficiency for safety property analysis.

Contribution

It extends trace theory with strong trace prefixes, improving predictive power and developing new algorithms for data race and deadlock detection with better efficiency.

Findings

Strong trace prefixes enhance predictive accuracy.

Algorithms for data race and deadlock detection are more efficient.

Practical evaluation confirms utility of the new approach.

Abstract

Runtime predictive analyses enhance coverage of traditional dynamic analyses based bug detection techniques by identifying a space of feasible reorderings of the observed execution and determining if any of these witnesses the violation of some desired safety property. The most popular approach for modelling the space of feasible reorderings is through Mazurkiewicz's trace equivalence. The simplicity of the framework also gives rise to efficient predictive analyses, and has been the de facto means for obtaining space and time efficient algorithms for monitoring concurrent programs. In this work, we investigate how to enhance the predictive power of trace-based reasoning, while still retaining the algorithmic benefits it offers. Towards this, we extend trace theory by naturally embedding a class of prefixes, which we call strong trace prefixes. We formally characterize strong trace prefixes using an enhanced dependence relation, study its predictive power and establish a tight connection to the previously proposed notion of synchronization preserving correct reorderings developed in the context of data race and deadlock prediction. We then show that despite the enhanced predictive power, strong trace prefixes continue to enjoy the algorithmic benefits of Mazurkiewicz traces in the context of prediction against co-safety properties, and derive new algorithms for synchronization preserving data races and deadlocks with better asymptotic space and time usage. We also show that strong trace prefixes can capture more violations of pattern languages. We implement our proposed algorithms and our evaluation confirms the practical utility of reasoning based on strong prefix traces.