Abstraction and Mining of Traces to Explain Concurrency Bugs

Abstract

We propose an automated mining-based method for explaining concurrency bugs. We use a data mining technique called sequential pattern mining to identify problematic sequences of concurrent read and write accesses to the shared memory of a multithreaded program. Our technique does not rely on any characteristics specific to one type of concurrency bug, thus providing a general framework for concurrency bug explanation. In our method, given a set of concurrent execution traces, we first mine sequences that frequently occur in failing traces and then rank them based on the number of their occurrences in passing traces. We consider the highly ranked sequences of events that occur frequently only in failing traces an explanation of the system failure, as they can reveal its causes in the execution traces. Since the scalability of sequential pattern mining is limited by the length of the traces, we present an abstraction technique which shortens the traces at the cost of introducing spurious explanations. Spurious as well as misleading explanations are then eliminated by a subsequent filtering step, helping the programmer to focus on likely causes of the failure. We validate our approach using a number of case studies, including synthetic as well as real-world bugs.

Abstraction and mining of traces to explain concurrency bugs

“…The diagrams in Fig. 11 show a comparison of the difference between the number of patterns generated at steps 5-8 of Algorithm 2 by method of this paper (current) and method of [33] (previous). We observed only a slight change between the outputs of the two methods in every step.…”

“…In the method of [33], we used the first option in the implementation of the method while in the method of this paper we used the second option. Therefore, we improved performance of the method at the cost of precision of the supports of macro patterns.…”

“…4.1, followed by a discussion of the parameters of the method and their effects. This section also describes an optimization of the computationally costly filtering step of [33], resulting in orders of magnitude speed up in run time. -In the section on experimental results, we demonstrate that our modification of the method in [33] preserves the effectiveness of the method while achieving more efficiency.…”

“…This section also describes an optimization of the computationally costly filtering step of [33], resulting in orders of magnitude speed up in run time. -In the section on experimental results, we demonstrate that our modification of the method in [33] preserves the effectiveness of the method while achieving more efficiency. Moreover, we show the effect of variations in the input datasets of traces on the effectiveness of the method by bounding the number of context switches in input traces.…”

(De-)Composing Causality in Labeled Transition Systems

“…A trace-based approach to identifying causality for failures of interleaved systems has been recently introduced in [4]. In short, the authors propose a method for identifying event sequences that frequently occur within failing system executions, thus possibly revealing causes for system failures.…”