Boosted decision trees for behaviour mining of concurrent programmes

Abstract

SummaryTesting of concurrent programmes is difficult since the scheduling nondeterminism requires one to test a huge number of different thread interleavings. Moreover, repeated test executions that are performed in the same environment will typically examine similar interleavings only. One possible way how to deal with this problem is to use the noise injection approach, which influences the scheduling by injecting various kinds of noise (delays, context switches, etc) into the common thread behaviour. However, for noise injection to be efficient, one has to choose suitable noise injection heuristics from among the many existing ones as well as to suitably choose values of their various parameters, which is not easy. In this paper, we propose a novel way how to deal with the problem of choosing suitable noise injection heuristics and suitable values of their parameters (as well as suitable values of parameters of the programmes being tested themselves). Here, by suitable, we mean such settings that maximize chances of meeting a given testing goal (such as, eg, maximizing coverage of rare behaviours and thus maximizing chances to find rarely occurring concurrency‐related bugs). Our approach is, in particular, based on using data mining in the context of noise‐based testing to get more insight about the importance of the different heuristics in a particular testing context as well as to improve fully automated noise‐based testing (in combination with both random as well as genetically optimized noise setting).

Efficient noise injection for exposing hidden data races

“…A trace is a sequence of events. Two events and in a trace are ordered by the HB relation ( → ) if (1) and are performed by the same thread, and appears before , or (2) if =< t 1 , release, o > and =< t 2 , acquire, o > are performed by two different threads, and appears before , (3) if → and → , then → .…”

“…Hrubá et al [21] and Avros et al [1] proposed methods to find suitable parameter values for random noise injection by using multiple-objective genetic algorithms and data mining. Fiedor et al [12] presented the experimental results of various existing heuristics and two new heuristics for the noise placement and noise seeding problems in terms of the detection capability, concurrency coverage, and performance degradation.…”

“…These concurrency bugs are considerably difficult to detect because they manifest nondeterministically. Among the various types of concurrency bugs, a data race (or a race, for short) is caused by two concurrent 1 and conflicting 2 accesses to the same shared-memory location. The data race itself may cause serious problems as well as other types of concurrency bugs such as atomicity violations and order violations.…”

Hierarchical Fusion of Machine Learning Algorithms in Indoor Positioning and Localization

“…The result of this algorithm is a tree with decision nodes. Decision trees can handle both categorical and numerical data [36,37].…”

A possibilistic framework for the detection of terrorism‐related Twitter communities in social media

“…Therefore, there is a potential need for analyzing social networks and detecting terrorist substructures taking into account the uncertainty that can be inherent in social networks. Indeed, the substructure detection is an important task in the area of social networking, which aims to find all communities in the entire network . Graph analytics have proven to be valuable tools in solving these challenges.…”