Localizing Defects in Multithreaded Programs by Mining Dynamic Call Graphs

Eichinger, Frank; Pankratius, Victor; Große, Philipp W. L.; Böhm, Klemens

doi:10.1007/978-3-642-15585-7_7

Cited by 12 publications

(15 citation statements)

References 26 publications

(34 reference statements)

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“…Localising Call-Graph-Affecting Bugs. In the past years, a number of callgraph-based techniques for defect localisation have been proposed [3,5,7,8,18]. Their basic idea is to mine for patterns in the call graph which are characteristic for failing executions.…”

Section: Fundamentals Of Call-graph-based Defect Localisationmentioning

confidence: 99%

“…Existing techniques focus on structure-affecting bugs [3,5,7,18] and callfrequency-affecting bugs [7,8]. The graphs in [3,5,18] incorporate temporal information, the ones in [7,8] do not.…”

Section: Fundamentals Of Call-graph-based Defect Localisationmentioning

confidence: 99%

“…The graphs in [3,5,18] incorporate temporal information, the ones in [7,8] do not. In [6,7] we explain that the increased graph size when temporal information is incorporated leads to scalability issues when it comes to the mining.…”

Section: Fundamentals Of Call-graph-based Defect Localisationmentioning

confidence: 99%

“…In [6,7] we explain that the increased graph size when temporal information is incorporated leads to scalability issues when it comes to the mining. [3,8,18] build on call graphs where exactly one node represents a method, while [5,7] allow for more than one node. This promises more precise results, as more detailed contexts of method calls can be included in the analysis.…”

Section: Fundamentals Of Call-graph-based Defect Localisationmentioning

confidence: 99%

“…In the past years, a number of studies has investigated defect localisation with graph-mining techniques [3,5,7,8,18]. They build on the analysis of dynamic call graphs (see [6] for an overview).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Software-Defect Localisation by Mining Dataflow-Enabled Call Graphs

Eichinger

Krogmann

Klug

et al. 2010

Machine Learning and Knowledge Discovery in Databases

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Abstract. Defect localisation is essential in software engineering and is an important task in domain-specific data mining. Existing techniques building on call-graph mining can localise different kinds of defects. However, these techniques focus on defects that affect the controlflow and are agnostic regarding the dataflow. In this paper, we introduce dataflowenabled call graphs that incorporate abstractions of the dataflow. Building on these graphs, we present an approach for defect localisation. The creation of the graphs and the defect localisation are essentially data mining problems, making use of discretisation, frequent subgraph mining and feature selection. We demonstrate the defect-localisation qualities of our approach with a study on defects introduced into Weka. As a result, defect localisation now works much better, and a developer has to investigate on average only 1.5 out of 30 methods to fix a defect.

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Section: Fundamentals Of Call-graph-based Defect Localisationmentioning

confidence: 99%

“…Existing techniques focus on structure-affecting bugs [3,5,7,18] and callfrequency-affecting bugs [7,8]. The graphs in [3,5,18] incorporate temporal information, the ones in [7,8] do not.…”

Section: Fundamentals Of Call-graph-based Defect Localisationmentioning

confidence: 99%

Section: Fundamentals Of Call-graph-based Defect Localisationmentioning

confidence: 99%

Section: Fundamentals Of Call-graph-based Defect Localisationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Software-Defect Localisation by Mining Dataflow-Enabled Call Graphs

Eichinger

Krogmann

Klug

et al. 2010

Machine Learning and Knowledge Discovery in Databases

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Data mining for defects in multicore applications: an entropy‐based call‐graph technique

Eichinger¹,

Pankratius

Böhm

2012

Concurrency and Computation

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SUMMARYMulticore computers are ubiquitous. Expert developers as well as developers with little experience in parallelism are now asked to create multithreaded software in order to exploit parallelism in mainstream shared-memory hardware. However, finding and fixing parallel programming errors is a complex and arduous task. Programmers thus rely on tools such as race detectors that typically focus on reporting errors due to incorrect usage of synchronization constructs or due to missing synchronization. This arsenal of debugging techniques, however, is incomplete. This article presents a new perspective and addresses a largely unexplored direction of defect localization where a wrong usage of non-parallel programming constructs might cause wrong parallel application behavior. In particular, we make a contribution by showing how to use data-mining techniques to locate defects in multithreaded shared-memory programs. Our technique analyzes execution anomalies in a condensed representation of the dynamic call graphs of a multithreaded object-oriented application and identifies methods that contain a defect. Compared to race detectors that concentrate on finding incorrect synchronization, our method is able to reveal a wider range of defects that affect the control flow of a parallel program. Results from controlled experiments show that our data-mining approach not only finds race conditions in different types of multicore applications, but also other errors that cause incorrect parallel program behavior. Data-mining techniques offer a fruitful new ground for parallel program debugging, and we also discuss long-term directions for this interesting field.

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Boosted decision trees for behaviour mining of concurrent programmes

Avros

Dudka

Křena

et al. 2017

Concurrency and Computation

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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).

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Localizing Defects in Multithreaded Programs by Mining Dynamic Call Graphs

Cited by 12 publications

References 26 publications

Software-Defect Localisation by Mining Dataflow-Enabled Call Graphs

Software-Defect Localisation by Mining Dataflow-Enabled Call Graphs

Data mining for defects in multicore applications: an entropy‐based call‐graph technique

Boosted decision trees for behaviour mining of concurrent programmes

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