Mining Behavior Graphs for “Backtrace” of Noncrashing Bugs

Liu, Chao; Yan, Xifeng; Yu, Hwanjo; Han, Jiawei; Yu, Philip S.

doi:10.1137/1.9781611972757.26

Cited by 108 publications

(98 citation statements)

References 28 publications

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“…In the following, we will first discuss the application of data mining techniques in this context -bug localisation is just one application. Then we concentrate on two graph mining based approaches [1,2] which are most related to our work. Finally, we describe some related work in the area of mining weighted structures.…”

Section: Related Workmentioning

confidence: 99%

“…The approach from Liu et al [1]: This first study which applies graph mining techniques to dynamic call graphs considers so called software behaviour graphs. These are reduced call graphs, augmented with some temporal information.…”

Section: Call Graph Based Fault Detectionmentioning

confidence: 99%

“…Figure 1(a) is an abstract example of such a call graph. Recent work [1,2] deploys graph mining techniques on call graphs for bug localisation. [2] then derives a ranking of methods which are most probable to contain a bug.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, follow up bugs need to be identified. [2] does not identify follow up bugs at all, and [1] only generates a backtrace-like structure which helps the programmer to find follow up bugs.…”

Section: Introductionmentioning

confidence: 99%

“…An important piece of information included in the raw call graphs is the call frequency of all methods. The reduction techniques in [1,2] loose this information. But it eases detection of bugs which affect the number of invocations of a method, call frequency affecting bugs.…”

Section: Introductionmentioning

confidence: 99%

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Mining Edge-Weighted Call Graphs to Localise Software Bugs

Eichinger¹,

Böhm²,

Huber³

Machine Learning and Knowledge Discovery in Databases

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An important problem in software engineering is the automated discovery of noncrashing occasional bugs. In this work we address this problem and show that mining of weighted call graphs of program executions is a promising technique. We mine weighted graphs with a combination of structural and numerical techniques. More specifically, we propose a novel reduction technique for call graphs which introduces edge weights. Then we present an analysis technique for such weighted call graphs based on graph mining and on traditional feature selection schemes. The technique generalises previous graph mining approaches as it allows for an analysis of weights. Our evaluation shows that our approach finds bugs which previous approaches cannot detect so far. Our technique also doubles the precision of finding bugs which existing techniques can already localise in principle.

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Section: Related Workmentioning

confidence: 99%

Section: Call Graph Based Fault Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Mining Edge-Weighted Call Graphs to Localise Software Bugs

Eichinger¹,

Böhm²,

Huber³

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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Fundamentals of association rules in data mining and knowledge discovery

Zhang

2011

WIREs Data Min & Knowl

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Association rule mining is one of the fundamental research topics in data mining and knowledge discovery that identifies interesting relationships between itemsets in datasets and predicts the associative and correlative behaviors for new data. Rooted in market basket analysis, there are a great number of techniques developed for association rule mining. They include frequent pattern discovery, interestingness, complex associations, and multiple data source mining. This paper introduces the up‐to‐date prevailing association rule mining methods and advocates the mining of complete association rules, including both positive and negative association rules. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 97‐116 DOI: 10.1002/widm.10 This article is categorized under: Algorithmic Development > Association Rules

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Mining Behavior Graphs for “Backtrace” of Noncrashing Bugs

Cited by 108 publications

References 28 publications

Mining Edge-Weighted Call Graphs to Localise Software Bugs

Mining Edge-Weighted Call Graphs to Localise Software Bugs

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

Fundamentals of association rules in data mining and knowledge discovery

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