Discriminative pattern mining in software fault detection

Fatta, Giuseppe Di; Leue, Stefan; Stegantova, Evghenia

doi:10.1145/1188895.1188910

Cited by 44 publications

(62 citation statements)

References 21 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%

“…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%

“…The reduced graphs keep much information, while they are relatively small. For example, [2] would reduce one call graph from our evaluation from 9,946 to 50 edges. With our technique, there are just 31 edges, while keeping more information and, ultimately, giving way to better results.…”

Section: Introductionmentioning

confidence: 99%

See 4 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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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: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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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Spectral‐based fault localization using hyperbolic function

2017

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Debugging is crucial for producing reliable software. One of the effective bug localization techniques is spectral-based fault localization. It tries to locate a buggy statement by applying an evaluation metric to program spectra and ranking program components on the basis of the score it computes. Here, we propose a restricted class of "hyperbolic" metrics, with a small number of numeric parameters. This class of functions is based on past theoretical and empirical results. We show that optimization methods such as genetic programming and simulated annealing can reliably discover effective metrics over a wide range of data sets of program spectra. We evaluate the performance for both real programs and model programs with single bugs, multiple bugs, "deterministic" bugs, and nondeterministic bugs and find that the proposed class of metrics performs as well as or better than the previous best-performing metrics over a broad range of data.

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Discriminative pattern mining in software fault detection

Cited by 44 publications

References 21 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

Spectral‐based fault localization using hyperbolic function

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