An observation-based model for fault localization

Abreu, Rui; Zoeteweij, Peter; Gemund, Arjan J. C. van

doi:10.1145/1401827.1401841

Cited by 31 publications

(48 citation statements)

References 15 publications

(20 reference statements)

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“…Regarding the latter restriction, Spectrum-based fault localization naturally extends to the multiple-fault case by iteratively applying the technique until all faults are repaired (see, e.g., Jones et al, 2002). However, we recently applied model-based diagnosis techniques to derive multiple-fault explanations directly from program spectra and pass/fail information (see Abreu et al (2008c) for an account of the approach), and further investigation is in progress to compare the debugging effort implied by the two approaches on multiple-fault programs. Regarding the restriction to the C programming language, we expect the technique to work well on other programming paradigms and languages.…”

Section: Discussionmentioning

confidence: 99%

A practical evaluation of spectrum-based fault localization

Abreu

Zoeteweij

Golsteijn

et al. 2009

Journal of Systems and Software

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399

363

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Section: Discussionmentioning

confidence: 99%

A practical evaluation of spectrum-based fault localization

Abreu

Zoeteweij

Golsteijn

et al. 2009

Journal of Systems and Software

Self Cite

399

363

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“…The first family of related debugging techniques are statistical fault localization techniques, which performs fault localization based on a large number of passing and failing executions using statistical inference [5], [6], [18], [20], [22], [31]. The major limitation of these techniques is that their results highly depend on the quality of test cases [25].…”

Section: B Software Debuggingmentioning

confidence: 99%

Reproducing and debugging field failures in house

Jin¹

2013

2013 35th International Conference on Software Engineering (ICSE)

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As confirmed by a recent survey among developers of the Apache, Eclipse, and Mozilla projects, failures of the software that occur in the field, after deployment, are difficult to reproduce and investigate in house. To address this problem, we propose an approach for in-house reproducing and debugging failures observed in the field. This approach can synthesize several executions similar to an observed field execution to help reproduce the observed field behaviors, and use these executions, in conjunction with several debugging techniques, to identify causes of the field failure. Our initial results are promising and provide evidence that our approach is able to reproduce failures using limited field execution information and help debugging.

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“…Before computing Pr(d k ) the h j must be estimated from (A, e). There are several approaches that approximate h j by computing the probability that the combination of components involved in a particular d k produce a failure, instead of computing the individual component intermittency rate values [3,10]. Although such approaches already give significant improvement over the classical model-based reasoning (see [4] for results), more accurate results can be achieved if the individual h j can be determined by an exact estimator.…”

Section: Candidate Rankingmentioning

confidence: 99%

Architecture-Based Run-Time Fault Diagnosis

Casanova

Schmerl

Garlan

et al. 2011

Software Architecture

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Abstract. An important step in achieving robustness to run-time faults is the ability to detect and repair problems when they arise in a running system. Effective fault detection and repair could be greatly enhanced by run-time fault diagnosis and localization, since it would allow the repair mechanisms to focus adaptation effort on the parts most in need of attention. In this paper we describe an approach to run-time fault diagnosis that combines architectural models with spectrum-based reasoning for multiple fault localization. Spectrum-based reasoning is a lightweight technique that takes a form of trace abstraction and produces a list (ordered by probability) of likely fault candidates. We show how this technique can be combined with architectural models to support run-time diagnosis that can (a) scale to modern distributed software systems; (b) accommodate the use of black-box components and proprietary infrastructure for which one has neither a specification nor source code; and (c) handle inherent uncertainty about the probable cause of a problem even in the face of transient faults and faults that arise only when certain combinations of system components interact.

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An observation-based model for fault localization

Cited by 31 publications

References 15 publications

A practical evaluation of spectrum-based fault localization

A practical evaluation of spectrum-based fault localization

Reproducing and debugging field failures in house

Architecture-Based Run-Time Fault Diagnosis

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