Model-Based Debugging or How to Diagnose Programs Automatically

Wotawa, Franz; Stumptner, Markus; Mayer, Wolfgang

doi:10.1007/3-540-48035-8_72

Cited by 49 publications

(35 citation statements)

References 7 publications

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“…There are other fault localization techniques including, but not limited to, data mining-based (e.g., Cellier et al [5] which discuss a combination of association rules and Formal Concept Analysis (FCA) to assist in fault localization), and model-based (e.g., [28]). Similarity-based coefficients such as Ochiai & Jaccard are also used in [1], [9].…”

Section: Other Techniquesmentioning

confidence: 99%

Software Fault Localization

Wong¹,

Debroy²

2010

Encyclopedia of Software Engineering

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Section: Other Techniquesmentioning

confidence: 99%

Software Fault Localization

Wong¹,

Debroy²

2010

Encyclopedia of Software Engineering

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“…Wotawa, et al (74) propose to construct dependency models based on a source code analysis of the target programs to represent program structures and behaviors in the first order logic. Test cases with expected outputs are also transformed into observations in terms of first order logic.…”

Section: Model-based Methodsmentioning

confidence: 99%

A Survey on Software Fault Localization

Wong

Gao

et al. 2016

IIEEE Trans. Software Eng.

Self Cite

819

545

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Software fault localization is one of the (if not the) most expensive, tedious and time consuming activities in program debugging. Therefore, there is a high demand for automatic fault localization techniques that can guide programmers to the locations of faults, with minimal human intervention. This demand has led to the proposal and development of various methods, each of which seeks to make the fault localization process more effective in its own unique and creative way. In this article we provide an overview of several such methods and discuss some of the key issues and concerns that are relevant to fault localization.

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“…An example of a white box technique is model-based diagnosis (see, e.g., [6,7]), where a diagnosis is obtained by logical inference from a formal model of the system, combined with a set of run-time observations. While white box approaches to software diagnosis exist (see, e.g., [14,17,18,19]), software modeling is extremely complex. Hence, most software diagnosis techniques are black box.…”

Section: Introductionmentioning

confidence: 99%

An Evaluation of Similarity Coefficients for Software Fault Localization

Abreu

Zoeteweij

Gemund

2006

2006 12th Pacific Rim International Symposium on Dependable Computing (PRDC'06)

388

357

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Automated diagnosis of software faults can improve the efficiency of the debugging process, and is therefore an important technique for the development of dependable software. In this paper we study different similarity coefficients that are applied in the context of a program spectral approach to software fault localization (single programming mistakes). The coefficients studied are taken from the systems diagnosis / automated debugging tools Pinpoint, Tarantula, and AMPLE, and from the molecular biology domain (the Ochiai coefficient). We evaluate these coefficients on the Siemens Suite of benchmark faults, and assess their effectiveness in terms of the position of the actual fault in the probability ranking of fault candidates produced by the diagnosis technique. Our experiments indicate that the Ochiai coefficient consistently outperforms the coefficients currently used by the tools mentioned. In terms of the amount of code that needs to be inspected, this coefficient improves 5% on average over the next best technique, and up to 30% in specific cases.

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Model-Based Debugging or How to Diagnose Programs Automatically

Cited by 49 publications

References 7 publications

Software Fault Localization

Software Fault Localization

A Survey on Software Fault Localization

An Evaluation of Similarity Coefficients for Software Fault Localization

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