Automated Fault Diagnosis in Embedded Systems

Zoeteweij, Peter; Pietersma, Jurryt; Abreu, Rui; Feldman, Alexander; Gemund, Arjan J. C. van

doi:10.1109/ssiri.2008.48

Cited by 17 publications

(9 citation statements)

References 14 publications

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“…In general, however, the faulty component(s) may be outranked by other components, entailing non-zero search effort by the users. Research has shown that for small programs (O(100) lines) 5 − 20% of the code remains to be inspected by the user [12]. However, for large programs this fraction drops to less than a percent [3], making SFL an interesting debugging aid.…”

Section: Fault Localizationmentioning

confidence: 99%

Zoltar

Janssen

Abreu

Gemund

2009

Proceedings of the 2009 ESEC/FSE Workshop on Software Integration and Evolution @ Runtime

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Locating software components which are responsible for observed failures is the most expensive, error-prone phase in the software development life cycle. Automated diagnosis of software faults can improve the efficiency of the debugging process, and is therefore an important process for the development of dependable software. In this paper we present a toolset for automatic fault localization, dubbed Zoltar, which adopts a spectrum-based fault localization technique. The toolset provides the infrastructure to automatically instrument the source code of software programs to produce runtime data, which is subsequently analyzed to return a ranked list of likely faulty locations. Aimed at total automation (e.g., for runtime fault diagnosis), Zoltar has the capability of instrumenting the program under analysis with fault screeners, for automatic error detection. Using a small thread-based example program as well as a large realistic program, we show the applicability of the proposed toolset.

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Section: Fault Localizationmentioning

confidence: 99%

Zoltar

Janssen

Abreu

Gemund

2009

Proceedings of the 2009 ESEC/FSE Workshop on Software Integration and Evolution @ Runtime

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“…Manifestations of (b) are frequently found in today's era of the Internet of Things (IoT), distributed or autonomous systems, and ubiquitous computing, where low-end microprocessors, often with only a small amount of RAM, are incorporated into almost any device. Whenever such devices should perform (self-)diagnosing actions [27,28], memory-limited diagnosis algorithms are a must [29,30].…”

Section: Introductionmentioning

confidence: 99%

Memory-limited model-based diagnosis

Rodler

2022

Artificial Intelligence

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“…As the spectra are captured by automatic profiling, and as the test oracles are readily implemented from existing specifications, no modeling effort is required. Benchmark studies, as well as case studies by the authors diagnosing embedded software (100 KLOC) from Philips Semiconductors (now NXP) have shown promising results [5], [6], [7]. Recently, a model-based approach to SFL has been presented [8] where the statistical approach has been replaced by a reasoning approach.…”

Section: Introductionmentioning

confidence: 99%

Automatic systems diagnosis without behavioral models

Gupta

Abreu

Kleer

et al. 2014

2014 IEEE Aerospace Conference

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Abstract-Recent feedback obtained while applying Modelbased diagnosis (MBD) in industry suggests that the costs involved in behavioral modeling (both expertise and labor) can outweigh the benefits of MBD as a high-performance diagnosis approach. In this paper, we propose an automatic approach, called ANTARES, that completely avoids behavioral modeling. Decreasing modeling sacrifices diagnostic accuracy, as the size of the ambiguity group (i.e., components which cannot be discriminated because of the lack of information) increases, which in turn increases misdiagnosis penalty. ANTARES further breaks the ambiguity group size by considering the component's false negative rate (FNR), which is estimated using an analytical expression. Furthermore, we study the performance of ANTARES for a number of logic circuits taken from the 74XXX/ISCAS benchmark suite. Our results clearly indicate that sacrificing modeling information degrades the diagnosis quality. However, considering FNR information improves the quality, attaining the diagnostic performance of an MBD approach.

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Automated Fault Diagnosis in Embedded Systems

Cited by 17 publications

References 14 publications

Zoltar

Zoltar

Memory-limited model-based diagnosis

Automatic systems diagnosis without behavioral models

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