The variation of software survival time for different operational input profiles (or why you can wait a long time for a big bug to fail)

Bishop, Peter

doi:10.1109/ftcs.1993.627312

Cited by 74 publications

(72 citation statements)

References 11 publications

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“…A more comprehensive study was conducted by Bishop (1993), who examined program faults in control functions for nuclear reactors. He found that virtually all the faults were "blob" faults -that is, each fault revealed failures in a contiguous region of the input domain.…”

Section: Failure Patternsmentioning

confidence: 99%

Adaptive Random Testing: The ART of test case diversity

Chen

Kuo

Merkel

et al. 2010

Journal of Systems and Software

292

227

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Random testing is not only a useful testing technique in itself, but also plays a core role in many other testing methods. Hence, any significant improvement to random testing has an impact throughout the software testing community. Recently, Adaptive Random Testing (ART) was proposed as an effective alternative to random testing. This paper presents a synthesis of the most important research results related to ART. In the course of our research and through further reflection, we have realised how the techniques and concepts of ART can be applied in a much broader context, which we present here. We believe such ideas can be applied in a variety of areas of software testing, and even beyond software testing. Amongst these ideas, we particularly note the fundamental role of diversity in test case selection strategies. We hope this paper serves to provoke further discussions and investigations of these ideas.

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Section: Failure Patternsmentioning

confidence: 99%

Adaptive Random Testing: The ART of test case diversity

Chen

Kuo

Merkel

et al. 2010

Journal of Systems and Software

292

227

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show abstract

“…For example, the operational profile ) (x U has been introduced in various reliability models to handle the relative frequencies of inputs [28]. As another example, the EL and LM models use a random parameter  to describe the uncertainty in both software structures and residual faults, i.e.…”

Section: Failure Distribution (Fd) Modelmentioning

confidence: 99%

A Diversity Model Based on Failure Distribution and its Application in Safety Cases

Chen

May

2016

IEEE Trans. Rel.

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Abstract-This work develops a new basis for evaluating the reliability benefits of diverse software, based on fault injection testing. In particular, the work investigates new forms of argumentation that could in principle be used to justify diversity as a basis for the construction of safety claims. Failure distributions of two versions of diverse software under various fault conditions are revealed separately by fault injection methods, and then the common failure probability of the version-pair can be estimated. The approach is justified theoretically, and cross validated with other work. This method is also used to explain the fundamental influence of failure distributions on diversity. Furthermore, the unique capabilities of the method are demonstrated by implementation of the fault injection test on a program pair Index Terms-Software Diversity, Safety Case, Safety Critical System, Fault Injection, Multi-version, Reliability. Abbreviations: AFRAverage failure rate CFP Common failure probability EL Eckhardt and Lee FD Failure distribution FI Fault injection IFP s-Independent failures model LM Littlewood and Miller MR Meulen and Revilla MSF Mmodified sign function NVP New version-pair RI Reliability improvement SFP

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“…Experiments and theoretical justifications show the existence of contiguous failure regions in the program input space and that, for many applications, such as real-time control systems, the inputs often follow a trajectory of contiguous points in the input space. For these reasons the inputs which originate failures of the software are very rarely isolated events but more likely grouped in clusters [Amman88,Bishop93,Bishop88]. For all the classes of applications to which these considerations apply, analyses of software dependability performed assuming independence between successive iterations seem to lead to results excessively diverging from the real behaviour of the analysed system.…”

Section: Introductionmentioning

confidence: 99%

Dependability of Iterative Software: A Model for Evaluating the Effects of Input Correlation

et al. 1995

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This paper deals with the dependability evaluation of software programs of iterative nature. In this work we define a new model that is able to account for both dependencies between input values of successive iterations and the effects of sequences of consecutive failures. Differently from previously proposed models, it is based on steady state probabilities of an iteration outcome (success, benign failure or catastrophic failure) and those representing the correlation. As such it allows to analyse the effects of the correlation between successive inputs on the dependability attributes of iterative software and assumes, as starting knowledge, information usually obtainable by testing. Requiring the designers or users to determine just the steady state probabilities of an iteration outcome, rather than difficult and costly state transition probabilities, this model is more useful and more generally applicable.

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The variation of software survival time for different operational input profiles (or why you can wait a long time for a big bug to fail)

Cited by 74 publications

References 11 publications

Adaptive Random Testing: The ART of test case diversity

Adaptive Random Testing: The ART of test case diversity

A Diversity Model Based on Failure Distribution and its Application in Safety Cases

Dependability of Iterative Software: A Model for Evaluating the Effects of Input Correlation

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