Assuring software safety

Keene, Samuel

doi:10.1109/arms.1992.187836

Cited by 7 publications

(4 citation statements)

References 9 publications

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“…Since our lives are being controlled by many safety-critical and mission-critical software systems, the concept of software safety becomes recognized as one of the most important topics in the software engineering. Keene (1992) introduced the so-called fault-tree approach to identify the software hazard. Tokuno and Yamada (1999) developed a simple continuous-time Markov model to assess both the software safety and software reliability.…”

Section: Introductionmentioning

confidence: 99%

“…Software safety can be defined as the probability that the software hazard caused by critical errors does not occur during a pre-specified time period (Keene 1992 andLeveson 1986). Since our lives are being controlled by many safety-critical and mission-critical software systems, the concept of software safety becomes recognized as one of the most important topics in the software engineering.…”

Section: Introductionmentioning

confidence: 99%

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Software safety assessment based on a subordinated Markov chain

Okamura

2010

Int J Syst Assur Eng Manag

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Software safety can be defined as the probability that any software hazard does not occur during a prespecified time period. This paper proposes a new software safety assessment model based on a subordinated Markov chain, which consists of both non-homogeneous Poisson process (NHPP) and discrete time Markov chain (DTMC). In the model, NHPP and DTMC indicate the cumulative number of software errors experienced in testing phase and their impact, respectively. Using the data set of three types of software errors (critical, major and minor errors) collected in an actual software development project, we examine the effectiveness of the proposed model by compared with two existing models. Through the experiment, it is shown that the proposed model is superior to the existing ones in terms of fitting ability.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Software safety assessment based on a subordinated Markov chain

Okamura

2010

Int J Syst Assur Eng Manag

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“…Software safety is defined as the property such that no situation inducing a hazard (dangerous condition) is created during the intended period under the specified environment when the software executes a predetermined function [4][5][6]. Keene [5] has used FTA (Fault Tree Analysis) and FMEA (Failure Mode and Effect Analysis) to define the state of unsafe software and has described a method to evaluate software safety. Tokuno and Yamada [8] have described the transition of software between safe and unsafe states by using a continuous time Markov chain (Markov process) and have proposed a mathematical model for the evaluation of software safety.…”

Section: Introductionmentioning

confidence: 99%

Software safety assessment based on a subordinated Markov chain model

Ito

Okamura

2004

Electron Comm Jpn Pt III

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SUMMARYSoftware safety can be defined as the property such that no situation inducing a hazard (dangerous condition) is created during the intended period under a specified environment when the software executes a predetermined function. In this paper, by considering the correlation in regard to the impact of software faults a new software safety assessment model based on a subordinated Markov chain is proposed. The goodness-of-fit is investigated for the software fault detection data observed in the actual test process, and we show the effectiveness of the proposed model in software safety evaluation.

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“…Fault tree analysis (FTA) and failure mode and effect analysis (FMEA) are representative qualitative safety-assessment techniques [8]. These are effective tools for investigation and verification of reliability and safety in the specification and the design phase.…”

Section: Introductionmentioning

confidence: 99%

Quantitative assessment models for software safety/reliability

Yamada

Tokuno

Kasano

1998

Electron. Comm. Jpn. Pt. II

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Safety and reliability have become important software quality characteristics in the development of safety‐critical software systems. However, there are so far no quantitative methods for assessing a safety‐critical software system in terms of safety/reliability characteristics. The metric of software safety is defined as the probability that conditions that can lead to hazards do not occur. In this paper, we propose two stochastic models for software safety/reliability assessment: the data‐domain dependent safety assessment model and the availability‐related safety assessment model. These models focus on describing the time‐ or execution dependent behavior of software faults that can lead to unsafe states when they cause software failures. Numerical examples are also provided for quantitative software safety assessment. © 1998 Scripta Technica, Electron Comm Jpn Pt 2, 81(5): 33–43, 1998

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Assuring software safety

Cited by 7 publications

References 9 publications

Software safety assessment based on a subordinated Markov chain

Software safety assessment based on a subordinated Markov chain

Software safety assessment based on a subordinated Markov chain model

Quantitative assessment models for software safety/reliability

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