Test coverage dependent software reliability estimation by the HGD model

Jacoby, R.; Masuzawa, Koichi

doi:10.1109/issre.1992.285845

Cited by 16 publications

(5 citation statements)

References 7 publications

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“…Code coverage measurements may also be used to estimate the failure parameters of the components [7], [43], [62], [26]. To estimate fault propagation probabilities among the components, a fault injection technique may be used [91], [46].…”

Section: Parameter Estimation Limitationsmentioning

confidence: 99%

Architecture-Based Software Reliability Analysis: Overview and Limitations

Gokhale

2007

IEEE Trans. Dependable and Secure Comput.

171

121

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Section: Parameter Estimation Limitationsmentioning

confidence: 99%

Architecture-Based Software Reliability Analysis: Overview and Limitations

Gokhale

2007

IEEE Trans. Dependable and Secure Comput.

171

121

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“…There are various reliability growth models developed in the literature, but in this section, some significant testing coverage‐based NHPP SRGMs have been discussed. In literature, Jacoby and Masuzawa 34 were the first who introduced the concept of testing coverage while studying reliability growth models. After that, Veevers and Marshall 35 formulated the testing coverage metric and established a relationship between TCF and reliability.…”

Section: Related Workmentioning

confidence: 99%

Testing coverage‐based software reliability growth model considering uncertainty of operating environment

Pradhan

Dhar

Kumar

2023

Systems Engineering

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Software reliability is one of the standard critical inherent characteristics of software systems. The testing coverage function (TCF) is a significant parameter for identifying the completeness and effectiveness of software testing. It is defined as the proportion of the code that has been tested up to time t. To capture the dynamic behavior of the number of faults detected over a period of time, several distributions, namely S‐shaped, inflection S‐shaped, logistic, log‐logistic, Weibull, Rayleigh, Erlang, and logarithmic exponentiated, have been used as TCF in literature. However, these distributions are not sufficient to describe TCF's practical behavior due to complexity and vagueness in the collected data. This study proposes two software reliability growth models (SRGMs), which incorporate the generalized inflection S‐shaped (GISS) distribution as TCF. The models have been developed in perfect and imperfect debugging environments while considering fault removal efficiency, error generation, and uncertainty in the operating environment. To analyze the effectiveness, the proposed models are then tested with six failure data sets. The choice of GISS distribution as a TCF improves the software reliability estimation in comparison with the existing models in the literature. Finally, single and multiple parameters sensitivity analysis also has been done and based on it, the critical parameters have been detected. The proposed models may be helpful for the system analyst to predict various parameters about some software systems.

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“…During the attempts to alleviate the drawbacks of SRGMs, namely the assumed knowledge of the operational profile, several authors discovered that the growth of test code coverage is also directly related to the reliability growth and could be integrated with SRGMs to increase their usability [31,35]. The test code coverage is a measure that describes the degree to which certain elements of the source code have been tested.…”

Section: Test Code Coverage Techniquesmentioning

confidence: 99%

Failure data collection for reliability prediction models

Bühnová

Chren

Fabriková

2014

Proceedings of the 10th International ACM Sigsoft Conference on Quality of Software Architectures

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Design decisions made early in software development have great impact on the software product quality. Design-time reliability prediction is one of the techniques that support software engineers in early design decisions, based on the evaluation of reliability impact of the individual design alternatives. The accuracy of reliability prediction is critically dependent on the accuracy of reliability prediction models, which relies on uncertain failure parameters (such as the failure probability of component-internal actions). Although the effectiveness of the failure-parameter estimation critically influences the usability of the prediction techniques, the parameter estimation often relies on expert knowledge and is not receiving systematic attention. This paper aims to survey existing techniques for estimation and collection of failure parameters in architecture-based reliability prediction models, and presents the findings that can be learned from their detailed analysis.

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Test coverage dependent software reliability estimation by the HGD model

Cited by 16 publications

References 7 publications

Architecture-Based Software Reliability Analysis: Overview and Limitations

Architecture-Based Software Reliability Analysis: Overview and Limitations

Testing coverage‐based software reliability growth model considering uncertainty of operating environment

Failure data collection for reliability prediction models

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