Sensitivity of reliability-growth models to operational profile errors vs. testing accuracy [software testing]

Pasquini, Alberto; Crespo, Adalberto Nobiato; Matrella, P.

doi:10.1109/24.556576

Cited by 76 publications

(38 citation statements)

References 21 publications

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“…For instance, in our case study, using a CP specification of nine categories and 33 choices and a test suite of 221 test cases, it took a couple of seconds to create 221 abstract test cases. We also used this technology for a different purpose in [9] and with a larger problem: the Space program [32]; for which we defined 83 categories and 582 choices, and abstracted 13,585 test cases in less than a minute. In short, Activity 1 in Fig.…”

Section: Manual Effort and Automationmentioning

confidence: 99%

Using machine learning to refine Category-Partition test specifications and test suites

Briand

Labiche

Bawar

et al. 2009

Information and Software Technology

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Section: Manual Effort and Automationmentioning

confidence: 99%

Using machine learning to refine Category-Partition test specifications and test suites

Briand

Labiche

Bawar

et al. 2009

Information and Software Technology

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“…The first data set, DS1, was collected by Pasquini et al (1996) The second data set, DS2, was collected, as a simulation of testing, by Gokhale and Mullen (2004) with SHARPE(Symbolic Hierarchical Automated Reliability and Performance Evaluator) that solves stochastic models of reliability. SHARPE contains 35,081 lines of C code and has a total of 373 functions.…”

Section: Numerical Examplesmentioning

confidence: 99%

Estimation of Coverage Growth Functions

Park¹,

Lee

Kim³

2011

Communications for Statistical Applications and Methods

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A recent trend in software reliability engineering accounts for the coverage growth behavior during testing. The coverage growth function (representing the coverage growth behavior) has become an essential component of software reliability models. Application of a coverage growth function requires the estimation of the coverage growth function. This paper considers the problem of estimating the coverage growth function. The existing maximum likelihood method is reviewed and corrected. A method of minimizing the sum of squares of the standardized prediction error is proposed for situations where the maximum likelihood method is not applicable.

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“…One of the three data sets is considered in this paper and called DS1. Pasquini et al (1996) reported a data set collected from a configuration software for an array of antennas developed by European Space Agency. The data set, called DS2, contains the values of block, branch, c-use and p-use coverages.…”

Section: Correlation Between Coverage Metricsmentioning

confidence: 99%

Virtual Coverage: A New Approach to Coverage-Based Software Reliability Engineering

Park¹,

Lee

2013

Communications for Statistical Applications and Methods

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It is common to measure multiple coverage metrics during software testing. Software reliability growth models and coverage growth functions have been applied to each coverage metric to evaluate software reliability; however, analysis results for the individual coverage metrics may conflict with each other. This paper proposes the virtual coverage metric of a normalized first principal component in order to avoid conflicting cases. The use of the virtual coverage metric causes a negligible loss of information.

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Sensitivity of reliability-growth models to operational profile errors vs. testing accuracy [software testing]

Cited by 76 publications

References 21 publications

Using machine learning to refine Category-Partition test specifications and test suites

Using machine learning to refine Category-Partition test specifications and test suites

Estimation of Coverage Growth Functions

Virtual Coverage: A New Approach to Coverage-Based Software Reliability Engineering

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