Applying Code Coverage Approach to an Infinite Failure Software Reliability Model

Crespo, Adalberto Nobiato; Pasquini, Alberto; Jino, Mário; Maldonado, José Carlos

doi:10.1109/sbes.2009.33

Cited by 5 publications

(9 citation statements)

References 27 publications

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“…In order to relate such SRGMs to the testing time, Park and Fujiwara (2006), Park et al (2007Park et al ( , 2008a and Park and Lee (2010) studied CGFs. Crespo et al (2008Crespo et al ( , 2009) recently proposed new SRGMs that simultaneously incorporate testing time and coverage.…”

Section: Introductionmentioning

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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Section: Introductionmentioning

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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“…Such a relationship is obtained by modeling the fault detection and removal process during testing; however, it was recognized that the testing time was not enough to express the fault detection and removal process. Attempts to integrate coverage information into SRGMs have been made by Gokhale et al (1996), Malaiya et al (2002), Pham and Zhang (2003), Park and Fujiwara (2006), Crespo et al (2008, 2009), and Park et al (2008b. Each coverage-based SRGM involves a coverage growth function(CGF), that describes the coverage growth process during testing.…”

Section: Introductionmentioning

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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“…However, it was recognized that the testing time was insufficient to express the fault detection and removal process. Attempts to integrate coverage information into SRGMs have been made by Gokhale et al (1996), Malaiya et al (2002), Pham and Zhang (2003), Park and Fujiwara (2006), Crespo et al (2008Crespo et al ( , 2009 and Park et al (2008b). Each coverage-based SRGM involves a coverage growth function(CGF) that describes the coverage growth process during testing.…”

Section: Introductionmentioning

confidence: 99%

Selection of a Predictive Coverage Growth Function

Park¹,

Lee²

2010

Communications for Statistical Applications and Methods

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A trend in software reliability engineering is to take into account the coverage growth behavior during testing. A coverage growth function that represents the coverage growth behavior is an essential factor in software reliability models. When multiple competitive coverage growth functions are available, there is a need for a criterion to select the best coverage growth functions. This paper proposes a selection criterion based on the prediction error. The conditional coverage growth function is introduced for predicting future coverage growth. Then the sum of the squares of the prediction error is defined and used for selecting the best coverage growth function.

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Applying Code Coverage Approach to an Infinite Failure Software Reliability Model

Cited by 5 publications

References 27 publications

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

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

Estimation of Coverage Growth Functions

Selection of a Predictive Coverage Growth Function

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