Early quality prediction: a case study in telecommunications

Khoshgoftaar, Taghi M.; Allen, Edward B.; Kalaichelvan, Kalai; Goel, Nishith

doi:10.1109/52.476287

Cited by 175 publications

(83 citation statements)

References 14 publications

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“…The second is to use a weighted sum. The latter are often referred to as " domain metrics" [73]. Instead of using the actual metrics during validation, one can then use the domain metrics, as was done, for example, in [73] [82].…”

Section: The Use Of Principal Componentsmentioning

confidence: 99%

Methodology for Validating Software Product Metrics

Emam

Schneidewind²

2002

Encyclopedia of Software Engineering

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A large number of software product metrics have been proposed in software engineering. We use the term metric here to be consistent with prevailing international standards. Product metrics quantitatively characterize some aspect of the structure of a software product, such as a requirements specification, a design, or source code. They are also commonly collectively known as complexity metrics . While many of these metrics are based on good ideas about what is important to measure in software to capture its complexity, it is still necessary to systematically validate them. Two types of validation are recognized internal and external. Internal validation is a theoretical exercise that ensures that the metric is a proper numerical characterization of the property it claims to measure. Demonstrating that a metric measures what it purports to measure is a form of theoretical validation. Typically, one defines the properties of the attribute that is to be measured, for example, the properties of module coupling. Then one demonstrates analytically that the product metric satisfies these properties. External validation involves empirically demonstrating that the product nature is associated which is important external metter or an improvement is shown above. Internal and external validation are also commonly referred to as theoretical and empirical validation respectively This article presents a comprehensive methodology for validating software product metrics. Our focus will be limited to empirical validation.

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Section: The Use Of Principal Componentsmentioning

confidence: 99%

Methodology for Validating Software Product Metrics

Emam

Schneidewind²

2002

Encyclopedia of Software Engineering

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“…However, other studies argue that the currently-known complexity metrics seem not to be good indicators of faults. The previous studies [6,9] can be replicated in the context of vulnerabilities. However, the results might not be necessarily the same as fault prediction, because even though vulnerabilities are a subset of faults [10], the differences in the characteristics of vulnerable code and faulty code have not been investigated quantitatively yet.…”

Section: Introductionmentioning

confidence: 85%

“…A software vulnerability is an instance of a [fault] in the specification, development, or configuration of software such that its execution can violate an [implicit or explicit] security policy [7]. Previous studies have shown that complexity is related with software faults [6,9]. However, other studies argue that the currently-known complexity metrics seem not to be good indicators of faults.…”

Section: Introductionmentioning

confidence: 98%

Is complexity really the enemy of software security?

Shin

Williams

2008

Proceedings of the 4th ACM Workshop on Quality of Protection

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Software complexity is often hypothesized to be the enemy of software security. We performed statistical analysis on nine code complexity metrics from the JavaScript Engine in the Mozilla application framework to investigate if this hypothesis is true. Our initial results show that the nine complexity measures have weak correlation (ρ=0.30 at best) with security problems for Mozilla JavaScript Engine. The study should be replicated on more products with design and code-level metrics. It may be necessary to create new complexity metrics to embody the type of complexity that leads to security problems.

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“…Complexity and size metrics have been used to predict the number of defects in software components. These include: logistic regression [32], discriminant analysis [33], the discriminant power techniques [34], artificial neural network, genetic algorithm and classification trees. Fenton and Neil proposed the Bayesian belief network as the most effective model to predict software quality.…”

Section: Related Work On Algorithmic Modelsmentioning

confidence: 99%

Performance of Cost Assessment on Reusable Components for Software Development using Genetic Programming

Tejaswini¹,

Devi²,

Krishna³

2015

IJITCS

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Abstract-Reusability is the quality of a piece of software, which enables it to be used again, be it partial, modified or complete. A wide range of modeling techniques have been proposed and applied for software quality predictions. Complexity and size metrics have been used to predict the number of defects in software components. Estimation of cost is important, during the process of software development. There are two main types of cost estimation approaches: algorithmic methods and non-algorithmic methods. In this work, using genetic programming which is a branch of evolutionary algorithms, a new algorithmic method is presented for software development cost estimation, using the implementation of this method; new formulas were obtained for software development cost estimation in which reusability of components is given priority. After evaluation of these formulas, the mean and standard deviation of the magnitude of relative error is better than related algorithmic methods such as COCOMO formulas.Index Terms-COCOMO formulas, cost estimation, genetic programming, magnitude of relative error, reuse of component.

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Early quality prediction: a case study in telecommunications

Cited by 175 publications

References 14 publications

Methodology for Validating Software Product Metrics

Methodology for Validating Software Product Metrics

Is complexity really the enemy of software security?

Performance of Cost Assessment on Reusable Components for Software Development using Genetic Programming

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