Integrated Metrics Handling in Open Source Software Quality Management Platforms

Escribano-Barreno, Julio; García-Muñoz, Javier; García-Valls, Marisol

doi:10.1007/978-3-319-32467-8_45

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…To ensure proper assessment of the visual quality of the mesh after the treatment, it is necessary to estimate its visual quality. The mean of the perceptual quality metrics is to judge the quality of a mesh based on human perception characteristics (Escribano-Barreno, & García-Muñoz, 2016).…”

Section: Objective Measures Of Quality Meshesmentioning

confidence: 99%

Perceptual Metrics Quality

Elloumi

Kacem²,

Nilanjan

et al. 2017

International Journal of Service Science, Management, Engineering, and Technology

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A 3D mesh can be subjected to different types of operations, such as compression, watermarking etc. Such processes lead to geometric distortions compared to the original version. In this context, quantifying the resultant modifications to the original mesh and evaluating the perceptual quality of degraded meshes become a critical issue. The perceptual 3D meshes quality is central in several applications to preserve the visual appearance of these treatments. The used metrics results have to be well correlated to the visual perception of humans. Although there are objective metrics, they do not allow the prediction of the perceptual quality, and do not include the human visual system properties. In the current work, a comparative study between the perceptual quality assessment metrics for 3D meshes was conducted. The experimental study on subjective database published by LIRIS / EPFL was used to test and to validate the results of six metrics. The results established that the Mesh Structural Distortion Measure metric achieved superior results compared to the other metrics.

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Section: Objective Measures Of Quality Meshesmentioning

confidence: 99%

Perceptual Metrics Quality

Elloumi

Kacem²,

Nilanjan

et al. 2017

International Journal of Service Science, Management, Engineering, and Technology

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“…But, reducing the cost of software quality testing was remained unsolved. An initial design and development of an integrated analyser component was intended in [3] for enlarging the functionality of the open source framework for software quality management. But, multiple software maintenance handled over time results in software quality degradation and also increases the service provisioning time.…”

Section: Introductionmentioning

confidence: 99%

Autocorrelation Weighted Sum Entropy Based Software Quality Management for Open Source Application

Chennappan¹

2017

ijarcs

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Software metrics is used to evaluate software systems quality and to improve the software reliability. Recently, few researches have been developed for enhancing the quality of open source software using Software metrics. But, the software quality management performance of existing works was not efficient while performing multiple software operations which affect the software reliability. To attain higher scalability rate with reduced service provisioning time while improving the reliability of software quality, a component model called Autocorrelation Weighted Sum Entropy (AWSE) technique is proposed. To minimize software quality degradation while performing multiple software operations, a service provisioning time entropy is considered. The AWSE technique initially measures autocorrelation function for consecutive versions of same application to find the relationship between a conventional versions and contemporary version. After that, AWSE technique computes autocorrelation for service provisioning time entropy that considers the effect of maintenance operations carried out both on contemporary versions and on the conventional versions. Then, AWSE technique measures average time entropy to obtain the time entropy of consecutive versions of same application. This in turn helps for reducing the service provisioning time and improving the scalability of software quality management. Finally, AWSE technique used Weighted Sum Entropy (WSE) model that considers the Mean Time between Failure (MTF) to improve the software reliability in a specified environment for a given amount of time and to reduce the cost of software quality testing. The AWSE technique conducts the experimental works on parameters such as scalability, service provisioning time and software reliability. The experimental result shows that the AWSE technique is able to improve the scalability and also reduces the service provisioning time of software quality management when compared to state-of-the-art-works.

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