Understanding Open Source Software Evolution Using Fuzzy Data Mining Algorithm for Time Series Data

Saini, Munish; Mehmi, Sandeep; Chahal, Kuljit Kaur

doi:10.1155/2016/1479692

Cited by 1 publication

(1 citation statement)

References 18 publications

(42 reference statements)

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“…Besides, a survey of diverse open source tools developed for measuring the internal quality of Java software products was examined in [18]. A fuzzy data mining algorithm was intended in [19] for time series data to generate the association rules for evaluating the existing trend and regularity in the evolution of open source software project. Quality Management for achieving higher software quality in the Service Sector was presented in [20].…”

Section: Related Workmentioning

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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Section: Related Workmentioning

confidence: 99%

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

Chennappan¹

2017

ijarcs

View full text Add to dashboard Cite

show abstract

Understanding Open Source Software Evolution Using Fuzzy Data Mining Algorithm for Time Series Data

Cited by 1 publication

References 18 publications

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

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

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