Metrics and Software Quality Evolution: A Case Study on Open Source Software

Drouin, Nicholas; Badri, Mourad; Touré, Fadel

doi:10.7763/ijcte.2013.v5.742

Cited by 11 publications

(4 citation statements)

References 25 publications

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“…A novel approach was designed in [8] for evaluating the stability of open-source software systems with aid of combination of Bayesian Classifiers. In [9], the quality evolution of an open source Java software system was examined with aid of metrics in which software quality was addressed from an internal point of view. However, the number of defects as a quality indicator was considered.…”

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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Section: Introductionmentioning

confidence: 99%

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

Chennappan¹

2017

ijarcs

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“…Various flaws and inconsistencies have been observed in the suite of six class-based metrics [23]. Validation is the process of evaluating a system or component during or at the end of the development process to determine whether it satisfies specified requirements or not and validation should establish confidence that the software is fit for its purpose and does what the user really requires [16].…”

Section: Introductionmentioning

confidence: 99%

Software Metrics Validation Methodologies in Software Engineering

Srinivasan¹,

Devi²

2014

IJSEA

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In the software measurement validations, assessing the validation of software metrics in software engineering is a very difficult task due to lack of theoretical methodology and empirical methodology [41, 44, 45]. During recent years, there have been a number of researchers addressing the issue of validating software metrics. At present, software metrics are validated theoretically using properties of measures. Further, software measurement plays an important role in understanding and controlling software development practices and products. The major requirement in software measurement is that the measures must represent accurately those attributes they purport to quantify and validation is critical to the success of software measurement. Normally, validation is a collection of analysis and testing activities across the full life cycle and complements the efforts of other quality engineering functions and validation is a critical task in any engineering project. Further, validation objective is to discover defects in a system and assess whether or not the system is useful and usable in operational situation. In the case of software engineering, validation is one of the software engineering disciplines that help build quality into software. The major objective of software validation process is to determine that the software performs its intended functions correctly and provides information about its quality and reliability. This paper discusses the validation methodology, techniques and different properties of measures that are used for software metrics validation. In most cases, theoretical and empirical validations are conducted for software metrics validations in software engineering [1-50].

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“…Results provide evidence that the Qi metric is able to accurately predict the unit testing effort of classes. More recently, we explored in [18] if the Qi metric can be used to observe how quality, measured in terms of defects, evolves in the presence of changes and in [19] if the Qi metric captures the evolution of two important OO metrics (related to coupling and complexity).…”

Section: Introductionmentioning

confidence: 99%

Analyzing Software Quality Evolution using Metrics: An Empirical Study on Open Source Software

Drouin¹,

Badri²,

Touré³

2013

JSW

Self Cite

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The study presented in this paper aims at analyzing empirically the quality of evolving software systems using metrics. We used a synthetic metric (Quality Assurance Indicator - Qi), which captures in an integrated way different object-oriented software attributes. We wanted to investigate if the Qi metric can be used to observe how quality evolves along the evolution of software systems. We consider software quality from an internal (structural) perspective. We used various object-oriented design metrics for measuring the structural quality of a release. We performed an empirical analysis using historical data collected from successive released versions of three open source (Java) software systems. The collected data cover, for each system, a period of several years (4 years for two systems and 7 years for the third one). We focused on three issues: (1) the evolution of the Qi metric along the evolution of the subject systems, (2) the class growth of the subject systems, and (3) the quality of added classes versus the quality of removed ones. Empirical results provide evidence that the Qi metric reflects properly the quality evolution of the studied software systems.

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Metrics and Software Quality Evolution: A Case Study on Open Source Software

Cited by 11 publications

References 25 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

Software Metrics Validation Methodologies in Software Engineering

Analyzing Software Quality Evolution using Metrics: An Empirical Study on Open Source Software

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