Synergies and conflicts among software quality attributes and bug fixes

Shatnawi, Raed

doi:10.1504/ijiscm.2017.10007287

Cited by 3 publications

(4 citation statements)

References 16 publications

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“…These projects have been used to deploy deep belief network to learn semantic features to enhance the learned semantic features in order to build a machine learning prediction models . On the other hand, Reference used these projects to compare the performances of bagging classifiers like naive Bayes classifiers, and another research was developed in Reference to find the empirical evidence of the interrelationships among six quality attributes (effectiveness, functionality, reusability, flexibility, understandability, and extendibility) with bugs. Table shows the full description for the Java open‐source projects dataset that is used in this article.…”

Section: Methodsmentioning

confidence: 99%

Software fault prediction using particle swarm algorithm with genetic algorithm and support vector machine classifier

Alsghaier

Akour

2020

Softw Pract Exp

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Summary Software fault prediction is a process of developing modules that are used by developers in order to help them to detect faulty classes or faulty modules in early phases of the development life cycle and to determine the modules that need more refactoring in the maintenance phase. Software reliability means the probability of failure has occurred during a period of time, so when we describe a system as not reliable, it means that it contains many errors, and these errors can be accepted in some systems, but it may lead to crucial problems in critical systems like aircraft, space shuttle, and medical systems. Therefore, locating faulty software modules is an essential step because it helps defining the modules that need more refactoring or more testing. In this article, an approach is developed by integrating genetics algorithm (GA) with support vector machine (SVM) classifier and particle swarm algorithm for software fault prediction as a stand though for better software fault prediction technique. The developed approach is applied into 24 datasets (12‐NASA MDP and 12‐Java open‐source projects), where NASA MDP is considered as a large‐scale dataset and Java open‐source projects are considered as a small‐scale dataset. Results indicate that integrating GA with SVM and particle swarm algorithm improves the performance of the software fault prediction process when it is applied into large‐scale and small‐scale datasets and overcome the limitations in the previous studies.

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Section: Methodsmentioning

confidence: 99%

Software fault prediction using particle swarm algorithm with genetic algorithm and support vector machine classifier

Alsghaier

Akour

2020

Softw Pract Exp

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“…Each one of these datasets has at least three consecutive releases, it is widely used in prediction models because it allows building defect predictors based on previous data which will help to predict defects in future releases. These projects have been used to deploy Deep Belief Network to learn semantic features to enhance the learned semantic features in order to build a machine learning prediction models, another research developed by Shatnawi 46 to find the empirical evidence of the inter‐relationships among six quality attributes (effectiveness, functionality, reusability, flexibility, understandability, and extendibility) with bugs. Table 3 shows the full description for the Java open source projects dataset that is used in this paper.…”

Section: Methodsmentioning

confidence: 99%

Software fault prediction using Whale algorithm with genetics algorithm

Alsghaier

Akour

2020

Softw Pract Exp

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Software fault prediction became an essential research area in the last few years, there are many prediction and optimization techniques that have been developed for fault prediction. In this paper, an approach is developed by integrating genetics algorithm with support vector machine (SVM) classifier and Whale optimization algorithm for software fault prediction. The developed approach is applied to 24 datasets (12‐NASA MDP and 12‐Java open‐source projects), where NASA MDP is considered as a large‐scale dataset, and Java open source projects are considered as a small‐scale dataset. Results indicate that integrating Genetics algorithm with SVM and Whale algorithm improves the performance of the software fault prediction process when it is applied to large‐scale and small‐scale datasets and overcome the limitations that appeared in the previous studies.

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“…Lots of models of quality have put forward links between external factors/attributes of quality and internal properties of software. It was mentioned by [8] [17] with hierarchies of measures, criteria and factors, and eleven external factors of quality proposed, namely: flexibility, maintainability, portability, testability, integrity, usability, efficiency, reliability, correctness (functionality), interoperability, and reusability. A model of quality was proposed by [18] that consisted of 3 characteristic levels: the high-level, the intermediate-level and the primitive-level.…”

Section: Models Of Software Qualitymentioning

confidence: 99%

“…The factor that is the highest determining one with regard to achievement is the quality and functionality for the outcome of the software and success in terms of external goals, including user satisfaction. The production of high quality software is essential so that the expectations of customers can be met [8]. The definition for quality of software, as provided within American National Standard Institute (ANSI)/American society for quality control (ASQC) standard (1978), is that it is a totality of characteristics and features of a service or product that have a bearing upon the ability it has to satisfy the needs given.…”

Section: Introductionmentioning

confidence: 99%

Framework for selecting the best software quality model for a smart health application based on intelligent approach

Saleh

Enaizan

2023

Bulletin EEI

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There is difficulty in knowing how to weigh the factors of software quality models so that decision-making can be eased. Furthermore, previous work was limited to undertake evaluation and selection of appropriate software quality model based upon multi-criteria in the context of smart health applications. This paper aims to evaluate and select an appropriate model of software quality based on multi-criteria decision-making (MCDM) by three phases of framework. Firstly, investigation of software quality models and factors that were identified based on ‘fuzzy delphi’. Secondly, identification of quality models that have uniform multi-criteria so that a decision matrix could be established. Uniform multi-criteria were used in the decision matrix as the basis of the models of quality and the multi-criteria. Subsequently, MCDM approach is adopted and the bases used in the employment of the MCDM approach for the eva luation and selection of the software quality model were technique for order preference by similarity to ideal solution (TOPSIS) and fuzzy analytical hierarchy process (FAHP). The results demonstrated that seven quality factors could be considered as the key factors based upon fuzzy delphi, i.e., usability, maintainability, reliability, interoperability, portability, modifiability, and efficiency. Also, reults shows that McCall is the most appropriate model.

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Synergies and conflicts among software quality attributes and bug fixes

Cited by 3 publications

References 16 publications

Software fault prediction using particle swarm algorithm with genetic algorithm and support vector machine classifier

Software fault prediction using particle swarm algorithm with genetic algorithm and support vector machine classifier

Software fault prediction using Whale algorithm with genetics algorithm

Framework for selecting the best software quality model for a smart health application based on intelligent approach

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