Software Quality Evaluation Model Based on Weighted Mutation Rate Correction Incompletion G1 Combination Weights

Ruan, Chuan-Yang; Yang, Jian

doi:10.1155/2014/541292

Cited by 14 publications

(6 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Corresponding mathematical programming models are constructed according to different circumstances such as hesitant fuzzy sets [49,50], IFSs [51] and multiple types of linguistic circumstances [52,53]. As one of the representative subjective weight determination methods, order relation analysis method (G1) determines the weight information by virtue of DMs' experience judgement [54]. G1 not only reflects the subjective judgment of DMs, but also possesses convenience and feasibility comparing to AHP method.…”

Section: Introductionmentioning

confidence: 99%

“…G1 not only reflects the subjective judgment of DMs, but also possesses convenience and feasibility comparing to AHP method. These advantages are due to its simple acquisition process, and the fact that there is no need to construct judgement matrix [54]. To adequately reflect more realistic information from both the subjective and objective aspects, this paper investigates an integrated criteria weight utilizing the combination strengths of above determination methods, and further employs it to the WASPAS technique under INSs environment.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Solving Solar-Wind Power Station Location Problem Using an Extended Weighted Aggregated Sum Product Assessment (WASPAS) Technique with Interval Neutrosophic Sets

2017

View full text Add to dashboard Cite

As one of the promising renewable energy resources, solar-wind energy has increasingly become a regional engine in leading the economy and raising competitiveness. Selecting a solar-wind power station location can contribute to efficient utilization of resource and instruct long-term development of socio-economy. Since the selection procedure consists of several location alternatives and many influential criteria factors, the selection can be recognized as a multiple criteria decision-making (MCDM) problem. To better express multiple uncertainty information during the selection procedure, fuzzy set theory is introduced to manage that issue. Interval neutrosophic sets (INSs), which are characterized by truth-membership, indeterminacy-membership and falsity-membership functions in the interval numbers (INs) form, are feasible in modeling more uncertainty of reality. In this paper, a newly extended weighted aggregated sum product assessment (WASPAS) technique, which involves novel three procedures, is utilized to handle MCDM issues under INSs environment. Some modifications are conducted in the extended method comparing with the classical WASPAS method. The most obvious improvement of the extended method relies on that it can generate more realistic criteria weight information by an objective and subjective integrated criteria weight determination method. A case study concerning solar-wind power station location selection is implemented to demonstrate the applicability and rationality of the proposed method in practice. Its validity and feasibility are further verified by a sensitivity analysis and a comparative analysis. These analyses effectively reveal that the extended WASPAS technique can well match the reality and appropriately handle the solar-wind power station location selection problem.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Solving Solar-Wind Power Station Location Problem Using an Extended Weighted Aggregated Sum Product Assessment (WASPAS) Technique with Interval Neutrosophic Sets

2017

View full text Add to dashboard Cite

show abstract

“…However, all of these subjective weights determining methods have lots of complexity and inconvenience. Thus, the G1 method was proposed by Ruan and Yang [46] as a succinct method to get the subjective weights. Due to G1 method's characteristics, it was applied by Xu, Liu [18] to identify criteria weight vector.…”

Section: Determining the Subjective Weights Of Tmsmentioning

confidence: 99%

New Framework for Quality Function Deployment Using Linguistic Z-Numbers

Song

Wang

2020

Mathematics

View full text Add to dashboard Cite

Quality function deployment (QFD) is a useful design quality control tool in service enterprises and manufacturing enterprises. However, there are several issues in extant QFD frameworks, that is, in three aspects: description of evaluation information, weight determination of expert team members (TMs), and weight identification of customer requirements (CRs). In order to address these issues, a novel QFD framework is first proposed utilizing linguistic Z-numbers (LZNs) with integrated subjective and objective weights of TMs and CRs. The LZNs can represent uncertain information and the reliability of information in a specific way while the fuzzy numbers cannot. Moreover, the order relation analysis (G1) method and improved maximum consensus (MC) method are developed to get the subjective and objective weights of TMs, respectively. Further, the step-wise weight assessment ratio analysis (SWARA) method and statistical distance (SD) method are studied to acquire combined weights of CRs. Next, the proposed QFD framework is applied to a case of logistics service provider, which illustrates the availability and utility of the framework. Then, a sensitivity analysis is conducted to prove the reliability of the framework. Finally, two comparative analyses are performed to declare the advantages of the framework. Results indicate the proposed QFD framework is better than existing models.

show abstract

“…The quality evaluation of apps plays a vital role in assisting with the development and improvement of mHealth apps. Recently, several software product measures and metrics have been used to evaluate app quality, but many of these metrics are technical and highly dependent on the software type [12]. The Mobile App Rating Scale (MARS) is one of many tools for evaluating mHealth apps in smartphones [13], and it has been used in different studies to evaluate a variety of mHealth apps [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Weight Management Apps in Saudi Arabia: Evaluation of Features and Quality

Alshathri

Al-Humaimeedy

Al-Hudhud

et al. 2020

JMIR Mhealth Uhealth

View full text Add to dashboard Cite

Background Weight management apps may provide support and management options for individuals with overweight and obesity. Research on the quality of weight management mHealth apps among the Saudi population is insufficient despite frequent use. Objective The aims of this study were to explore user perceptions of weight management apps, explore reasons for starting and stopping app use, appraise the quality of weight management apps available in the App Store, and compare the features currently available within the app market and those that are most desirable to weight management app users. Methods A web-based survey consisted of 31 open and closed questions about sociodemographic information, general health questions, app use, app user perceptions, and discontinuation of app use. The quality of the weight management apps available on the App Store was assessed using the Mobile App Rating Scale and evidence-based strategies. We also used six sigma evaluations to ensure that the quality measured by the tools consistently meets customer expectations. Results Data from the survey were analyzed. Of the respondents, 30.17% (324/1074) had used a weight management app, 18.16% (195/1074) used the apps and stopped, and 51.68% (555/1074) had never used a weight management app. Of apps mentioned, 23 met the inclusion criteria. The overall average Mobile App Rating Scale quality of apps was acceptable; 30% (7/23) received a quality mean score of 4 or higher (out of 5), and 30% (7/23) did not meet the acceptability score of 3 or higher. Evidence-based strategy results showed that feedback was not observed in any of the apps, and motivation strategy was observed in only 1 app. The sigma results of evidence-based strategies reflect that most of the apps fail to pass the mean. Conclusions App users desired a feature that allows them to communicate with a specialist, which is a missing in the available free apps. Despite the large number and accessibility of weight management apps, the quality and features of most are variable. It can be concluded from six sigma results that passing the mean does not ensure that the quality is consistently distributed through all app quality properties and Mobile App Rating Scale and evidence-based strategies do not give developers an indication of the acceptance of their apps by mobile users. This finding stresses the importance of reevaluating the passing criterion, which is ≥50% for designing an effective app.

show abstract

Software Quality Evaluation Model Based on Weighted Mutation Rate Correction Incompletion G1 Combination Weights

Cited by 14 publications

References 9 publications

Solving Solar-Wind Power Station Location Problem Using an Extended Weighted Aggregated Sum Product Assessment (WASPAS) Technique with Interval Neutrosophic Sets

Solving Solar-Wind Power Station Location Problem Using an Extended Weighted Aggregated Sum Product Assessment (WASPAS) Technique with Interval Neutrosophic Sets

New Framework for Quality Function Deployment Using Linguistic Z-Numbers

Weight Management Apps in Saudi Arabia: Evaluation of Features and Quality

Contact Info

Product

Resources

About