Initial Framework for Software Quality Evaluation Based on ISO/IEC 25022 and ISO/IEC 25023

Nakai, Hiromi; Tsuda, Naohiko; Honda, Kiyoshi; Washizaki, Hironori; Fukazawa, Yoshiaki

doi:10.1109/qrs-c.2016.66

Cited by 16 publications

(7 citation statements)

References 8 publications

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“…Second, the more the larger training datasets and optimizing model parameters are used, the better the model prediction performance [55], and our dataset can be extended to enhance the performance of the proposed prediction model. ird, literature review related to software metrics validation [7,10,12,13] shows that usually numerous quality attributes can be used to validate a software metric. Hence, we can use other reliability subcharacteristics like fault-proneness to show the utility of the redundancy metrics as reliability indicators.…”

Section: Discussion and Reats To Validitymentioning

confidence: 99%

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An Empirical Assessment and Validation of Redundancy Metrics Using Defect Density as Reliability Indicator

Amara

Fatnassi

Rabai

2021

Scientific Programming

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Software metrics which are language-dependent are proposed as quantitative measures to assess internal quality factors for both method and class levels like cohesion and complexity. The external quality factors like reliability and maintainability are in general predicted using different metrics of internal attributes. Literature review shows a lack of software metrics which are proposed for reliability measurement and prediction. In this context, a suite of four semantic language-independent metrics was proposed by Mili et al. (2014) to assess program redundancy using Shannon entropy measure. The main objective of these metrics is to monitor program reliability. Despite their important purpose, they are manually computed and only theoretically validated. Therefore, this paper aims to assess the redundancy metrics and empirically validate them as significant reliability indicators. As software reliability is an external attribute that cannot be directly evaluated, we employ other measurable quality factors that represent direct reflections of this attribute. Among these factors, defect density is widely used to measure and predict software reliability based on software metrics. Therefore, a linear regression technique is used to show the usefulness of these metrics as significant indicators of software defect density. A quantitative model is then proposed to predict software defect density based on redundancy metrics in order to monitor software reliability.

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Section: Discussion and Reats To Validitymentioning

confidence: 99%

“…Software metrics are quantitative measures used to make evaluation, improvement, and prediction of different software quality attributes [11][12][13][14][15]. Numerous software metrics (or combination of them) are proposed to assess internal attributes like complexity, cohesion and size, faults count, and defect density.…”

Section: Introductionmentioning

confidence: 99%

An Empirical Assessment and Validation of Redundancy Metrics Using Defect Density as Reliability Indicator

Amara

Fatnassi

Rabai

2021

Scientific Programming

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“…There are many works related to the development of academic software in different contexts [34,47]; however, few works such as [16,43,54] have carried out usability assessment of authoring tools, see Table 13. In many related works, usability assessment is done considering a laboratory study [5,28,42,43,54,55] where there is higher control on the experiments, frequently, a set of specific instructions are given to users with the purpose of having more strict interactions, that is, avoiding that users make actions freely. Due to the nature of this type of study, it is not possible to identify those possible defects that occur when the user interacts freely (without being subject to a series of tasks or steps to be strictly followed) with the software [21].…”

Section: Discussionmentioning

confidence: 99%

SiGOAM: Web system for developing quality mobile learning objects

Guerrero¹,

García²,

Domínguez³

et al. 2020

Comp Applic In Engineering

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Mobile learning (m‐learning) refers to knowledge and abilities acquisition through the use of mobile technology anywhere at any time. Mobile learning objects (MLOs) are key elements for this teaching paradigm; several software tools have been proposed to support the construction of MLOs; however, they do not take into account pedagogical and technological aspects simultaneously during the construction cycle of MLOs. This paper presents the analysis, design, and development of a web system called SiGOAM, which supports specialized services to professors to generate quality MLOs. These services follow a formal MLO's construction cycle integrated into four phases: analysis, design, development, and product‐oriented assessment. As a result of this formal approach, the proposed system generates quality MLOs that are embedded in mobile devices and accessed through web browsers. We performed a usability assessment of our system SiGOAM based on a field study. The results show that professors assigned acceptable values for effectiveness, efficiency, and satisfaction.

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“…The results were that functional suitability and reliability are the most used quality characteristics by the apps evaluated [18]. Therefore, these indicators are considered essential for evaluating the quality of the software based on ISO / IEC 25022 standards [19].…”

Section: Related Workmentioning

confidence: 99%

Quality in Use of an Android-based Mobile Application for Calculation of Bone Mineral Density with the Standard ISO/IEC 25022

Sulla-Torres¹,

Gutierrez-Quintanilla²,

Pinto-Rodriguez³

et al. 2020

IJACSA

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One of the most critical bone diseases is osteoporosis, which can be evaluated through measurements of bone mineral density. Though there is a lot of commercialized portable about health in general, few are oriented towards bone health and with a lack of user-friendly interface and data management system. This paper presents a mobile application development for the calculation of the Bone Mineral density, which integrates with Google technology. The Mobile-D methodology for the development of mobile applications, due to the sequentially in the processes or stages is used. Bone mineral density (BMD) was calculated using anthropometric regression equations, and an Android-based Mobile Application with Google technology was developed. By using Firebase Authentication and Firebase Storage provided by Google technology, it allows admin to have full control over database management. In short, this mobile application allows the calculation of the BMD of the students and data storage and data uploading to cloud storage for post-processing, online data management system with user authentication. In addition, the Internacional Organization for Standardization / International Electrotechnical Commission (ISO/IEC) 25022 standard was used to evaluate the quality in use of the mobile app, resulting in 93% of quality in use, this app being able to be used by health professionals for better decision-making.

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Initial Framework for Software Quality Evaluation Based on ISO/IEC 25022 and ISO/IEC 25023

Cited by 16 publications

References 8 publications

An Empirical Assessment and Validation of Redundancy Metrics Using Defect Density as Reliability Indicator

An Empirical Assessment and Validation of Redundancy Metrics Using Defect Density as Reliability Indicator

SiGOAM: Web system for developing quality mobile learning objects

Quality in Use of an Android-based Mobile Application for Calculation of Bone Mineral Density with the Standard ISO/IEC 25022

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