Iso Iec 25022

[Autumn Pitz]

Qualityin use has become a point of interest in recent years for the software development industry. As a result, the need has arisen to verify that a software product reaches an acceptable level of quality. There are currently ISO standards related to software quality for the different stages of the life cycle. In order to evaluate the quality in use, ISO 25022 is used, in which the metrics to be applied are declared. This research work seeks, through a case study, to propose a quality assessment model in use based on ISO 25022 following the process of the engineering cycle. With the proposed model, a human talent management information system was evaluated, obtaining results of the characteristics in the range: Satisfactory (Effectiveness 88.75%), while the others are in the range of Fair (Efficiency 59.21% and Satisfaction 66.74%). These results allow us to identify the level of quality in use by characteristics and in general to help in the decision making of those responsible for software development. With this, it is concluded that it is possible to evaluate the quality in use of a Human Talent Management Information System using the model designed in this research.

Over the last decade, innovation has centred on making archaeological data more interoperable, increasing the discoverability of data through integrated cross-search and facilitating knowledge creation by combining data in new ways. An emerging research challenge for the next decade is optimising archaeological data for reuse and defining what constitutes good practice around reuse. Critical to this research is understanding the current state-of-the-art regarding both existing best practices and barriers to using and reusing archaeological data. This research aimed to understand how to optimise archives and interfaces to maximise the discovery, use and reuse of archaeological data and explore how archaeological archives can better respond to user needs.

The study was bound by (i) the reuse of digital archaeological archives; (ii) orientation to content usability and reusability; (iii) maintaining a user-orientated approach; (iv) collecting data from professionals in archaeology and heritage. The research group members adopted the quality-in-use conceptual approach for this study. Quality in use is 'the degree to which a product or system can be used by specific users to meet their needs to achieve specific goals with effectiveness, efficiency, satisfaction, and freedom from risk in specific contexts of use'. The research methodology is based on the SQuaRE (System and Software Quality Requirements and Evaluation) model, represented in the ISO/IEC 25000 standards series. In addition, the quality-in-use metric for investigation of reuse and barriers to reuse of archaeological data were adopted from the standardised measurement functions and methods of ISO/IEC 25022:2016. The result was a methodological model composed of 5 characteristics (Effectiveness, Efficiency, Satisfaction, Context coverage and Usability) with 14 measures (Task completeness, Objectives achievement, Task time, Cost-effectiveness, Overall satisfaction, Satisfaction with features, User trust in the system, data and paradata, User pleasure, Physical comfort, Context completeness, Flexible context of use and User guidance completeness). The methodology was tested with specific Contexts of use (use cases), orientated to a distinct user with the specific professional goal of data reuse. Three use cases relating to 3D Pottery, radiocarbon, and GIS data were created. The pilot study has proven that the methodology works and could be applied in future research. This article discusses the application of the quality-in-use approach for evaluating the quality of digital archaeological archives, as well as presenting the methodology and the results of the pilot study.

Figure 3: Effectiveness evaluates the respondents' ability to complete the tasks in a given scenario. The measurement is a ratio between the tasks supplied and the number of tasks respondents could complete. This graph displays the effectiveness of scenarios with five tasks

Figure 4: Effectiveness evaluates the respondents' ability to complete the tasks in a given scenario. The measurement is a ratio between the tasks supplied and the number of tasks respondents could complete. This graph displays the effectiveness of scenarios with four tasks

Figure 6: Efficiency determines the resources required to complete a given scenario. The second measure of efficiency is the cost-effectiveness of the user, where the cost of completing each task is considered in relation to the time taken to complete individual tasks

Figure 7: 'Was the platform easy to use?' assessed the respondents' satisfaction when using their chosen platform. Satisfaction was used to determine the degree to which users' expectations were met when using their chosen platform

Figure 8:: 'Were you satisfied with the features offered?' assessed the respondents' satisfaction with the features offered on their chosen platform. Satisfaction was used to determine the degree to which users' expectations were met when using their chosen platform

Figure 9: 'Did you enjoy using the platform?' assessed the respondents' enjoyment when using their chosen platform. Satisfaction was used to determine the degree to which users' expectations were met when using their chosen platform

Figure 10: 'Did the platform have the data you were looking for?' assessed the respondents' ability to find relevant data when using their chosen platform. Satisfaction was used to determine the degree to which the user's expectations were met when using their chosen platform

Figure 11: 'Did the platform help you better engage with the data?' assessed whether the respondents' chosen platform improved their engagement with the data offered. Satisfaction was used to determine the degree to which users' expectations were met when using their chosen platform

Figure 12: 'What was your overall satisfaction with using your chosen platform?' assessed the respondents' overall satisfaction when using their chosen platform. Satisfaction was used to determine the degree to which users' expectations were met when using their chosen platform

Figure 13: 'Was there enough metadata for you to understand the dataset?' assessed the extent to which the respondents' trusted their chosen platform. Trust was a sub-characteristic of satisfaction that determined the degree to which the user's expectations were met when using their chosen platform

Figure 14: 'Was the metadata sufficient for your research needs?' assessed the extent to which the respondents' trusted the data used for the survey. Trust was a sub-characteristic of satisfaction that determined the degree to which the user's expectations were met when using their chosen platform

Figure 15: 'Are you confident that you could use the metadata in your own research?' assessed the extent to which the respondents' trusted the metadata or paradata found on their chosen platform. Trust was a sub-characteristic of satisfaction that determined the degree to which the user's expectations were met when using their chosen platform

Figure 16: Flexibility measured the extent to which the data could be used in additional contexts, in this instance, whether the data could be exported for use with different software. The question provided a text box, and the responses are summarised here. Respondents who failed to answer the question were marked as 'undefined'

Figure 17: Flexibility measured the extent to which the data could be used in additional contexts, in this instance, by considering whether the respondent was satisfied with the file formats available for download. Respondents who failed to answer the question were marked as 'undefined'

Figure 18: Usability determined whether sufficient documentation was provided to use their chosen platform. The question posed whether licensing information was available. Respondents who failed to answer the question were marked as 'undefined'

Figure 19: Usability determined whether sufficient documentation was provided to use their chosen platform. The question was whether user documentation was made available to assist the respondent in navigating the platform. Respondents who failed to answer the question were marked as 'undefined'

Internet Archaeology is an open access journal based in the Department of Archaeology, University of York. Except where otherwise noted, content from this work may be used under the terms of the Creative Commons Attribution 3.0 (CC BY) Unported licence, which permits unrestricted use, distribution, and reproduction in any medium, provided that attribution to the author(s), the title of the work, the Internet Archaeology journal and the relevant URL/DOI are given.

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Background: Previous studies have demonstrated the effectiveness of information and communication technologies to support healthy lifestyle interventions. In particular, personal health record systems (PHR-Ss) empower self-care, essential to support lifestyle changes. Approaches such as the user-centered design (UCD), which is already a standard within the software industry (ISO 9241-210:2010), provide specifications and guidelines to guarantee user acceptance and quality of eHealth systems. However, no single PHR-S for metabolic syndrome (MS) developed following the recommendations of the ISO 9241-210:2010 specification has been found in the literature.

Methods: The proposed PHR-S was developed using a formal software development process which, in addition to the traditional activities of any software process, included the principles and recommendations of the ISO 9241-210 standard. To gather user information, a survey sample of 1,187 individuals, eight interviews, and a focus group with seven people were performed. Throughout five iterations, three prototypes were built. Potential users of each system evaluated each prototype. The quality attributes of efficiency, effectiveness, and user satisfaction were assessed using metrics defined in the ISO/IEC 25022 standard.

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