This study presents a capacity framework for measuring community readiness for supporting big data science projects during cyberinfrastructure (CI) diffusion. CI projects are academic big data science projects driven ...
详细信息
In functional User Interface testing, test scenarios are written with respect to the requirements that are specified by test analysts. Usually, a test analyst focuses on base URLs and HTML components while collecting ...
详细信息
ISBN:
(纸本)9781728162515
In functional User Interface testing, test scenarios are written with respect to the requirements that are specified by test analysts. Usually, a test analyst focuses on base URLs and HTML components while collecting requirements of User Interface test scenarios. A base URL is essentially a unit segment of large scale graph data. It has mostly dynamic shape and is used to navigate pages amongst application's pages. We argue that even though dynamic URLs have additional important information about the content of the page, they are not being utilized in generating User Interface test scenarios. In this study, we address this lack of capability and focus on the development of a methodology that can support the usage of large-scale dynamic URL datasets in UI test script generation. Our proposed methodology is designed as an add-on tool that can be used on the top of the existing UI test automation tools to improve testing quality. We introduce a higher quality testing methodology to make the results more accurate, and we discuss the proposed methodology and give an overview of the implementation details followed by the evaluation results. We perform various performance evaluations to investigate how well the proposed algorithms scale under increasing data sizes. The results are promising and show the usability of the proposed methodology.
datascienceprojects often involve various machine learning (ML) methods that depend on data, code, and models. One of the key activities in these projects is the selection of a model or algorithm that is appropriate...
详细信息
This paper presents a socio-technical framework for measuring organizational capacity for cyberinfrastructure (CI) implementation, adoption, and diffusion at the team's level. CI implementation is an example of bi...
详细信息
ISBN:
(纸本)9781665439022
This paper presents a socio-technical framework for measuring organizational capacity for cyberinfrastructure (CI) implementation, adoption, and diffusion at the team's level. CI implementation is an example of bigdatascience project in data-intensive projects funded by the US National science Foundation (NSF), providing a unique case for understanding bigdatascience teams from an important field that is academic and scientific in nature. We argue that organizational capacity can be defined by the three dimensions of foundational technical expertise, daily social interactions, and enduring organizational qualities. We provide scale items for measuring these three dimensions, using a questionnaire in a self-reported and self-reflexive fashion. The overall average score and the individual composite scores of the three dimensions (and their sub-dimensions) can be used as feedback and capacity building activities as intervention strategies. Future research will statistically validate the framework using exploratory and confirmatory factor analyses.
暂无评论