5Ws of green and sustainable software

Calero, Coral; Mancebo, Javier; García, Félix; Moraga, María Ángeles; Berná, José Alberto García; Fernández-Alemán, José Luis; Toval, Ambrosio

doi:10.26599/tst.2019.9010006

Cited by 28 publications

(12 citation statements)

References 19 publications

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“…The 5Ws þ 1H model uncovers essential information that needs to be considered for any story to be told, namely, motivation, time, actor, location, content and causality (Jia et al, 2016). A helpful starting point for these questions is shown below as follows (Tattersall, 2015a(Tattersall, , 2015bJia et al, 2016;Calero et al, 2020):…”

Section: Introductionmentioning

confidence: 99%

“…Various studies have performed bibliometric analysis using a 5Ws approach for informationgathering or problem-solving. For example, Calero et al (2020) conducted a bibliometric analysis of green and sustainable software using a 5Ws or 5Ws þ 1H approach (Galindo et al, 2019;Bibi et al, 2020) and Rahimi and Rosman (2020) used the 5Ws approach to investigate enterprise content management.…”

Section: Introductionmentioning

confidence: 99%

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The Kirkpatrick model for training evaluation: bibliometric analysis after 60 years (1959–2020)

Alsalamah

Callinan

2021

ICT

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Purpose A number of studies on Kirkpatrick’s four-level training evaluation model have been published, since its inception in 1959, either investigating it or applying it to evaluate the training process. The purpose of this bibliometric analysis is to reconsider the model, its utility and its effectiveness in meeting the need to evaluate training activities and to explain why the model is still worth using even though other later models are available. Design/methodology/approach This study adopts a “5Ws+1H” model (why, when, who, where, what and how); however, “when” and “how” are merged in the methodology. A total of 416 articles related to Kirkpatrick’s model published between 1959 and July 2020 were retrieved using Scopus. Findings The Kirkpatrick model continues to be useful, appropriate and applicable in a variety of contexts. It is adaptable to many training environments and achieves high performance in evaluating training. The overview of publications on the Kirkpatrick model shows that research using the model is an active and growing area. The model is used primarily in the evaluation of medical training, followed by computer science, business and social sciences. Originality/value This paper presents a comprehensive bibliometric analysis to reconsider the model, its utility, its effectiveness in meeting the need to evaluate training activities, its importance in the field measured by the growth in studies on the model and its applications in various settings and contexts.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Kirkpatrick model for training evaluation: bibliometric analysis after 60 years (1959–2020)

Alsalamah

Callinan

2021

ICT

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“…Mobile Edge Computing (MEC) as a new computing paradigm brings remote cloud resource to the edge servers nearby mobile users, enabling mobile users to offload partial or all computation tasks of mobile applications to edge servers for collaborative execution, and thereby greatly alleviating the conflict between resource supply and demand, effectively reducing the application completion time and the mobile devices' energy consumption [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning for Security-Aware Workflow Application Scheduling in Mobile Edge Computing

Huang

Xiang

et al. 2021

Security and Communication Networks

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Mobile edge computing as a novel computing paradigm brings remote cloud resource to the edge servers nearby mobile users. Within one-hop communication range of mobile users, a number of edge servers equipped with enormous computation and storage resources are deployed. Mobile users can offload their partial or all computation tasks of a workflow application to the edge servers, thereby significantly reducing the completion time of the workflow application. However, due to the open nature of mobile edge computing environment, these tasks, offloaded to the edge servers, are susceptible to be intentionally overheard or tampered by malicious attackers. In addition, the edge computing environment is dynamical and time-variant, which results in the fact that the existing quasistatic workflow application scheduling scheme cannot be applied to the workflow scheduling problem in dynamical mobile edge computing with malicious attacks. To address these two problems, this paper formulates the workflow scheduling problem with risk probability constraint in the dynamic edge computing environment with malicious attacks to be a Markov Decision Process (MDP). To solve this problem, this paper designs a reinforcement learning-based security-aware workflow scheduling (SAWS) scheme. To demonstrate the effectiveness of our proposed SAWS scheme, this paper compares SAWS with MSAWS, AWM, Greedy, and HEFT baseline algorithms in terms of different performance parameters including risk probability, security service, and risk coefficient. The extensive experiments results show that, compared with the four baseline algorithms in workflows of different scales, the SAWS strategy can achieve better execution efficiency while satisfying the risk probability constraints.

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“…focusing on the environmental dimension, but expanding also to the other ones. A recent survey of sustainable software research based on the 5Ws formula (why, when, who, where, and what) [3], shows that this is a very active and collaborative area of research, with a good level of maturity. Despite the efforts from various research communities, however, there is still a lot of ground to be covered with respect to establishing and exploiting the relation between software architecture and sustainability as a software quality [30], and especially for software services.…”

Section: Introductionmentioning

confidence: 99%

Software Sustainability in the Age of Everything as a Service

Andrikopoulos

Lago

2021

Lecture Notes in Computer Science

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5Ws of green and sustainable software

Cited by 28 publications

References 19 publications

The Kirkpatrick model for training evaluation: bibliometric analysis after 60 years (1959–2020)

The Kirkpatrick model for training evaluation: bibliometric analysis after 60 years (1959–2020)

Reinforcement Learning for Security-Aware Workflow Application Scheduling in Mobile Edge Computing

Software Sustainability in the Age of Everything as a Service

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