Using Enactable Models to Enhance Use Case Descriptions, March 2003

Many tools developed for process modelling either model client business processes or the software development process itself. In both cases, benefits are to be found by using the model to highlight real process problems either of clients or developers. However, the modelling of client business processes allows a further opportunity for gain, where the intention is to build a system to provide support for the process being modelled. Although process models inform the requirements process, by providing clarity and understanding at the business modelling stage, the potential of such technology is often lost in the subsequent development phases. The premise of the work described here is to use enactable state-based approaches, previously used successfully in business process modelling and simulation, to improve artefacts of requirements engineering, by providing enactable versions of use case descriptions. This allows for the kind of validation and checking so useful to business models. In particular, such models can be used to inform design, by providing rigorous scrutiny of the (low-level) details of use case behaviour. The efficacy of this approach was gauged initially by producing enactable equivalents of use case descriptions using the existing process modelling language and tool RolEnact. However, industrial application also found that there was a mapping overhead and, hence, end users were reluctant to devote their time to producing enactable use cases without increased automation. This suggested a pressing need for tool support. That is, a use-case description tool which provided enaction capability, but without need for any further description. A prototype use case enaction tool is, therefore, introduced, along with a discussion of development issues and possible future directions

MRFS: A Multi-Resource Fair Scheduling Algorithm in Heterogeneous Cloud Computing

Task scheduling in cloud computing is considered as a significant issue that has attracted much attention over the last decade. In cloud environments, users expose considerable interest in submitting tasks on multiple Resource types. Subsequently, finding an optimal and most efficient server to host users’ tasks seems a fundamental concern. Several attempts have suggested various algorithms, employing Swarm optimization and heuristics methods to solve the scheduling issues associated with cloud in a multi-resource perspective. However, these approaches have not considered the equalization of dominant resources on each specific resource type. This substantial gap leads to unfair allocation, SLA degradation and resource contention. To deal with this problem, in this paper we propose a novel task scheduling mechanism called MRFS. MRFS employs Lagrangian multipliers to locate tasks in suitable servers with respect to the number of dominant resources and maximum resource availability. To evaluate MRFS, we conduct time-series experiments in the cloudsim driven by randomly generated workloads. The results show that MRFS maximizes per-user utility function by %15-20 in FFMRA compared to FFMRA in absence of MRFS. Furthermore, the mathematical proofs confirm that the sharingincentive, and Pareto-efficiency properties are improved under MRF

The role of Comprehension in Requirements and Implications for Use Case Descriptions

Within requirements engineering it is generally accepted that in writing specifications (or indeed any requirements phase document), one attempts to produce an artefact which will be simple to comprehend for the user. That is, whether the document is intended for customers to validate requirements, or engineers to understand what the design must deliver, comprehension is an important goal for the author. Indeed, advice on producing ‘readable’ or ‘understandable’ documents is often included in courses on requirements engineering. However, few researchers, particularly within the software engineering domain, have attempted either to define or to understand the nature of comprehension and it’s implications for guidance on the production of quality requirements. Therefore, this paper examines thoroughly the nature of textual comprehension, drawing heavily from research in discourse process, and suggests some implications for requirements (and other) software documentation. In essence, we find that the guidance on writing requirements, often prevalent within software engineering, may be based upon assumptions which are an oversimplification of the nature of comprehension. Hence, the paper examines guidelines which have been proposed, in this case for use case descriptions, and the extent to which they agree with discourse process theory; before suggesting refinements to the guidelines which attempt to utilise lessons learned from our richer understanding of the underlying discourse process theory. For example, we suggest subtly different sets of writing guidelines for the different tasks of requirements, specification and design

FFMRA: A Fully Fair Multi-Resource Allocation Algorithm in Cloud Environments

The need for effective and fair resource allocation in cloud computing has been identified in the literature and in industrial contexts for some time now. Cloud computing, as a promising technology, offers usage-based payment, ondemand computing resources. However, in the recent decade, the growing complexity of the IT world resulted in making Quality of Service (QoS) in the cloud a challenging subject and an NP-hard problem. Specifically, fair allocation of resources in the cloud is one of the most important aspects of QoS that becomes more interesting especially when many users submit their tasks and requests include multiple resources. Research in this area has been considered since 2012 by introducing Dominant Resource Fairness (DRF) algorithm as an initial attempt to solve the resource fair allocation problem in the cloud. Although DRF has some good features in terms of fairness, it has been proven inefficient in some conditions. Remarkably, DRF and other works in its extension are not proven intuitively fair after all. These implementations have been unable to utilize all the resources in the system and more specifically, they leave the system in an imbalanced situation with respect to each specific resource. To tackle those problems, in this paper we propose a novel algorithm namely FFMRA inspired by DRF which allocate resources in a fully fair way considering both dominant and non-dominant shares. The results from the experiments show that our proposed method provides approximately 100% utilization of resources and distributes them fairly among the users and meets good fairness properties

H-FfMRA: A multi resource fully fair resources allocation algorithm in heterogeneous cloud computing

The allocation of multiple types of resources fairly and efficiently has become a substantial concern in state-of-the-art computing systems. Accordingly, the rapid growth of cloud computing has highlighted the importance of resource management as a complicated and NP-hard problem. Unlike traditional frameworks, in modern data centers, incoming jobs pose demand profiles, including diverse sets of resources such as CPU, memory, and bandwidth across multiple servers. Accordingly, the fair distribution of resources, respecting such heterogeneity appears to be a challenging issue. Furthermore, the efficient use of resources as well as fairness, establish trade-off that renders a higher degree of satisfaction for both users and providers. Dominant Resource Fairness (DRF) has been introduced as an initial attempt to address fair resource allocation in multi-resource cloud computing infrastructures. Dozens of approaches have been proposed to overcome existing shortcomings associated with DRF. Although all those developments have satisfied several desirable fairness features, there are still substantial gaps. Firstly, it is not clear how to measure the fair allocation of resources among users. Secondly, no particular trade-off considers non-dominant resources in allocation decisions. Thirdly, those allocations are not intuitively fair as some users are not able to maximize their allocations. In particular, the recent approaches have not considered the aggregate resource demands concerning dominant and non-dominant resources across multiple servers. These issues lead to an uneven allocation of resources over numerous servers which is an obstacle against utility maximization for some users with dominant resources. Correspondingly, in this paper, a resource allocation algorithm called H-FFMRA is proposed to distribute resources with fairness across servers and users, considering dominant and non-dominant resources. The experiments show that H-FFMRA achieves approximately %20 improvements on fairness as well as full utilization of resources compared to DRF in multi-server settings

MLF-DRS: A Multi-level Fair Resource Allocation Algorithm in Heterogeneous Cloud Computing Systems

Cloud computing is a novel paradigm which provides on demand, scalable and pay-as-you-use computing resources in a virtualized form. With cloud computing, users are able to access large pools of resources anywhere without any limitation. In order to use the provided facilities by the cloud in an efficient way, the management of resources is an undeniable fact that should be considered in different aspects. Among all those aspects, resource allocation has received much attentions. Given the fact that the cloud is heterogeneous, the allocation of resources has to become more sophisticated. As a first promising work to deal with that problem, Dominant Resource Fairness (DRF) has been proposed which takes into account dominant shares of users. Although DRF has a sort of desirable fairness properties, it has some limitations that have already been identified in the literature. Unfortunately, DRF and its recent developments are not intuitively fair with respect to various resource demands. In this paper, we propose a Multi-level Fair Dominant Resource Scheduling (MLF-DRS) algorithm as a new allocation model inspired by Max-Min fairness and proportionality. Unlike other works that they equalize dominant shares of different resource types which leads to starvation in the maximization of allocation for some users, our algorithm guarantees that each user receives the resources they desire for based on dominant shares. As can be deducted from the mathematical proofs, MLF-DRS provides a full utilization of resources and meets some of the desirable fair allocation properties and it is applicable to be used in a naïve extension form in the presence of multiple servers as wel

Engineering Digital Motivation in Businesses: A Modelling and Analysis Framework

Digital Motivation refers to the use of software-based solutions to change, enhance, or maintain people’s attitude and behaviour towards specific tasks, policies, and regulations. Gamification, persuasive technology and entertainment computing are example strands of such a paradigm. Digital Motivation has unique properties which necessitate careful consideration of its analysis design methods. This stems from the strong human factor involvement, and if it is not implemented effectively, it can result in Digital Motivation being perceived negatively or leading to reduced motivation. The emerging literature on the topic includes approaches for creating Digital Motivation solutions. However, their primary focus is on specifying its operation, for example, the design of feedback, rewards and levels. In this paper, we propose a novel modelling language which enables capturing Digital Motivation as an integral part of the organisational and social structure of a business, captured via Goal Models. We also demonstrate how modelling of motivational techniques at this level, the goal level, enables a more powerful analysis that informs the introduction, design and management of Digital Motivation. Finally, we evaluate the language and its analysis using different perspectives and quality measures and report the results

Smartphone Usage before and during COVID-19: A Comparative Study Based on Objective Recording of Usage Data

Most studies that claimed changes in smartphone usage during COVID-19 were based on self-reported usage data, e.g., that collected through a questionnaire. These studies were also limited to reporting the overall smartphone usage, with no detailed investigation of distinct types of apps. The current study investigated smartphone usage before and during COVID-19. Our study used a dataset from a smartphone app that objectively logged users’ activities, including apps accessed and each app session start and end time. These were collected during two periods: pre-COVID-19 (161 individuals with 77 females) and during COVID-19 (251 individuals with 159 females). We report on the top 15 apps used in both periods. The Mann–Whitney U test was used for the inferential analysis. The results revealed that the time spent on smartphones has increased since COVID-19. During both periods, emerging adults were found to spend more time on smartphones compared to adults. The time spent on social media apps has also increased since COVID-19. Females were found to spend more time on social media than males. Females were also found to be more likely to launch social media apps than males. There has also been an increase in the number of people who use gaming apps since the pandemic. The use of objectively collected data is a methodological strength of our study. Additionally, we draw parallels with the usage of smartphones in contexts similar to the COVID-19 period, especially concerning the limitations on social gatherings, including working from home for extended periods. Our dataset is made available to other researchers for benchmarking and future comparisons

Transparency in persuasive technology, immersive technology, and online marketing: facilitating users’ informed decision making and practical implications

In the current age of emerging technologies and big data, transparency has become an important issue for technology users and online consumers. However, there is a lack of consensus on what constitutes transparency across domains of research, not to mention transparency guidelines for designers and marketers. In this review, we explored the question of what transparency means in current research and practices by reviewing the literature in three domains: persuasive technology, immersive technology and online marketing. Literature reviewed, including both empirical research and position articles, covered multidisciplinary areas including computer science and information technology, psychology, healthcare, human computer interaction, business and management, law and public health. In this paper, we summarized our findings through a framework of transparency and provided insights into the different aspects of transparency, categorized into ten themes (i.e., Organizational Transparency, Information Transparency, Transparency of System Design, Data Privacy and Informed Consent, Transparency of Online Advertising, Potential Risks, User Autonomy, Informed Decision Making, Information Visualization, Personalization and User-centered design) along three dimensions (i.e., Types of transparency, Impact on User and Potential Solutions). Addressing aspects of transparency will facilitate users’ autonomy and contribute to their informed decision making

Transformation of UML Activity Diagram for Enhanced Reasoning

IT industry has adopted the unified modelling language activity diagram (UML-AD) as a de facto standard. UML AD facilitates modellers to graphically represent and document business processes to show the flow of activities and behaviour of a system. However, UML AD has many drawbacks such as lack of formal semantics i.e. ontology used for the constructs based on intuition, that vaguely describes processes and no provision for verifiability. Petri Net (PN) has been around for decades and used to model the workflow systems but PNs and its variants are too complex for business process modellers with no prior experience. A logical foundation is desirable to construct a business process with a precision that facilitates in transforming UML AD into a formal mechanism supported by verifiability capabilities for enhanced reasoning. Therefore, in this paper, we will provide a framework that will provide formal definitions for UML AD core terms and constructs used for modelling, and subsequently transform them to formal representation called point graph(PG). This will provide an insight into UML AD and will improve the overall functionality required from a modelling tool. A case study is conducted at King’s College Hospital trust’ to improve their patient flows of an accident and emergency (A&E) department