Complex Systems Informatics and Modeling Quarterly
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Managing Uncertainty: Company’s Adaptive Capabilities during Covid-19
Get PDFThe concept of organizational learning receives increasing attention and recognition in recent years as a critical enabler of organizational adaptation, survival, and growth during uncertain times. Our study applies a sociotechnical lens to shed light on the organizational learning processes taking place in 40 various sizes and kinds of UK businesses during the critical, volatile, and unprecedented period – February–May 2021. The study identifies learning antecedents and key organizational context enabling and/or impeding learning processes and follow-up evolution within the studied companies. Our research confirms that in an uncertain environment, companies need to develop and apply ad-hoc learning and quick adaptation practices which are critical for survival and growth, and not standard management practices. The findings suggest, however, that even if employees have capability, not all are able to capture and transform intelligence into learning and apply it at a strategic level, reconfiguring purposefully future operational capabilities to respond to environmental changes, as they are not empowered and supported by the organizational management
Retail Sales Forecasting Using Deep Learning: Systematic Literature Review
Get PDFThis systematic literature review examines the deep learning (DL) models for retail sales forecast. The accuracy of a retail sales forecast is a prevalent force for uninterrupted business operations. Accuracy for retailers means limiting supply chain and storage costs, ensuring no product is out of stock, and facilitating smooth promotional operations. The study analyses the DL frameworks used in reviewed literature. Tested DL models are listed, as well as other machine learning and linear models used for the evaluation comparison. Additionally, the review presents the metrics used by the authors for the model evaluation. This article concludes by describing the benefits and limitations of DL models for sales forecasting
Approaches for Documentation in Continuous Software Development
Get PDFIt is common practice for practitioners in industry as well as for ICT/CS students to keep writing – and reading – about software products to a bare minimum. However, refraining from documentation may result in severe issues concerning the vaporization of knowledge regarding decisions made during the phases of design, build, and maintenance. In this article, we distinguish between knowledge required upfront to start a project or iteration, knowledge required to complete a project or iteration, and knowledge required to operate and maintain software products. With `knowledge', we refer to actionable information. We propose three approaches to keep up with modern development methods to prevent the risk of knowledge vaporization in software projects. These approaches are `Just Enough Upfront' documentation, `Executable Knowledge', and `Automated Text Analytics' to help record, substantiate, manage and retrieve design decisions in the aforementioned phases. The main characteristic of `Just Enough Upfront' documentation is that knowledge required upfront includes shaping thoughts/ideas, a codified interface description between (sub)systems, and a plan. For building the software and making maximum use of progressive insights, updating the specifications is sufficient. Knowledge required by others to use, operate and maintain the product includes a detailed design and accountability of results. `Executable Knowledge' refers to any executable artifact except the source code. Primary artifacts include Test Driven Development methods and infrastructure-as-code, including continuous integration scripts. A third approach concerns `Automated Text Analysis' using Text Mining and Deep Learning to retrieve design decisions
Metrics to Estimate Model Comprehension Quality: Insights from a Systematic Literature Review
Get PDFConceptual models are an effective and unparalleled means to communicate complicated information with a broad variety of stakeholders in a short period of time. However, in practice, conceptual models often vary in clarity, employed features, communicated content, and overall quality. This potentially impacts model comprehension to a point where models are factually useless. To counter this, guidelines to create “good” conceptual models have been suggested. However, these guidelines are often abstract, hard to operationalize in different modeling languages, partly overlap, or even contradict one another. In addition, no comparative study of proposed guidelines exists so far. This issue is exacerbated as no established metrics to measure or estimate model comprehension for a given conceptual model exist. In this article, we present the results of a literature survey investigating 109 publications in the field and discuss metrics to measure model comprehension, their quantification, and their empirical substantiation. Results show that albeit several concrete quantifiable metrics and guidelines have been proposed, concrete evaluative recommendations are largely missing. Moreover, some suggested guidelines are contradictory, and few metrics exist that allow instantiating common frameworks for model quality in a specific way
Security Requirements Specification and Tracing within Topological Functioning Model
Get PDFSpecification and traceability of security requirements is still a challenge since modeling and analysis of security aspects of systems require additional efforts at the very beginning of software development. The topological functioning model is a formal mathematical model that can be used as a reference model for functional and non-functional requirements of the system. It can also serve as a reference model for security requirements. The purpose of this study is to determine the approach to how security requirements can be specified and traced using the topological functioning model. This article demonstrates the suggested approach and explains its potential benefits and limitations
CyberSecurity Readiness: A Model for SMEs based on the Socio-Technical Perspective
Get PDFLike most companies, small and medium-sized enterprises (SMEs) have become reliant on digital technology for their day-to-day business operations. While valuable, this comes with challenges; one of which is the rise in cybercrime. In terms of their cybersecurity resilience and risk, SMEs are among the most vulnerable and least mature. This article addresses a gap in the literature that has neglected cybersecurity readiness in SMEs. The study proposes a CyberSecurity Readiness Model for SMEs (CSRM-SME) based on a Socio-Technical view of organizations. The model was applied to three SMEs to assess their cybersecurity readiness and further understand the environment and strategies adopted to prevent and manage cyber-attacks
“Simplifying” Digital Complexity? A Socio-Technical Perspective. Editorial Introduction to Issue 33 of CSIMQ
Get PDFThis thematic issue of four papers focuses on the importance of a socio-technical perspective in research and practice. A socio-technical perspective sees an organization as a combination of two components – a social and a technical one. The real pattern of behavior in the organization is determined by how well these parts fit each other. While analyzing system problems of getting things done, adequate consideration should be given to technology as well as informal and formal interactions of people with the technology as well as with other people using the technology. The papers in this issue present the sociotechnical perspective as a lens enabling researchers and practitioners to simplify the digital complexity in the socio-technical context
Selected Topics on Business Informatics: Editorial Introduction to Issue 31 of CSIMQ
Get PDFThis thematic issue introduces two structured literature review articles as well as a couple of empirical ones. The authors of the literature reviews move into the broad field of assuring the quality of IT artifacts, focusing on different dimensions of the software engineering process. With the ever-increasing scale of computerization in more and more areas of life, insufficient emphasis on quality is not only associated with significant costs of bug-fixing. After all, considerable risks arise from the possibility of exploiting the vulnerabilities of the target product. In extreme cases, poor quality can lead to loss of health and life. Not surprisingly, academics and practitioners alike have been looking at this challenge for many years, and from numerous perspectives. The quality of IT artefacts also depends on the education of professionals and good understanding of application domains. The empirical papers concern educational issues regarding Enterprise Architecture and deepen our understanding of decentralized autonomous systems
A Literature Review on the Challenges of Applying Test-Driven Development in Software Engineering
Get PDFDue to the ongoing trend of digitalization, the importance of software for today’s society is continuously increasing. Naturally, there is also a huge interest in improving its quality, which led to a highly active research community dedicated to this aim. Consequently, a plethora of propositions, tools, and methods emerged from the corresponding efforts. One of the approaches that have become highly prominent is the concept of test-driven development (TDD) that increases the quality of created software by restructuring the development process. However, such a big change to the followed procedures is usually also accompanied by major challenges that pose a risk for the achievement of the set targets. In order to find ways to overcome them, or at least to mitigate their impact, it is necessary to identify them and to subsequently raise awareness. Furthermore, since the effect of TDD on productivity and quality is already extensively researched, this work focuses only on issues besides these aspects. For this purpose, a literature review is presented that focuses on the challenges of TDD. In doing so, challenges that can be attributed to the three categories of people, software, and process are identified and potential avenues for future research are discussed
Precision Livestock Farming IT Support Model for the Poultry Industry
Get PDFThe presented work proposes a practical approach to bird weight data processing and augmentation to enable production outcome forecast model training, which contributes to higher productivity. We suggest using the parametrized model, where parameter values are found through genetic optimization and thus are closely corresponding to broiler body weight factual measurements. The proposed approach is implemented as a stand-alone software system, exposing the models through containerized web services enabling different use scenarios