Towards a Common Testing Terminology for Software Engineering and Data Science Experts

Artificial intelligence (AI) has revolutionized various industries, and its impact on the job market is significant. While AI has the potential to automate repetitive and time-consuming tasks, it also presents new job opportunities that require expertise in data analysis, software engineering, and machine learning. The paper provides an overview of the current state of the job market and the challenges it faces, and explains how AI is transforming the job market, including its impact on job creation and destruction, skill requirements, and wage distribution. The paper analyzes the job market trends from 2019 to 2023 based on the latest research and reports, and discusses the implications of AI for the workforce and policymakers. The paper concludes with some recommendations for future research and policy interventions. It discusses the benefits of AI, such as increased productivity, efficiency, and accuracy, and highlights the challenges that AI poses, including the displacement of certain jobs and the need for retraining workers. The review paper also discusses how AI is impacting workplace safety and health, including both the potential benefits and the new hazards that AI presents. Finally, the review paper explores the policies and regulations needed to ensure that AI is used ethically and responsibly in the job market. Overall, this review paper aims to provide a comprehensive understanding of the impact of AI on the job market and its implications for workers, businesses, and society

This panel gives a non-standard view of the future of software engineering. Two of the speakers are recent Ph.D. graduates in computer science, with expertise in software engineering, who have taken academic positions; as people who will educate the next generation of software engineering practitioners and researchers, they provide a key vision of the future. The other two speakers are senior, having moved from the research community into a world in which they face the problems of engineering software on a daily basis. Collectively, along with interactions from the audience, these two often underrepresented perspectives provide a sense of the key directions in which software engineering— practice, research, and education—should and must go.

The aim of the paper is twofold. First to examine the role of the board of directors in facilitating the adoption of AI and ML in Saudi Arabian banking sector. Second, to explore the effectiveness of artificial intelligence and machine learning in protection of Saudi Arabian banking sector from cyberattacks. A qualitative research approach was applied using in-depth interviews with 17 board of directors from prominent Saudi Arabian banks. The present study highlights both the opportunities and challenges of integrating artificial intelligence and machine learning advanced technologies in this highly regulated industry. Findings reveal that advanced artificial intelligence and machine learning technologies offer substantial benefits, particularly in areas like threat detection, fraud prevention, and process automation, enabling banks to meet regulatory standards and mitigate cyber threats efficiently. However, the research also identifies significant barriers, including limited technological infrastructure, a lack of cohesive artificial intelligence strategies, and ethical concerns around data privacy and algorithmic bias. Interviewees emphasized the board of directors’ critical role in providing strategic direction, securing resources, and fostering partnerships with artificial intelligence technology providers. The study further highlights the importance of aligning artificial intelligence and machine learning initiatives with national development goals, such as Saudi Vision 2030, to ensure sustained growth and competitiveness. The findings from the present study offer valuable implications for policymakers in banking in navigating the complexities of artificial intelligence and machine learning adoption in financial services, particularly in emerging markets.

Abstract. Computational models play an increasingly vital role in scientific research by enabling the numerical simulation of complex processes. Such models are also fundamental in geosciences. For instance, they offer critical insights into the impacts of global change on the Earth system today and in the future. Beyond their value as research tools, models are also software products and should therefore adhere to certain established software engineering standards. However, scientists are rarely trained as software developers, which can lead to potential deficiencies in software quality like unreadable, inefficient, or erroneous code. The complexity of models, coupled with their integration into broader workflows, also often makes it challenging to reproduce results, evaluate processes, and build upon them. In this paper, we review the state and current practices of the development processes of the state-of-the-art land surface models used by the Global Carbon Budget. We combine the experience of modelers from the respective research groups with the expertise of software engineers from tech companies to outline key principles and tools for improving software quality in research. We explore four main areas: (1) model testing and validation, (2) scientific, technical, and user documentation, (3) version control, continuous integration, and code review, and (4) the portability and reproducibility of workflows. Our review reveals that while modeling communities are incorporating many best practices, significant room for improvement remains in areas such as automated testing, automated documentation, and reproducibility. Therefore, we here identify and promote essential software engineering practices, including numerous examples of practices from within the community that can serve as guidelines for other models and could help streamline processes across the entire community. We conclude with an open-source example implementation of these principles, demonstrating portable and reproducible data flows, a continuous integration setup, and web-based visualizations. This example may serve as a practical resource for model developers, users, and all scientists engaged in scientific programming.

The design and implementation of wireless sensor network applications often require domain experts, who may lack expertise in software engineering, to produce resource-constrained, concurrent, real-time software without the support of high-level software engineering facilities. The Insense language aims to address this mismatch by allowing the complexities of synchronisation, memory management and event-driven programming to be borne by the language implementation rather than by the programmer. The main contribution of this paper is an initial step towards verifying the correctness of WSN applications with a focus on concurrency. We model part of the synchronisation mechanism of the Insense language implementation using Promela constructs and verify its correctness using Spin . We demonstrate how a previously published version of the mechanism is shown to be incorrect by Spin , and give complete verification results for the revised mechanism.

Forensic-ready software systems enhance the security posture by designing the systems prepared for potential investigation of incidents. Yet, the principal obstacle is defining their exact requirements, i.e., what they should implement. Such a requirement needs to be on-point and verifiable. However, what exactly comprises a forensic readiness requirement is not fully understood due to distinct fields of expertise in software engineering and digital forensics. This paper describes a forensic readiness qualitative factor reference model that enables the formulation of specific requirements for forensic-ready software systems. It organises the qualitative properties of forensic readiness into a taxonomy, which can then be used to formulate a verifiable requirement targeted at a specific quality. The model is then utilised in an automated valet parking service to define requirements addressing found inadequacies regarding a potential incident investigation.

In honor of this issue’s theme of “Artificial Intelligence (AI) Engineering—Realizing the Potential of AI,” this edition of the “Practitioners’ Digest” brings you recent papers on AI and machine learning (ML) engineering, which we believe will be of interest to practitioners. These papers were published in the First International Conference on AI Engineering (CAIN 2022), the 26th International Conference on Evaluation and Assessment in Software Engineering 2022 (EASE 2022), and the 2022 IEEE/Association for Computing Machinery First International Workshop on Software Engineering for Responsible Artificial Intelligence (SE4RAI).

Artificial intelligence (AI) refers to a computer-controlled system or device's ability to interpret and judge human tasks. AI has advanced significantly in a range of disciplines in recent years, from medical to experimental. With the assistance of someone who knows what they are doing, you can learn concepts and algorithms. The success of current efforts has contributed to learning because of the availability of huge datasets, accessibility, and computing capacity. Machine learning is the process of a computer extracting information from patterns and using those patterns to make predictions. Technology for assisted reproduction, many mechanical learning technologies have been employed to improve assisted reproductive technology performance, and they are now being used more frequently in health care. Despite the numerous hurdles, the integration of AI therapy will undoubtedly provide an important direction for the future development of medicine. We cover the work done on machine learning and AI in this overview, as well as how to keep the industries ahead of the curve, and, in terms of fertility therapists, the tests conducted on these industries and experiments address the issues as well as give light on the future direction and potential. We have accounted for all of these things in this post, keeping machine learning and AI expertise and concepts in mind. A review of the above-mentioned reproductive medicine experiments, as well as a review of AI future issues and trends, are thoroughly examined. AI trials in the medical industry will continue to rise as precision medicine develops and large data becomes more widely available. Despite its limits, present AI technology is ideally suited to addressing well-defined difficulties in a variety of therapeutic settings. Women who do not have children, such as infertility patients, could benefit from such a system, which could improve pregnancy outcomes and treatment. Similarly, to a married woman who, despite her best attempts, has been unable to conceive for many years, that type of woman can assist in conceiving. We realize that AI technology capabilities are expected to grow over time and that solutions can benefit by delivering high-quality health care more precisely and effectively in practice.

Artificial Intelligence: The Future of Maxillofacial Prognosis and Diagnosis?

Artificial intelligence and Machine Learning approaches in sports: Concepts, applications, challenges, and future perspectives

Artificial intelligence (AI) is in the era of rapid evolution. Like other healthcare fields, AI has significantly impacted sleep medicine. We aim to explain the evolving role of AI in sleep medicine and provide clinicians with key information related to its benefits and limitations. AI technologies, like machine learning and deep learning, improve the detection of sleep disorders, such as obstructive sleep apnea, insomnia, and narcolepsy, through advanced data analysis from tools like polysomnography and consumer sleep devices. AI also enables targeted therapies by endotyping sleep disorders, optimizing patient care, and reducing unnecessary treatments. On the other hand, there are many challenges that need to be addressed before using AI in clinical settings. Ethical issues regarding patient privacy, biases, and transparency regarding data use are some of the key challenges. AI could transform sleep medicine by enhancing diagnostic accuracy and personalizing treatment plans. Effective collaboration between clinicians and AI experts is necessary to use AI optimally in clinical settings.

PurposeAs artificial intelligence (AI) and machine learning (ML) technologies continue to revolutionize various industries, understanding their impact on job embeddedness becomes crucial. This study examine the role of AI and ML technologies on job embeddedness, identifying key trends and proposing future research directions. It seeks to understand how these technologies influence employee attachment within organizations.Design/methodology/approachThis study uses bibliometric analysis to assess 890 articles published from 2001 to 2023 on job embeddedness and its relationship with AI and ML. The Scopus database is examined utilizing VOSviewer and Biblioshiny applications to determine themes and research deficiencies. This study visualizes the intellectual landscape of this area.FindingsThis study highlights the growing interest in AI and ML job embeddedness, highlighting the complex relationship between AI adoption and employee attachment, links and fit. It also highlights emerging themes like AI-enabled talent management, remote work implications and ethical considerations in AI-driven workplaces.Research limitations/implicationsThe extent of the Scopus database, the time period and the metadata correctness all provide restrictions on this investigation of how AI and ML affect job embeddedness. Nonetheless, the results underscore the need for empirical study on the effects of AI and ML and provide researchers with useful insights. This study also highlights how technological improvements influenced employee attitudes and actions.Originality/valueThis study presents a comprehensive overview of job embeddedness and AI/ML technologies, utilizing bibliometric techniques to evaluate research publications. It reveals key trends, identifies gaps and suggests future directions in this field.

In the last few years Artificial Intelligence (AI) algorithms and Machine Learning (ML) approaches have been successfully applied in real-world scenarios like commerce, industry and digital services, but they are not a widespread reality in Software Testing. Due to the complexity of software testing, most of the work of AI/ML applied to it is still academic. This paper briefly presents the state of the art in the field of software testing, applying ML approaches and AI algorithms. The progress analysis of the AI and ML methods used for this purpose during the last three years is based on the Scopus Elsevier, web of Science and Google Scholar databases. Algorithms used in software testing have been grouped by test types. The paper also tries to create relations between the main AI approaches and which type of tests they are applied to, in particular white-box, grey-box and black-box software testing types. We conclude that black-box testing is, by far, the preferred method of software testing, when AI is applied, and all three methods of ML (supervised, unsupervised and reinforcement) are commonly used in black-box testing being the “clustering” technique, Artificial Neural Networks and Genetic Algorithms applied to “fuzzing” and regression testing.

The purpose of this research is to investigate the nature of the relationship between artificial intelligence (AI) and machine learning (ML) technologies in service-based organizations on the one hand and organizational resilience on the other, as well as the potential impact of external market shocks on this relationship. A quantitative survey of 171 service-based groups was used to perform the research. The study's findings indicate that using AI and ML technologies in service delivery has a beneficial and substantial effect on organizational resilience. Furthermore, the research discovered that the degree to which external market shocks influence the efficacy of these technologies in improving an organization's resilience moderates the connection between the use of AI and ML technologies and organizational resilience. According to the results, service-based companies can improve their resilience by incorporating AI and ML technologies into their service delivery processes. They must, however, evaluate the possible influence of external market shocks on the efficacy of these technologies in increasing resilience. The research has ramifications for service-based organizations and adds to the body of knowledge on organizational resilience and the application of AI and ML technologies in service delivery.

Positive climate effects when AR customer support simultaneous trains AI experts for the smart industries of the future