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Background: Generative artificial intelligence has the potential to revolutionize health technology product development by improving coding quality, efficiency, documentation, quality assessment and review, and troubleshooting., Objective: This paper explores the application of a commercially available generative artificial intelligence tool (ChatGPT) to the development of a digital health behavior change intervention designed to support patient engagement in a commercial digital diabetes prevention program., Methods: We examined the capacity, advantages, and limitations of ChatGPT to support digital product idea conceptualization, intervention content development, and the software engineering process, including software requirement generation, software design, and code production. In total, 11 evaluators, each with at least 10 years of experience in fields of study ranging from medicine and implementation science to computer science, participated in the output review process (ChatGPT vs human-generated output). All had familiarity or prior exposure to the original personalized automatic messaging system intervention. The evaluators rated the ChatGPT-produced outputs in terms of understandability, usability, novelty, relevance, completeness, and efficiency., Results: Most metrics received positive scores. We identified that ChatGPT can (1) support developers to achieve high-quality products faster and (2) facilitate nontechnical communication and system understanding between technical and nontechnical team members around the development goal of rapid and easy-to-build computational solutions for medical technologies., Conclusions: ChatGPT can serve as a usable facilitator for researchers engaging in the software development life cycle, from product conceptualization to feature identification and user story development to code generation., Trial Registration: ClinicalTrials.gov NCT04049500; https://clinicaltrials.gov/ct2/show/NCT04049500., (©Danissa V Rodriguez, Katharine Lawrence, Javier Gonzalez, Beatrix Brandfield-Harvey, Lynn Xu, Sumaiya Tasneem, Defne L Levine, Devin Mann. Originally published in JMIR Human Factors (https://humanfactors.jmir.org), 06.03.2024.)

Presented as a research challenge in 2001, the Semantic Web (SW) is now a mature technology, used in several cross‐domain applications. One of its key benefits is a formal semantics of its RDF data format, which enables a system to validate data, infer implicit knowledge by automated reasoning, and explain it to a user; yet the analysis presented here of 71 RDF‐based SW systems (out of which 17 reasoners) reveals that the exploitation of such semantics varies a lot among all SW applications. Since the simple enumeration of systems, each one with its characteristics, might result in a clueless listing, we borrow from Software Engineering the idea of maturity model, and organize our classification around it. Our model has three orthogonal dimensions: treatment of blank nodes, degree of deductive capabilities, and explanation of results. For each dimension, we define 3–4 levels of increasing exploitation of semantics, corresponding to an increasingly sophisticated output in that dimension. Each system is then classified in each dimension, based on its documentation and published articles. The distribution of systems along each dimension is depicted in the graphical abstract. We deliberately exclude resources consumption (time and space) since it is a dimension not peculiar to SW. This article is categorized under:Fundamental Concepts of Data and Knowledge > Knowledge RepresentationFundamental Concepts of Data and Knowledge > Explainable AI [ABSTRACT FROM AUTHOR]

In the context of its initiative to promote the adoption of model‐based approaches in the development of present and upcoming missions, the European Space Agency (ESA) organised this year its annual workshop on Model‐Based Space Systems and Software Engineering (MBSE2023) at its European Space Research and Technology Centre (ESTEC) premises. The aim of this year's workshop was to investigate how the model‐based systems engineering community could contribute to bridging the gap with domain‐specific model‐based approaches used in subsystem design. The World Café Method was used to facilitate the group discussions, the outcomes of which are summarized and presented in this paper. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has witnessed an exponential increase in use in various applications. Recently, the academic community started to research and inject new AI-based approaches to provide solutions to traditional software-engineering problems. However, a comprehensive and holistic understanding of the current status needs to be included. To close the above gap, synthetic knowledge synthesis was used to induce the research landscape of the contemporary research literature on the use of AI in software engineering. The synthesis resulted in 15 research categories and 5 themes—namely, natural language processing in software engineering, use of artificial intelligence in the management of the software development life cycle, use of machine learning in fault/defect prediction and effort estimation, employment of deep learning in intelligent software engineering and code management, and mining software repositories to improve software quality. The most productive country was China (n = 2042), followed by the United States (n = 1193), India (n = 934), Germany (n = 445), and Canada (n = 381). A high percentage (n = 47.4%) of papers were funded, showing the strong interest in this research topic. The convergence of AI and software engineering can significantly reduce the required resources, improve the quality, enhance the user experience, and improve the well-being of software developers. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) applications streamline workflows, automate tasks, and require adaptive strategies for effective integration into business processes. This research explores the transformative influence of AI on various industries, such as software engineering, automation, education, accounting, mining, legal services, and media. We investigate the relationship between technological advancements and the job market to identify relevant skills for individuals and organizations for implementing and managing AI systems and human–machine interactions necessary for actual and future jobs. We focus on the essential adaptations for individuals and organizations to flourish in this era. To bridge the gap between AI-driven demands and the existing capabilities of the workforce, we employ the Rapid Review methodology to explore the integration of AI in businesses, identify crucial skill sets, analyze challenges, and propose solutions in this dynamic age. We searched the Scopus database, screening a total of 39 articles, of which we selected 20 articles for this systematic review. The inclusion criteria focused on conference papers and journal articles from 2020 or later and written in English. The selected articles offer valuable insights into the impact of AI on education, business, healthcare, robotics, manufacturing, and automation across diverse sectors, as well as providing perspectives on the evolving landscape of expertise. The findings underscore the importance of crucial skill sets, such as technical proficiency and adaptability, to successfully adopt AI. Businesses respond strategically by implementing continuous skill adaptation and ethical technology to address challenges. The paper concludes by emphasizing the imperative of balanced skill development, proactive education, and strategic integration to navigate the profound impact of AI on the workforce effectively. [ABSTRACT FROM AUTHOR]

This article explores the adoption of Generative Artificial Intelligence (AI) tools within the domain of software engineering, focusing on the influencing factors at the individual, technological, and social levels. We applied a convergent mixed-methods approach to offer a comprehensive understanding of AI adoption dynamics. We initially conducted a questionnaire survey with 100 software engineers, drawing upon the Technology Acceptance Model, the Diffusion of Innovation Theory, and the Social Cognitive Theory as guiding theoretical frameworks. Employing the Gioia methodology, we derived a theoretical model of AI adoption in software engineering: the Human-AI Collaboration and Adaptation Framework. This model was then validated using Partial Least Squares–Structural Equation Modeling based on data from 183 software engineers. Findings indicate that at this early stage of AI integration, the compatibility of AI tools within existing development workflows predominantly drives their adoption, challenging conventional technology acceptance theories. The impact of perceived usefulness, social factors, and personal innovativeness seems less pronounced than expected. The study provides crucial insights for future AI tool design and offers a framework for developing effective organizational implementation strategies. [ABSTRACT FROM AUTHOR]

The emergence of foundation models, such as large language models (LLMs) GPT-4 and text-to-image models DALL-E, has opened up numerous possibilities across various domains. People can now use natural language (i.e., prompts) to communicate with AI to perform tasks. While people can use foundation models through chatbots (e.g., ChatGPT), chat, regardless of the capabilities of the underlying models, is not a production tool for building reusable AI services. APIs like LangChain allow for LLM-based application development but require substantial programming knowledge, thus posing a barrier. To mitigate this, we systematically review, summarise, refine and extend the concept of AI chain by incorporating the best principles and practices that have been accumulated in software engineering for decades into AI chain engineering, to systematize AI chain engineering methodology. We also develop a no-code integrated development environment, Prompt Sapper , which embodies these AI chain engineering principles and patterns naturally in the process of building AI chains, thereby improving the performance and quality of AI chains. With Prompt Sapper, AI chain engineers can compose prompt-based AI services on top of foundation models through chat-based requirement analysis and visual programming. Our user study evaluated and demonstrated the efficiency and correctness of Prompt Sapper. [ABSTRACT FROM AUTHOR]

The characteristics of AI-based software have the potential to reshape traditional software development paradigms. Consequently, this study conducts a systematic literature review (SLR) within the field of AI Engineering to identify the unique challenges in software engineering for AI-based systems, which are transforming traditional software development paradigms. The scope of the SLR includes literature from academic journals and conference proceedings published between 2018 and 2023, selected through a rigorous process. The methodology involved using specific search keywords across databases such as Scopus, ScienceDirect, ACM Digital Library, and IEEE Xplore, with a stringent application of Kitchenham's inclusion and exclusion criteria to ensure a focused and relevant review. This review provides a consolidated summary of diverse research endeavors addressing challenges, issues, and methodologies relevant to AI-based software development. Highlighted topics encompass challenges in requirements engineering for AI-intensive system development, responsible software development (responsible AI), the formulation of a software engineering roadmap for responsible AI, the application of TrustOps as a risk management methodology in AI system development, the necessity of incorporating software engineering methods in AI-based systems, as well as studies exploring requirements engineering practices, AI-intensive system development, and the utilization of tools in machine learning model development. Key findings include the importance of recognizing ethical requirements in AI development, the role of risk management and ethical attributes, and the challenges of connecting requirements between software developers, data scientists, and machine learning specialists. This research provides valuable insights for practitioners and researchers involved in developing AI-based software to overcome existing challenges and apply appropriate methods in the development process. [ABSTRACT FROM AUTHOR]

Summary: Cyber‐physical systems (CPSs) are increasing in developmental complexity. Several emerging technologies, such as Model‐based engineering, DevOps, and Artificial intelligence, are expected to alleviate the associated complexity by introducing more advanced capabilities. The AIDOaRt research project investigates how the aforementioned technologies can assist in developing complex CPSs in various industrial use cases. In this paper, we discuss the experiences of industry and academia collaborating to improve the development of complex CPSs through the experiences in the research project. In particular, the paper presents the results of two working groups that examined the challenges of developing complex CPSs from an industrial and academic perspective when considering the previously mentioned technologies. We present five identified challenge areas from developing complex CPSs and discuss them from the perspective of industry and academia: data, modeling, requirements engineering, continuous software and system engineering, as well as intelligence and automation. Furthermore, we highlight practical experience in collaboration from the project via two explicit use cases and connect them to the challenge areas. Finally, we discuss some lessons learned through the collaborations, which might foster future collaborative efforts. [ABSTRACT FROM AUTHOR]

Large language models (LLMs) have had a significant impact on several domains, including software engineering. However, a comprehensive understanding of LLMs' use, impact, and potential limitations in software engineering is still emerging and remains in its early stages. This paper analyzes the role of large language models (LLMs), such as ChatGPT-3.5, in software requirements engineering, a critical area in software engineering experiencing rapid advances due to artificial intelligence (AI). By analyzing several studies, we systematically evaluate the integration of ChatGPT into software requirements engineering, focusing on its benefits, challenges, and ethical considerations. This evaluation is based on a comparative analysis that highlights ChatGPT's efficiency in eliciting requirements, accuracy in capturing user needs, potential to improve communication among stakeholders, and impact on the responsibilities of requirements engineers. The selected studies were analyzed for their insights into the effectiveness of ChatGPT, the importance of human feedback, prompt engineering techniques, technological limitations, and future research directions in using LLMs in software requirements engineering. This comprehensive analysis aims to provide a differentiated perspective on how ChatGPT can reshape software requirements engineering practices and provides strategic recommendations for leveraging ChatGPT to effectively improve the software requirements engineering process. [ABSTRACT FROM AUTHOR]

Over the past year, the emergence of state-of-the-art large language models (LLMs) in tools like ChatGPT has ushered in a rapid acceleration in artificial intelligence (AI) innovation. These powerful AI models can generate tailored and high-quality text responses to instructions and questions without the need for labor-intensive task-specific training data or complex software engineering. As the technology continues to mature, LLMs hold immense potential for transforming clinical workflows, enhancing patient outcomes, improving medical education, and optimizing medical research. In this review, we provide a practical discussion of LLMs, tailored to gastroenterologists. We highlight the technical foundations of LLMs, emphasizing their key strengths and limitations as well as how to interact with them safely and effectively. We discuss some potential LLM use cases for clinical gastroenterology practice, education, and research. Finally, we review critical barriers to implementation and ongoing work to address these issues. This review aims to equip gastroenterologists with a foundational understanding of LLMs to facilitate a more active clinician role in the development and implementation of this rapidly emerging technology. Plain language summary: Large language models in gastroenterology: a simplified overview for clinicians This text discusses the recent advancements in large language models (LLMs), like ChatGPT, which have significantly advanced artificial intelligence. These models can create specific, high-quality text responses without needing extensive training data or complex programming. They show great promise in transforming various aspects of clinical healthcare, particularly in improving patient care, medical education, and research. This article focuses on how LLMs can be applied in the field of gastroenterology. It explains the technical aspects of LLMs, their strengths and weaknesses, and how to use them effectively and safely. The text also explores how LLMs could be used in clinical practice, education, and research in gastroenterology. Finally, it discusses the challenges in implementing these models and the ongoing efforts to overcome them, aiming to provide gastroenterologists with the basic knowledge needed to engage more actively in the development and use of this emerging technology. [ABSTRACT FROM AUTHOR]

Mobile apps, used by many people worldwide, have become an essential part of life. Before using a mobile app, users judge the reliability of apps according to their reviews. Therefore, app reviews are essential components of management for companies. Unfortunately, some fake reviewers write negative reviews for competing apps. Moreover, artificial intelligence (AI)-based macro bot programs that generate app reviews have emerged and can create large numbers of reviews with malicious purposes in a short time. One notable AI technology that can generate such reviews is Generative Pre-trained Transformer-2 (GPT-2). The reviews generated by GPT-2 use human-like grammar; therefore, it is difficult to detect them with only text mining techniques, which use tools like part-of-speech (POS) tagging and sentiment scores. Thus, probability-based sampling techniques in GPT-2 must be used. In this study, we identified features to detect reviews generated by GPT-2 and determined the optimal feature combination for improving detection performance. To achieve this, based on the analysis results, we built a training dataset to find the best feature combination for detecting the generated reviews. Various machine learning models were then trained and evaluated using this dataset. As a result, the model that used both text mining and probability-based sampling techniques detected generated reviews more effectively than the model that used only text mining techniques. This model achieved a top classification accuracy of 90% and a macro F1 of 0.90. We expect the results of this study to help app developers maintain a more stable mobile app ecosystem. [ABSTRACT FROM AUTHOR]

With the recent rise in internet usage, the volume of crucial, private, and confidential data traveling online has increased. Attackers have made attempts to break into the network due to weaknesses in the security mechanisms, gaining access to sensitive data that could disrupt the operation of the systems and jeopardize the confidentiality of the data. Intrusion detection systems, a key component of cybersecurity, were used to defend against these potential threats. Numerous review papers have examined various intrusion detection system (IDS) approaches for networks, many of which focused primarily on the anomaly-based IDS while paying less attention to the signature and hybrid-based approaches. Additionally, a lot of these studies took a non-systematic approach, comparing existing techniques, without taking into account the in-depth analytical synthesis of methodologies and results of the approaches to provide a thorough grasp of state-of-the-art. In order to provide a thorough assessment of the current status of Network IDS, this work provides an in-depth insight into what is attainable in the research of Anomaly, Signature, and Hybrid-Based methods by adhering to the principles of Preferred Reporting Items for Systematic Reviews and Meta-Analyses and guidelines for Software Engineering Domain. We discovered unexplored study topics and unsolved research issues based on the study's findings. We end by highlighting potential, high impact future research areas in order to improve the IDS model. [ABSTRACT FROM AUTHOR]

Software defect prediction is a critical task in software engineering that aims to identify and mitigate potential defects in software systems. In recent years, numerous techniques and approaches have been developed to improve the accuracy and efficiency of the defect prediction model. In this research paper, we proposed a comprehensive approach that addresses class imbalance by utilizing stratified splitting, explainable AI techniques, and a hybrid machine learning algorithm. To mitigate the impact of class imbalance, we employed stratified splitting during the training and evaluation phases. This method ensures that the class distribution is maintained in both the training and testing sets, enabling the model to learn from and generalize to the minority class examples effectively. Furthermore, we leveraged explainable AI methods, Lime and Shap, to enhance interpretability in the machine learning models. To improve prediction accuracy, we propose a hybrid machine learning algorithm that combines the strength of multiple models. This hybridization allows us to exploit the strength of each model, resulting in improved overall performance. The experiment is evaluated using the NASA-MD datasets. The result revealed that handling the class imbalanced data using stratify splitting approach achieves a better overall performance than the SMOTE approach in SDD (Software Defect Detection). [ABSTRACT FROM AUTHOR]

Scaled agile development approaches are now used widely in modern software engineering, allowing businesses to improve teamwork, productivity, and product quality. The incorporation of artificial intelligence (AI) into scaled agile development methods (SADMs) has emerged as a potential strategy in response to the ongoing demand for simplified procedures and the increasing complexity of software projects. This paper explores the intersection of AI-driven assistants within the context of the scaled agile framework (SAFe) for large-scale software development, as it stands out as the most widely adopted framework. Our paper pursues three principal objectives: (1) an evaluation of the challenges and impediments encountered by organizations during the implementation of SADMs, (2) an assessment of the potential advantages stemming from the incorporation of AI in large-scale contexts, and (3) the compilation of aspects of SADMs that AI-driven assistants enhance. Through a comprehensive systematic literature review, we identified and described 18 distinct challenges that organizations confront. In the course of our research, we pinpointed seven benefits and five challenges associated with the implementation of AI in SADMs. These findings were systematically categorized based on their occurrence either within the development phase or the phases encompassing planning and control. Furthermore, we compiled a list of 15 different AI-driven assistants and tools, subjecting them to a more detailed examination, and employing them to address the challenges we uncovered during our research. One of the key takeaways from this paper is the exceptional versatility and effectiveness of AI-driven assistants, demonstrating their capability to tackle a broader spectrum of problems. In conclusion, this paper not only sheds light on the transformative potential of AI, but also provides invaluable insights for organizations aiming to enhance their agility and management capabilities. [ABSTRACT FROM AUTHOR]

IntroductionArtificial Intelligence (AI) is increasingly used as a helper to develop computing programs. While it can boost software development and improve coding proficiency, this practice offers no guarantee of security. On the contrary, recent research shows that some AI models produce software with vulnerabilities. This situation leads to the question: How serious and widespread are the security flaws in code generated using AI models?MethodsThrough a systematic literature review, this work reviews the state of the art on how AI models impact software security. It systematizes the knowledge about the risks of using AI in coding security-critical software.ResultsIt reviews what security flaws of well-known vulnerabilities (e.g., the MITRE CWE Top 25 Most Dangerous Software Weaknesses) are commonly hidden in AI-generated code. It also reviews works that discuss how vulnerabilities in AI-generated code can be exploited to compromise security and lists the attempts to improve the security of such AI-generated code.DiscussionOverall, this work provides a comprehensive and systematic overview of the impact of AI in secure coding. This topic has sparked interest and concern within the software security engineering community. It highlights the importance of setting up security measures and processes, such as code verification, and that such practices could be customized for AI-aided code production.

This document is a summary of a special issue of the journal Concurrency and Computation: Practice and Experience, which contains eight selected papers from the 14th International Conference on Parallel Processing and Applied Mathematics (PPAM 2022). The conference focused on topics related to high-performance computing (HPC) and included discussions on emerging technologies, algorithms, and software tools. The selected papers cover a range of subjects, including the convergence of iterative methods for solving nonlinear equations, testing interval arithmetic libraries, automatic performance tuning for symmetric eigenvalue problems, GPU computing, simulation of molecular magnetism, load balancing in distributed memory systems, predicting execution time of computational workloads, and resource selection for cloud-based applications. The papers provide valuable insights and advancements in the field of HPC. [Extracted from the article]

This document is a guest editorial from the journal IET Intelligent Transport Systems. It discusses the concept of electro-mobility (e-Mobility) and its potential as a dominant technology for urban mobility in the future. The editorial highlights the challenges of broad adoption of electric vehicles, such as high battery prices and long charging durations, and the need for advanced measures to ensure safe and secure operations. The document also provides a summary of five papers accepted for a special issue on e-Mobility, covering topics such as social investigation, battery power, on-board information, and scheduling control. Overall, the papers demonstrate advancements in the field of electric vehicles and provide inspiration for the integration of e-Mobility into urban transportation systems. [Extracted from the article]

The document is an editorial from the Bulletin of the Polish Academy of Sciences: Technical Sciences. It introduces a special section on IIoT (Industrial Internet of Things) and AI-based optimization. The aim of the section is to present the latest developments and applications in these areas, including methods and systems in IIoT, IoT infrastructure, and applications of AI in IoT. The topics covered align with current research trends in computer science, telecommunications, and mechanical engineering. The document also highlights the importance of integrating intelligent machines and systems in industry and the role of AI in solving complex problems. [Extracted from the article]

Artificial intelligence (AI) has witnessed an exponential increase in use in various applications. Recently, the academic community started to research and inject new AI-based approaches to provide solutions to traditional software-engineering problems. However, a comprehensive and holistic understanding of the current status needs to be included. To close the above gap, synthetic knowledge synthesis was used to induce the research landscape of the contemporary research literature on the use of AI in software engineering. The synthesis resulted in 15 research categories and 5 themes—namely, natural language processing in software engineering, use of artificial intelligence in the management of the software development life cycle, use of machine learning in fault/defect prediction and effort estimation, employment of deep learning in intelligent software engineering and code management, and mining software repositories to improve software quality. The most productive country was China (n = 2042), followed by the United States (n = 1193), India (n = 934), Germany (n = 445), and Canada (n = 381). A high percentage (n = 47.4%) of papers were funded, showing the strong interest in this research topic. The convergence of AI and software engineering can significantly reduce the required resources, improve the quality, enhance the user experience, and improve the well-being of software developers.

This document is a guest editorial for a special section in the journal "Software & Systems Modeling." The special section features revised and extended versions of selected papers from the 20th International Conference on Software Engineering and Formal Methods (SEFM'22), which was held in Berlin, Germany in September 2022. The SEFM conference series aims to advance the state of the art in formal methods and their integration into practical software engineering methods and tools. The special section includes papers on topics such as user-centric analysis, automated generation of finite automata, formal specifications of smart contracts, distributed verification of programs, and abstraction-based verification of neural networks. The authors express their gratitude to the authors and reviewers for their contributions and thank Springer for their support in publishing the special section. [Extracted from the article]