Adopting Software Engineering Trends in AI

PurposeDrawing upon a circular model proposition, Kantian ethics is employed to explore how ethical considerations within AI translate into concrete actions that prioritize transparency, privacy, inclusivity and equality. Additionally, agency theory is applied to understand the relevance of fairness in the interactions between agents, principals and algorithmic systems, particularly in the creation of value through digital platforms.Design/methodology/approachA review of literature on ethical concerns within the AI ecosystem is conducted, proposing a unifying ethical principle and standards. The circular model for ethics in action is then developed, emphasising the responsible use of AI and its role in capturing and creating social value, ultimately contributing to sustainable organizational outcomes. The model also highlights key drivers that shape the ethical framing of AI, as well as the influence of the institutional context on its adoption and effectiveness.FindingsResponsible use of AI positively affects organizational performance and the digital ecosystem via the psychological mechanism of ethical identity. Ethical standards and regulations are the global requirements for the AI ecosystem that are required for achieving a sustainable digital society.Research limitations/implicationsThis study contributes to a comprehensive understanding of the responsible use of AI’s and its practical and theoretical implications for organizations in the current digital ecosystem. Lack of global understanding, awareness and implementation of ethical practice in the AI ecosystem is not yet developed and practice. Future researchers can design a cross-border ethical framework to overcome these limitations. Organizations targeting to increase responsible digital interactions can benefit from maintaining ethical principles through responsible labourers, leaders and all stakeholders involved in the ecosystem.Practical implicationsThis study offers practical guidance for businesses, policymakers and AI practitioners on the ethical use of AI. It emphasizes the need for robust data governance, a “human-first” approach focusing on privacy and accountability and alignment with ethical standards. Given AI’s global reach, international cooperation and standard-setting are essential to navigate diverse regulatory and cultural contexts. The paper also highlights the importance of ethics education for AI developers and practitioners. Investing in training that integrates technical skills with ethical awareness will help build a responsible AI workforce capable of addressing societal impacts and maintaining public trust.Social implicationsThis study underscores the urgent need for responsible AI adoption, highlighting risks such as bias, lack of transparency and privacy concerns. As AI reshapes work, decision-making and governance, its social impact grows – potentially deepening inequalities if left unchecked. The study calls for explainable, fair and inclusive AI systems guided by ethical frameworks that respect human dignity. A “human-first” approach ensures AI supports-not replaces-human agency. By fostering transparency, accountability and cultural sensitivity, organizations can build public trust, empower diverse communities and contribute to a more equitable digital future. Ethical leadership and inclusive design are essential to avoid reinforcing systemic harms.Originality/valueThis study presents an original approach to integrating ethical considerations into the development and deployment of artificial intelligence, by conceptualizing how transparency, accountability and inclusivity can be embedded throughout the AI ecosystem, fostering trust and responsible innovation. Through a comprehensive examination of ethical principles and requirements, we recommend a set of tools and strategies needed to promote ethical AI practices, mitigate risks and maximize societal benefit. Furthermore, this study serves as a roadmap for building AI systems that prioritize human collaboration and uphold fundamental values in the digital age.

This special issue focuses on issues arising from the RAISE’12 Workshop on Realizing Artificial Intelligence Synergies in Software Engineering. Our objective is to provide a forum for researchers and industrial practitioners to exchange and discuss the latest innovative synergistic AI and SE techniques/practices. Why explore this combination of SE and AI? We think there are many answers to that question. As SE is asked to answer dynamic automated, adaptive, and/or large-scale demands, other computer science disciplines come to play. AI is one of them that may bring SE to further heights. Conversely, SE can also play a role to alleviate development costs and the development effort associated with AI tools. Such mutually beneficial characteristics have appeared in the past few decades and still evolve due to new challenges. That is, this special issue explores not only the application of AI techniques to software engineering problems but also the application of software engineering techniques to AI problems. This special issue is the result of much work that is still ongoing. As to future work, the RAISE series is also on-going. At the time of this writing, the RAISE’13 workshop has just completed (that event was sponsored by the United States National Science Foundation, and we thank them for their generous support). At that meeting, we saw much continued interest in this union of AI and SE. The reader should expect much novel and exciting work from this combination of ideas, in the very near future. And regarding the work seen to date, we hope that all the aforementioned papers will provide readers with some glimpse of the kind of work discussed at RAISE’12. Also, we would like to sincerely thank the reviewers for their assistance in the reviewing process.

Computer‐aided software engineering (CASE) is both a field of study and a marketplace. As a field of study, it is the collection of research and development over many years in software development management, methods, environments, and software tools. In general terms, CASE is the industrialization of software engineering techniques and computer technology to improve and automate the practice of information systems development. It is an integration technology that incorporates software development techniques, tools, and approaches into common and consistent frameworks. CASE draws on technologies from computer‐aided design, software engineering, operating systems, database, networking, and many other computer disciplines. Thus CASE is not principally a field of new invention. Rather, it is a field of application, integration, and synthesis. As a marketplace, CASE represents a diverse set of products and services for software development and systems integration. The acronym CASE is used as an umbrella for a wide variety of claims regarding software tools and software development productivity. Definitions of CASE are given. CASE evironments, automation of analusis, computer‐supported collaboration, apportunities for quality engineering, maintenance and reengineering are discussed.

The discipline of Software Engineering is continuously adapting to new challenges while gaining more and more insights. The age of globalisation has brought about a new movement of internationalisation and localisation. While practitioners fully embrace the efforts, educators only marginally consider the implications for the teaching and learning of Software Engineering. While the relevance of the software deployment context has been widely recognised, the intrinsic values of the development context are less evident. Besides western cultural indicators being omnipresent in software applications, they are deeply rooted in Software Engineering concepts and methods. Standards and models have been established in the absence of possible deviations from other -- e.g. African -- contexts. Educators and authors of common and internationally used textbooks present Software Engineering concepts and methods as universally valid. Thus software engineering graduates all over the world continue to be ill-equipped for specific software development contexts. Moreover the necessity to localise Software Engineering education is illustrated by our vast amount of challenges, experiences and best-practices of teaching Software Engineering in a Sub-Saharan country. In this paper, we introduce a generic framework leading towards a Contextualised Software Engineering education (CSE2).

The increasing popularity of AI, particularly Large Language Models (LLMs), has significantly impacted various domains, including Software Engineering. This study explores the integration of AI tools in software engineering practices within a large organization. We focus on ANZ Bank, which employs over 5000 engineers covering all aspects of the software development life cycle. This paper details an experiment conducted using GitHub Copilot, a notable AI tool, within a controlled environment to evaluate its effectiveness in real-world engineering tasks. Additionally, this paper shares initial findings on the productivity improvements observed after GitHub Copilot was adopted on a large scale, with about 1000 engineers using it. ANZ Bank's six-week experiment with GitHub Copilot included two weeks of preparation and four weeks ofactive testing. The study evaluated participant sentiment and the tool's impact on productivity, code quality, and security. Initially, participants used GitHub Copilot for proposed use-cases, with their feedback gathered through regular surveys. In the second phase, they were divided into Control andCopilot groups, each tackling the same Python challenges, and their experiences were again surveyed. Results showed a notable boost in productivity and code quality with GitHub Copilot, though its impact on code security remained inconclusive. Participant responses were overall positive, confirming GitHub Copilot's effectiveness in large-scale software engineering environments. Early data from 1000 engineers also indicated a significant increase in productivity and job satisfaction.

In nursing care, providing high-quality, individualized care requires a deep understanding of each care recipient’s narratives (Guendouzi et al. 2015). These narratives are rarely recorded or organized for practical use, making it challenging for novice care workers to effectively incorporate them into caregiving. To address these issues, we propose a “narrative utilization ecosystem” aimed at improving the quality of nursing care services (Ihara et al. 2025). This paper outlines the proposed ecosystem and presents case studies for collecting and analyzing narrative fragments by revisiting familiar locations from the care recipient’s past. Additionally, we define the requirements for prompt engineering of a generative AI-based care advisory tool that provides advice grounded in person-centered care principles (Kitwood et al. 1992).The proposed narrative utilization ecosystem consists of three core components: collecting narratives, analyzing them, and applying them in caregiving settings. Narrative fragments are often gathered during initial assessments when a care recipient begins using nursing services. Daily conversations are another common method for collecting narratives. For individuals with dementia, opportunities to recall and share old memories are especially valuable. To facilitate this, we conducted case studies in which an individual with dementia walked with an accompanying care worker around familiar locations, such as former homes, schools or playgrounds. During these walks, we recorded their conversations and the scenery they observed. In one-hour walking sessions, the care recipient spoke for approximately 10 minutes. Out of 165 recorded utterances, 84 were brief responses like “Yeah, that’s right,” and 33 were expressions of uncertainty, such as “I don’t know,” referring to directions or places. However, we also gathered meaningful narrative fragments, including 11 statements related to location recognition (e.g., “There was company housing beyond here”) and 9 observations about vacant homes (e.g., “No one lives there anymore”). This approach encouraged storytelling by prompting the care recipient with questions linked to shared visual cues, enabling the construction of richer narratives from the collected fragments. Analysis revealed that asking questions to reaffirm the past, rather than replaying it, was particularly effective in eliciting narratives from individuals with dementia.When designing generative AI tools for handling narratives, it is essential to prioritize the emotional sensitivity of individuals with dementia and minimize burden on care workers. Through discussions with dementia care and AI experts, we identified key design requirements, including the ability to adapt to the recipient’s daily mood and fatigue levels, considering the current rapport between care workers and recipients, and avoiding both labeling care recipients with dementia and blaming failures in care on dementia.Our future work will focus on developing AI tools that meet these requirements and testing them in real-world care settings. We also plan to continue collecting and structuring additional narrative fragments to enhance the ecosystem's effectiveness.Guendouzi, J. et al. (2015). Expanding Expectations for Narrative Styles in the Context of Dementia. Topics in Language Disorders. 35. 237-257. 10.1097/TLD.0000000000000061.Ihara, M. et al. (2025). Narrative Utilization Ecosystem for Person-Centered Care. Proc. of Intelligent Human Systems Integration: Integrating People and Intelligent Systems (IHSI 2025). (to appear)Kitwood, T. et al. (1992). Towards a theory of dementia care: Personhood and well-being, Ageing and Society, 12(3), 269-287.

The developments in conversational AI raised urgent questions about the future direction of many aspects of society, including computing education. The first reactions to the fast-paced evolution of conversational agents were varied: Some announced "the end of programming," while others considered this "premature obituary of programming." Some adopted a defensive approach to detecting the use of conversational AI and avoiding an increase in plagiarism, while others questioned, "So what if ChatGPT wrote it?" Nevertheless, questions arise about whether computing education in its current form will still be relevant and fit for purpose in the era of conversational AI. Recognizing these diverse reactions to the advent of conversational AI, this paper aims to contribute to the ongoing discourse by exploring the current state through three perspectives in a dedicated literature review: adoption of conversational AI in (1) software engineering education specifically and (2) computing education in general, and (3) a comparison with software engineering practice. Our results show a gap between software engineering practice and higher education in the pace of adoption and the areas of use and generally identify preliminary research on student experience, teaching, and learning tools for software engineering.

Since the term “software engineering” was coined some 20+ years ago, [4] a number of definitions for both the practice and the practitioner, a “software engineer,” have been proposed. The definition from a recent report on undergraduate software engineering curricula [3] states:Software engineering entails the understanding and application of engineering principles, design skills, good management practice, computer science and mathematical formalism. …The Software Engineer must be able to estimate the cost and duration of the software development process, and to determine correctness and reliability and express them with a degree of precision meaningful to other engineers and informed clients. …In recent years, several advanced degree programs in “software engineering” have been instituted in the US and abroad. One example of this can be found at the SEI itself. Two of the goals of the SEI Education Program are to: (1) develop software engineering curriculum modules that identify, organize, and document the body of knowledge that could be taught in software engineering degree programs, and (2) design, develop, support, and maintain model curricula for undergraduate and graduate software engineering programs.The question remains, however, how much of this can be taught in a classroom setting or even in a design lab, and how much must be learned on the job or via some sort of apprentice program? What does Ada have to do with software engineering ? Can Ada exist without it? Can software engineering be done without Ada?There are those who believe the term “software engineering” is just plain wrong: [1]… I deeply resent the misuse of the word “engineering” in the compound term “software engineering.” Such misuse constitutes, in my opinion, a cheap, deceptive attempt to take advantage of the professional reputation and image developed by real engineers over a century or so … it constitutes theft of professional reputation.In 1986, I coordinated a similar panel for the ACM Computer Science Conference (CSC) [2]. The introduction from that session still holds true for this one, and is paraphrased and tuned below:Most universities prepare students for continued growth and development during a lifetime of employment, whether in an industrial or government setting. Since traditionally, such an education has implied better job opportunities, the university student believes that by studying computer science and/or software engineering, she2 will be assured of a good job at a reasonable salary performing interesting and challenging tasks.On the other hand, some universities prepare students for graduate education and research, placing a heavy emphasis on theory. From this perspective, the university student believes that upon completion of a computer science or software engineering degree, she will be able to choose where to pursue her research, at the university, industry, or government site of her choosing.Three years have passed; and I have found other group of articulate people to join us and present new and exciting viewpoints on the topic of software engineering, university education, and, of course Ada. Richard Fairley, who participated in that CSC panel, continues to represent the academic point of view; Marcia Finfer represents the industrial point of view; Garth Glynn also provides the industrial point of view with the interesting addition of evaluating graduate-level software engineering degree curricula; David Markus brings the perspective of the human resource issues; Lyn Plinta provides the view of the recent graduate dealing with veterans of the hacking era; and David Umphress represents the DoD interests.These panelists, bringing together a wealth of experience as educators, practitioners, and policy makers, provide insights from both the demand-side of software engineering and the supply-side, as they take a multi-faceted, multi-national look at industrial, academic, and government expectations of software engineering as it is taught and practiced today.

A recent trend in software engineering is the shift from a focus on laboratory-oriented software engineering to a more industry-oriented view of software engineering processes. This complements preceding ideas about software engineering in terms of organization and process-orientation. From the domain coverage point of view, many of the existing software engineering approaches have mainly concentrated on the technical aspects of software development. Important areas of software engineering, such as the technical and organizational infrastructures, have been left untouched. As software systems increase in scales, issues of complexity and professional practices become involved. Software development as an academic or laboratory activity, has to engage with software development as a key industrialized process. This expanded domain of software engineering exposes the limitations of existing methodologies that often address only individual subdomains. There is, therefore, a demand for an overarching approach that provide a basis for theoretical and practical infrastructures capable of accommodating the whole range of modern software engineering practices and requirements. One approach is provided by Process-Based Software Engineering (PBSE); part of the more general trend towards a focus on process. This paper provides a review of process techniques for software engineering and a high-level perspective on PBSE. Typical approaches and techniques for the establishment, assessment, improvement and benchmarking of software engineering process systems are introduced in this paper, and many are developed further in other contributions to this volume.

The development of AI and machine learning applications at an industry mature level while maintaining quality and productivity goals is one of today's major challenges. Research in the field of intelligent agents has achieved many successes in recent years, especially due to various reinforcement learning techniques, and promises a high benefit in times of automation and autonomous systems. Bringing them into production, however, requires optimization against many other criteria than just accuracy. This leads to the emerging field of machine teaching. We already know many of the objectives used there from software engineering research, which has led to many well-established principles in recent decades. One of them is the component-based development whose idea finds an interesting counterpart in hierarchical reinforcement learning. We show that both areas can benefit from each other and introduce our approach of concept based software engineering, which is focused on supporting productivity and quality goals during the development of such systems.

This article provides an introduction to the usage of software engineering approaches and tools for the design of user experience (UE). This approach was developed for three main reasons: (a) to provide UE design teams a rigorous, methodical, encompassing method that can be applied throughout the entire product development cycle; (b) to provide designers a way to work with abstract concepts to establish the conceptual design of the system before progressing to the realization of the design to the appropriate platform; and (c) to communicate the intended design to the implementation team in a manner that is concise, complete, and familiar. The approach is the application of Unified Modeling Language concepts to describe users and the UE design rather than the technical architecture. It is believed that designing the UE design separately from the technical architecture will help ensure teams deliver a usable and delightful UE. The methods in this article have been applied to a handful of real-world projects in the last year and a half. Thus far, the application of software engineering approaches and tools have been found to be very powerful aids that have enabled the creation of highly useful Web experiences as well as software applications. Our intent is to address the full gamut of issues involved in creating a total user experience (UE)-from discovery of users needs through high-level and low-level design, ensuring fidelity during development, and deployment, with a full assessment of how well the design meets users' needs. We believe that through a concerted use of such a rigorous approach we can greatly improve not only the UE of products we create, but also the experience of designing and developing them. We are not the only team taking this direction in UE design. It has become clear to many in our field that a more rigorous, thorough, and accurate approach to UE design is not only possible-it is necessary, for many of the same reasons that software engineers long ago embraced such practices. As we are evolving our approaches in other phases, this article focuses on the discovery and design phase activities.

This paper deals with design of the agile method RISVICO. The method is for more effective collaboration between software engineer and database analyst by using an effective visualization tool in 3D environment, database analyst and software engineer belong to the software engineering. The method is for more effective collaboration between software and database development by using an effective visualization tool in 3D environment, database analyst and software engineer belongs to the software engineering. The visualization tool will provide important information for a database analyst and for a software engineer. Main idea is make use of nature of data which the program uses, for development and maintenance in software life cycle. Data which software will use determine procedure in development and maintenance, in this way we can provide better quality of software product.

With the rapid development of Generative AI, the field of language education is undergoing profound technology-driven changes. Research has confirmed that AI tools have significantly improved language learning efficiency and equity through personalized learning path design, immediate feedback mechanisms, and multimodal resource supply, but the existing results are fragmented and lack cross-scenario theoretical integration and long-term impact assessment. To this end, using bibliometric methods, combined with CiteSpace and vosviewer tools, this paper systematically analyzes 209 high-quality documents on AI enabled language education in the web of science core database in the past eight years (2017-2025), and draws a knowledge map to reveal research trends and knowledge networks. The results show that the research focuses on three core directions - the application of generative AI (such as ChatGPT) in language skills training and emotional interaction, the optimization of AI-driven automated assessment systems, and the exploration of technology ethics and sociocultural adaptability. It provides a global perspective and empirical basis for the theoretical integration and practical optimization of AI enabled language education.

Component reuse has become a trend in software engineering. However, third-party components have the potential to introduce vulnerabilities into software applications and become the weakest link in the security chain. In this paper, we discuss the limitations of traditional security practices and controls against vulnerable components. As a solution, we present a software design and development approach, combined with a collaborative, cloud-based vulnerability and threat management system. This combination aims at enabling applications to gain “artificial immunity” to third-party components by dynamically identifying and controlling related security risks. It also strives to promote the automatic discovery of, and near real-time information dissemination about emerging threats and zero-day vulnerabilities. At the heart of our solution, we use application-level API sandboxing, as well as adaptive signature-based and anomaly-based API intrusion detection and prevention. The need-to-know, cost-effectiveness, and user acceptance through separation of concerns have been our guiding security engineering principles.

As software applications get increasingly connected and complex, cybersecurity becomes more and more important to consider during development and evaluation. Software engineers need to be aware of various security threats and the countermeasures that can be taken to mitigate them. Currently, there is a lack of guidance for software engineers aiming to develop secure web applications. We conducted a design science research study, resulting in a set of guidelines to aid software engineers in developing secure web applications. The set of guidelines was constructed based on interview data with 10 industry practitioners. These guidelines were then evaluated using a survey with 28 respondents. Additionally, we conducted experiments in which we provided a large language model with our guidelines and vulnerability reports as input. The large language model should extend the given vulnerability reports by recommending which of our guidelines can help prevent the given vulnerability in the future. The extended reports were evaluated by two external researchers experienced in cyber security and one author. Our results indicate that developers consider using these proposed guidelines for the development and assessment of secure web applications in different stages of the software development lifecycle. Our results also show that it is possible to automatically enhance vulnerability reports to support developers meaningfully and that the guidelines recommended by the large language model are useful to prevent the respective vulnerabilities in the future.