Enhancing Software Engineering With AI: Innovations, Challenges, and Future Directions

Software engineering (SE) needs to make substantial breakthroughs in many different areas to allow order of magnitude improvements in software development times, software quality, and system cost. Artificial intelligence (AI) is uniquely positioned to help the SE research community in many of these areas, and we examine issues in AI for SE research. Given the fuzzy definition of AI, we provide a list of AI techniques to identify how much AI there is in specific AI for SE research. We recommend using the divide and conquer approach for SE automation and provide criteria for dividing the SE problems. We provide a vision of the future CASE environment, a knowledge and database management system at the center in a client-server architecture, and argue that it constitutes an ideal test-bed for research in AI for SE. We recommend an AI for SE research approach that includes dividing the problem up, using protocol analysis, implementing on a realistic CASE environment, and evaluating in industrial settings. We give criteria to evaluate applications of AI to SE including generality, scalability, and combinability. We conclude that AI will help SE to make slow and steady progress, but that it constitutes no silver bullet.

Estimates predict a global deficit of 4 million software engineers by 2025, further complicated by the software engineering (SE) industry's escalating use of artificial intelligence (AI). To tackle this issue, our research suggests that computer science (CS) curricula in middle and high schools need to be updated to incorporate SE industry segments that significantly employ AI. This strategic curriculum alignment is significant for preparing a workforce equipped to meet future industry demands. Our initial analysis involved reviewing nine international AI education guidelines to evaluate current methods for integrating AI into SE education. The findings indicated a pronounced lack of specific guidance connecting AI applications in the SE industry with educational content. To address this, we performed a systematic literature review of 12 research papers focusing on AI's role across the SE industry, followed by multiple rounds of inductive content analysis. An industry segment was deemed "essential" if it was referenced in 75% or more of the papers' findings. Through this method, we identified 10 essential SE industry segments for inclusion in CS education: software development, software maintenance, process improvement, software economics, knowledge management, project management, software testing, software security, quality assurance, and deployment and operations (DevOps). These findings led to the creation of the AI-USE (Artificial Intelligence Usage in Software Engineering) framework, which maps these 10 key segments to the predominant uses of SE in the industry as identified in the literature. Further inductive content analysis helped us develop subsegments for these essential areas. Ongoing framework development involves refining these subsegments and gathering feedback from industry and academic professionals. We anticipate that the fully developed AI-USE framework will significantly enhance SE education, equipping the next generation of software engineers with the AI proficiency required to address the industry’s evolving demands.

A number of key issues relating to the application of artificial intelligence (AI) to software engineering (SE) and the reverse are discussed. Issues relating to how AI can help SE are: whether automatic programming is synonymous with AI for SE and whether it represents a major paradigm for SE in the next decade; whether expert systems technology is sufficiently successful and mature enough to provide significant solutions to certain aspects of the SE process; whether AI development and maintenance environments are suitable for direct application to the SE process; how AI methodology can be applied to the software design process; and how the AI rapid prototyping paradigm is useful as a SE paradigm. Issues relating to the common belief that SE is not useful for AI are: SE is synonymous with the waterfall model, and the waterfall model is linear and hence not suitable for AI; expert systems can not be specified and thus SE techniques do not apply; and AI software is easy to maintain and thus SE is not required. An attempt is made to relate AI and Se at the level of objectives and issues. >

Automation, optimization, and enhanced decision-making are just a few ways artificial intelligence (AI) changes the game across several sectors. Its applications extend across diverse fields, including healthcare, transportation, finance, education, and software engineering. This study explores the integration of AI in software engineering, highlighting its transformative role in streamlining development workflows, improving software quality, and fostering collaboration between technical and non-technical stakeholders. The rise of no-code and low-code platforms has democratized access to AI, allowing users with limited technical expertise to implement AI-powered solutions like NLP and predictive analytics. Key benefits of AI in software development include automation of repetitive tasks, early bug detection, efficient project management, and personalized user experiences. The study also discusses the current trends in AI integration, including ML, NLP, robotics, and explainable AI, while addressing the challenges. Furthermore, AI tools for software development demonstrate their impact on education and skill development. Finally, the paper explores prospects in AI-driven software development. By analyzing the current and future trends, this study provides insights into how AI can shape the next generation of software development.

The quest for sustainability has extended its reach into the realm of software engineering, prompting an exploration of tools, techniques, and emerging trends to mitigate the environmental impact of software development and operation. This review provides a critical review of current practices and future directions in sustainable software engineering. In recent years, the software industry has recognized the need to address the environmental footprint of software systems, considering factors such as energy consumption, resource utilization, and carbon emissions. Consequently, a plethora of tools and techniques have emerged to support sustainable software development processes. These range from energy-efficient programming languages and frameworks to eco-friendly software architectures and design patterns. Moreover, methodologies such as Green Software Engineering (GSE) and Sustainable Software Development (SSD) have gained traction, emphasizing the integration of sustainability considerations throughout the software development lifecycle. By adopting practices like green requirements engineering, energy-aware testing, and eco-design principles, organizations can optimize their software systems for reduced environmental impact without compromising functionality or performance. Furthermore, trends in sustainable software engineering extend beyond traditional development practices. The rise of cloud computing, edge computing, and Internet of Things (IoT) technologies presents both challenges and opportunities for sustainability. Techniques such as serverless computing and containerization offer potential benefits in terms of resource efficiency and scalability, while also introducing new considerations regarding data center energy consumption and electronic waste management. Looking ahead, the future of sustainable software engineering is marked by innovation and collaboration. Emerging technologies such as artificial intelligence (AI) and blockchain hold promise for optimizing resource allocation, enhancing energy efficiency, and fostering transparency in sustainability efforts. Additionally, interdisciplinary collaboration between software engineers, environmental scientists, and policymakers will be essential in shaping a more sustainable digital ecosystem. The journey towards sustainable software engineering involves a multifaceted approach encompassing tools, techniques, and ongoing adaptation to evolving trends. By critically evaluating current practices and embracing future directions, the software industry can contribute to a more environmentally responsible and resilient future.

The rapid advancement of artificial intelligence (AI) has raised significant ethical and social concerns, necessitating a systematic analysis of research trends in this domain. This study employs a bibliometric analysis using data from Scopus and visualization through VOSviewer to map the scholarly landscape of AI ethics. The analysis identifies key research themes, including algorithmic bias, data privacy, transparency, accountability, and trust, while highlighting emerging topics such as ChatGPT, adversarial machine learning, AI in education and healthcare, and sustainability. The co-authorship and country collaboration networks reveal a highly interdisciplinary and globally connected research community, with strong contributions from the United States, Germany, India, and China, but limited representation from the Global South. Findings indicate that AI ethics research is evolving beyond theoretical discussions to address real-world applications and governance challenges. The study underscores the need for more inclusive AI policies, interdisciplinary collaborations, and ethical AI governance frameworks to ensure responsible AI development. Future research should focus on bridging the gap between AI engineering and ethical oversight, regulating AI-driven misinformation, and expanding the global diversity of AI ethics discourse.

Artificial intelligence and recent advances in deep learning architectures, including transformer networks and large language models, change the way people think and act to solve problems. Software engineering, as an increasingly complex process to design, develop, test, deploy, and maintain large-scale software systems for solving real-world challenges, is profoundly affected by many revolutionary artificial intelligence tools in general and machine learning in particular. In this roadmap for artificial intelligence in software engineering, we highlight the recent deep impact of artificial intelligence on software engineering by discussing successful stories of applications of artificial intelligence to classic and new software development challenges. We identify the new challenges that the software engineering community has to address in the coming years to successfully apply artificial intelligence in software engineering, and we share our research roadmap toward the effective use of artificial intelligence in the software engineering profession, while still protecting fundamental human values. We spotlight three main areas that challenge the research in software engineering: the use of generative artificial intelligence and large language models for engineering large software systems, the need of large and unbiased datasets and benchmarks for training and evaluating deep learning and large language models for software engineering, and the need of a new code of digital ethics to apply artificial intelligence in software engineering.

AI-based software systems are rapidly spreading in various business areas. In this context, the unavoidable convergence of the Software Engineering and Artificial Intelligence and Machine Learning (AI/ML) disciplines is considered an obvious and one of the following significant challenges within the engineering process. The life cycle, models, and technologies of AI/ML elements are pretty specific, and this should be considered in software engineering to ensure their performance and compliance with business needs. AI/ML applications have some distinct characteristics compared to traditional software applications. Thus, several challenges and risk factors regarding AI/ML applications appear to software developers. To study the common challenges in AI/ML application development, we used two different perspectives: software engineering and machine learning. AI/ML applications, like other software systems, need a well-defined software engineering process for their development and maintenance. We discussed challenges and recommendations for different phases of the software development life cycle for ML applications, particularly requirement engineering, design, implementation, integration, testing, and deployment. AI/ML application development has specific aspects to consider as a software development project. We discussed the characteristics and recommendations concerning problem formulation, data acquisition, preprocessing, feature extraction, model building, evaluation, model integration and deployment, model management, and ethics in AI/ML development. In the work, there were formulated recommendations for each analyzed challenge that should be useful for software developers. The next stage of this research is the compilation of detailed systematic guidelines for the software development process for AI/ML systems.

The software engineering landscape is undergoing a significant transformation with the advent of artificial intelligence (AI). AI technologies are poised to redefine traditional software development practices, offering innovative solutions to long-standing challenges. This paper explores the integration of AI into software engineering processes, aiming to identify its impacts, benefits, and the challenges that accompany this paradigm shift. A comprehensive analysis of current AI applications in software engineering is conducted, supported by case studies and theoretical models. The study examines various phases of software development to assess where AI contributes most effectively. The integration of AI enhances productivity, improves code quality, and accelerates development cycles. Key areas of impact include automated code generation, intelligent debugging, predictive maintenance, and enhanced decision-making processes. AI is revolutionizing software engineering by introducing automation and intelligence into the development lifecycle. Embracing AI-driven tools and methodologies is essential for staying competitive in the evolving technological landscape.

Artificial intelligence ethics is a field gaining vast functional importance in recent years. Most people assume that Artificial Intelligence (AI) could make employees redundant in future while others raise questions about ethical line of control while using AI technologies. Thus ensuring protection of data while adopting new technologies is critical and needs the prioritization of ethical standards for AI. Globalized bodies, particularly government agencies, private firms and academic institutions have made comprehensive efforts to address numerous ethical issues by writing AI ethical guidelines and holding active debates on AI ethics within and outside the AI community. This article addresses these activities in the form of a systematic literature review (SLR) with an emphasis on the recognition and practice of basic ethical concepts of AI. To the best of our knowledge, no such SLR has been conducted in this domain in past. In this SLR, research articles have been collected between the span of 2015 to 2020. A total of 784 articles have been analysed from which only 44 papers were selected for their proposed works. The analysis founds a consensus among specific standards of AI and commonly applied principles for clarity, equality, accountability, non-maleficence, and secrecy. This article presents a comprehensive review of the schemes developed and applied by the researchers for introduction of AI ethics. In this review, it has been noted that ethical standards must be integrated with all aspects of AI product life cycle.

This article discusses trends in the use of artificial intelligence (AI) in social sciences and natural sciences research. The introduction highlights how AI has evolved into an essential tool in both fields, addressing the limitations of traditional methods in social sciences and accelerating data analysis in natural sciences. The research method used is bibliometric analysis, with data collected from Google Scholar using keywords related to AI in social and natural sciences. Relevant articles were selected through a content evaluation and exclusion process, resulting in 1,000 social science publications and 999 natural science publications, which were further analyzed using VOSviewer with such as being outside the five-year range (published from 2020 to 2025). The study's findings indicate that in social sciences, AI is widely used to enhance research effectiveness through faster data processing, particularly in higher education and social policy analysis. Additionally, AI studies in social sciences are expanding, focusing on ethics, regulation, and human-AI interaction. In natural sciences, AI plays a crucial role in resource management, environmental research, and the healthcare industry, including disease diagnosis and drug development. Recent trends also show an increasing use of large language models (LLMs) and natural language processing (NLP) in scientific research. The study concludes that AI has become a key element in both social and natural science research. Recommendations for social science researchers include further exploration of AI’s impact on psychology, law, and education, as well as the use of bibliometric methods. Meanwhile, natural science researchers are advised to focus on improving AI transparency, developing more accurate technologies, and applying AI in environmental and industrial research. Interdisciplinary collaboration is necessary to ensure AI development remains ethical and inclusive.

The rapid integration of Artificial Intelligence (AI) technologies has transformed the global software engineering industry, and this change is no exception in Sri Lanka. This research study focuses on the impact of AI on salary trends, job role demands, and evolving skill requirements within Sri Lanka's software engineering sector. This study examined survey responses and analyzed LinkedIn job postings to identify the overarching trends: the surging demand for AI-centric roles, such as machine learning engineers, data scientists, and AI specialists, while the traditional roles like software developers are being redefined and specialized. It also investigates how dependence on AI is growing and focuses on the automatic code generation platforms, intelligent debugging assistants, and AI-driven testing frameworks, providing additional resources to accelerate software development process of software, enhancing productivity, and bringing quality to code. It calls for a complete set of new skills, which are machine learning, natural language processing, and AIbased software testing, in addition to bringing more efficiency into the workflow. Upskilling and educational reforms are crucial to addressing skill gaps created by AI in the software engineering landscape. It aims to provide actionable insights to industry stakeholders, educators, and policymakers on adapting to the AI-driven transformations necessary to maintain competitiveness of Sri Lanka in the software engineering workforce.

This paper proposes an ontology-based approach to facilitate collaboration between domain experts and software engineers for development of engineering software applications. In this approach, ontologies are employed to serve as the knowledge interfaces for encapsulating the analysis units identified from the solution workflow of the targeted problem domain and for reducing the collaboration complexity and knowledge coupling between domain problem solving and software engineering in software development. In this paper, the proposed ontology-based software development approach is discussed. Some considerations are given to the software development environment needed for realization of the proposed approach. In addition, the application of the proposed approach is demonstrated using an engineering software development example and a prototype ontology-based software development environment.

As automation is changing everything in today’s world, there is an urgent need for artificial intelligence, the basic component of today’s automation and innovation to have standards for software engineering for analysis and design before it is synthesized to avoid disaster. Artificial intelligence software can make development costs and time easier for programmers. There is a probability that society may reject artificial intelligence unless a trustworthy standard in software engineering is created to make them safe. For society to have more confidence in artificial intelligence applications or systems, researchers and practitioners in computing industry need to work not only on the cross-section of artificial intelligence and software engineering, but also on software theory that can serve as a universal framework for software development, most especially in artificial intelligence systems. This paper seeks to(a) encourage the development of standards in artificial intelligence that will immensely contribute to the development of software engineering industry considering the fact that artificial intelligence is one of the leading technologies driving innovation worldwide (b) Propose the need for professional bodies from philosophy, law, medicine, engineering, government, international community (such as NATO, UN), and science and technology bodies to develop a standardized framework on how AI can work in the future that can guarantee safety to the public among others. These standards will boost public confidence and guarantee acceptance of artificial intelligence applications or systems by both the end-users and the general public.

This workshop is the fourth in the series and continued to build upon the work carried out at the previous iterations of the International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, which were held at ICSE in 2012, 2013 and 2014. RAISE 2015 brought together researchers and practitioners from the artificial intelligence (AI) and software engineering (SE) disciplines to build on the interdis- ciplinary synergies that exist and to stimulate further interaction across these disciplines. Mutually beneficial characteristics have appeared in the past few decades and are still evolving due to new challenges and technological advances. Hence, the question that motivates and drives the RAISE Workshop series is: "Are SE and AI researchers ignoring important insights from AI and SE?". To pursue this question, RAISE'15 explored not only the application of AI techniques to SE problems but also the application of SE techniques to AI problems. RAISE not only strengthens the AI- and-SE community but also continues to develop a roadmap of strategic research directions for AI and SE.