This paper demonstrates an AI-assisted search process to aid in literature surveys within a fast-moving research area, and assesses the usefulness and validity of these results.
Abstract
Formal methods are software engineering approaches with a rigorous mathematical basis that can help ensure the correctness of software systems, especially where safety or security is critical. Artificial Intelligence (AI) has developed very rapidly in the area of Generative AI (GenAI), where questions can be answered with increasingly impressive but potentially unreliable and variable results. This paper surveys research in integrating the two approaches in a synergistic manner. Traditionally, such explorations have required significant manual efforts in searching for and evaluating existing research. However, most relevant publications are now accessible online, and AI tools are increasingly good at answering research questions with more and more reliability. This paper takes the approach of using GenAI to evaluate research questions on combining formal methods and AI-related techniques. The paper assesses the usefulness and validity of these results. With recent improvements in AI search tools, the approach is now a useful aid to researchers, significantly reducing the time needed to survey existing research, while always needing human checking by an expert. In addition, the combination of formal methods and AI approaches looks to be an interesting and beneficial research area with potential industrial-scale applications in the future. This paper does not claim to be an exhaustive systematic review, but rather demonstrates an AI-assisted search process to aid in literature surveys within a fast-moving research area.
A baseline review of the selected papers volume of the inaugural International Conference on Formal Methods and Foundations of Artificial Intelligence (FMF-AI 2025), published as Annales Mathematicae et Informaticae, introduces simple baseline metrics that can be recomputed in future FMF-AI editions to observe structural changes in the research landscape.
Gábor Kusper· Annales Mathematicae et Info...· 0 citations
Over the past decade, the exponential integration of artificial intelligence (AI) systems across various sectors has been propelled by significant advances in machine learning algorithms, data availability, and computational power. This progress has produced highly effective AI systems, but also underscores the critical need for effective auditing to critically evaluate these technologies. In this paper, we conduct a systematic review of the literature on methodologies, frameworks, and techniques for auditing AI systems, focusing on legal and ethical considerations and compliance with regulations. By reviewing key academic databases, including Google Scholar, IEEE, ACM, and Springer, we establish the scope of our survey and derive topics from our research questions. Our findings reveal gaps in current auditing practices and highlight the importance of incorporating AI value chain stages and AI maturity levels into auditing frameworks. This approach enables us to distinguish and recommend existing frameworks and methodologies that are most suitable for the specific contexts of different organisations, thus enhancing the effectiveness of AI system evaluations.
Usman Shahbaz, Amin Beheshti, B. Abedin et al.· ACM Computing Surveys· 0 citations
To identify reported practices for software systems with GenAI components in academic literature compiled in a catalog to serve as a guide for software engineers to develop safe, accurate, and reliable software systems, a systematic literature review was conducted.
In the last few years Generative Artificial Intelligence (GenAI) has taken universities by storm. This does not only affect the way what and how to teach and how to assess knowledge and competences in exams, but it also impacts the way how to do research. On one hand, GenAI has fast access to a huge amount of wide-ranging (explicit) knowledge and the capability to extract and combine knowledge that seems to be relevant in the context of certain research. On the other hand, research is closely linked to the human capability of being creative showing curiosity, care, collaboration, and critical thinking. Against this background, the paper addresses a question of immense social and also ethical relevance: May we use (Gen)AI as tool for scientific research and if so to what extent? The paper starts analysing the problem from a theoretical and conceptual point of view by comparing capabilities of GenAI with those of a good researcher. Based on this the complete research cycle is investigated to understand which capabilities are more helpful to achieve research results of high quality and novelty, but in an effective way. To gather empirical data from a self-experiment, the setup of an unexperienced researcher (e.g. PhD student) with just a rough idea about the intentional research focus was chosen. Addressing the first and fundamental phase of any research process, idea generation, by comparing performance of an AI researcher with a human one, results demonstrate the usefulness of combining strength of both worlds mandatorily keeping the human researcher in the loop. With this, the paper focusses on chances and challenges coming with the highly dynamic evolution of AI within a complex, knowledge-intense application area where human experience, problem-solving capability and creativity is decisive. It contributes to discussions about necessary adjustments in how organizations (and particularly universities) manage their knowledge generating potential considering artificial and human intelligence.
E. Tomé, G. Neumann, Igor Lopes Martinez· European Conference on Knowl...· 0 citations
The results show that AI effectively lowers cognitive barriers by summarizing pull requests, detecting minor localized errors, and helping developers discover system-level problems, but AI tools must adopt a "do more with less" design philosophy where the design should focus on high-reliability features and not overload the users with information.
This article examines an emergent research program aimed at the complete automation of science, analyzing how various automation projects funded by DARPA and others are working to replicate traditionally human scientific competencies. Rather than directly attempting to create fully autonomous robot scientists, these projects break down complex scientific work into discrete, automatable “tasks.” While individual projects often preserve some role for human scientists, collectively they reveal a comprehensive blueprint for scientific automation. Through interviews with 26 DARPA contractors, program managers, and others involved in scientific automation, the authors demonstrate how automation efforts target three key domains of human scientific expertise: insight (hypothesis generation and pattern recognition), embodied knowledge (experimental procedures and techniques), and judgment (evaluation of scientific claims). Instead of simply trying to replicate human capabilities, these initiatives are actively reshaping scientific practice to be more amenable to machine intervention through initiatives like standardized ontologies and machine-readable data formats. Although full automation faces significant technical and institutional challenges, this emergent program portends major changes in scientific practice and careers while raising important questions about the future role of human knowledge in scientific discovery. The article contributes to ongoing debates about artificial intelligence in science while challenging existing sociological accounts about purportedly uniquely human scientific abilities.
David Peterson, Bernard J. Koch, Ramya Natarajan et al.· Big Data & Society· 0 citations
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