Gustavo Rossi

Scraping is a topic studied from various perspectives, encompassing automatic and AI-based approaches, and a wide range of programming libraries that expedite development. As the volume of available web content increases, it becomes increasingly challenging to anticipate end-user requirements regarding what, how, and when to extract data from the web. This challenge is compounded when integrating data from multiple websites, particularly when websites’ search engines dynamically retrieve unavailable data via permanent links. Complex scraping processes, such as these are difficult to develop using general-purpose programming languages and are challenging to automate with AI-based approaches. Controllability is a crucial aspect of scraping, that is, how end users can make decisions during the scraper specification process, understand information sources, and how the data are ultimately extracted, compiled, and formatted for output. In response, our study presents an innovative end-user approach for specifying scrapers that focuses on seamlessly integrating data from multiple sources. Through this approach and its supporting toolset, we aim to provide users with greater control and transparency over the extraction, integration, and formatting of data, thereby addressing the key concerns in web scraping. The approach and toolset were evaluated and they yielded promising results.

Microservices architecture has emerged as a dominant approach for developing scalable and modular software systems, driven by the need for agility and independent deployability. However, designing these architectures poses significant challenges, particularly in service decomposition, inter-service communication, and maintaining data consistency. To address these issues, artificial intelligence (AI) techniques, such as machine learning (ML) and natural language processing (NLP), have been applied with increasing frequency to automate and enhance the design process. This systematic literature review examines the application of AI in microservices design, focusing on AI-driven tools and methods for improving service decomposition, decision-making, and architectural validation. This review analyzes research studies published between 2018 and 2024 that specifically focus on the application of AI techniques in microservices design, identifying key AI methods used, challenges encountered in integrating AI into microservices, and the emerging trends in this research area. The findings reveal that AI has effectively been used to optimize performance, automate design tasks, and mitigate some of the complexities inherent in microservices architectures. However, gaps remain in areas such as distributed transactions and security. The study concludes that while AI offers promising solutions, further empirical research is needed to refine AI’s role in microservices design and address the remaining challenges.

Requirements engineering is one the most critical stages in software development. If the requirements are not correct the software development team will produce an artifact that will not satisfy the needs, wishes and expectations of the client. Requirements (and knowledge in general) are spread among many stakeholders. Natural language is widely used since it is an adequate tool con-sidering non-technical stakeholder. Nevertheless communication problems arise with the use of natural language. The software development team members need to learn about the application domain and this process of learning means focusing on the features to be included in the software application, while leav-ing apart the elements out of the boundaries of the application. This process is not easy when people face a new application domain. Thus, this paper proposes an approach to define the software application language from a vocabulary of the application domain.

Designing and evaluating user interfaces is a complex task. The current focus on user experience (UX) demands user-centered processes that take considerable time and resources, and thus collide with the fast-paced agile methodologies that currently dominate the industry. In this context, academia and industry have proposed the automation of different tasks in the design and evaluation user interfaces (UIs) like prototyping and user testing. Artificial intelligence (AI) techniques such as machine learning have been a considerable help to achieve this. The articles featured in this special issue describe original proposals in the use of AI for UI and user behavior evaluation.