Legacy systems still play a critical role in the operation of many software organizations. However, these systems have high maintenance costs due to their reliance on deprecated technologies. Also, they are often too complex to be rewritten from scratch. Such systems could benefit from being migrated to an event-driven architecture (EDA), as it enables the creation of software systems with loosely coupled components which improves their maintainability and scalability. However, migrating legacy systems is not straightforward, and may have significant costs. Furthermore, there is limited empirical knowledge on how practitioners approach legacy-to-EDA migration in industrial settings. To bridge this gap, we conducted a survey with software practitioners to investigate the state of practice of legacy-to-EDA migration. The purpose of the survey is to gain insights on the methodologies adopted for implementing EDAs, and the specifics of the legacy-to-EDA migration process in industrial settings. The survey consists of two parts: (1) an online questionnaire featuring 26 questions, and (2) an interview session with some of the participants. The questionnaire was answered by 31 participants, two of whom volunteered for the interview. The key findings of the survey include: (1) the main motivation behind legacy-to-EDA migrations is to decouple parts of the system, (2) the technologies preferred by professionals when implementing EDAs are Java and Apache Kafka, and (3) software professionals mainly rely on business processes and human expertise to guide the migration. Our study highlights the limited adoption of automation tools in the legacy-to-EDA migration process. It also emphasizes the need for practitioners to develop tools that facilitate the migration and to adopt best practices to handle data consistency.

In model-driven engineering, the concrete syntax of a domain-specific modeling language (DSML) is fundamental as it constitutes the primary point of interaction between the user and the DSML. Nevertheless, the conventional one-size-fits-all approach to concrete syntax often undermines the effectiveness of DSMLs, as it fails to accommodate the diverse constraints and specific requirements inherent to diverse users and usage contexts. Such shortcomings can lead to a significant decline in the performance, usability, and efficiency of DSMLs. This vision paper proposes a conceptual framework to generate concrete syntax intelligently. Our framework considers multiple concerns of users and aims to align the concrete syntax with the context of the DSML usage. Additionally, we detail a baseline process to employ our framework in practice, leveraging large language models to expedite the generation of tailored concrete syntax. We illustrate the potential of our vision with two concrete examples and discuss the shortcomings and research challenges of current intelligent generation techniques.

Cross-platform mobile app development frame-works allow developers to use a single codebase to develop apps targeting different platforms. As these frame-works provide distinct features and may impact the apps' quality, their selection must be done with care. Although many works evaluated mobile frame-works, there is no synthesis on these studies. In this paper, we present a Systematic Literature Review (SLR) on approaches that evaluated cross- platform frame-works. Our SLR covers 75 papers and provides insights on 1) the most studied frame-works, 2) the criteria used for evaluation, 3) the evaluation methods used and 4) the results of these evaluations. The SLR shows that prior works generally used a prototype app to evaluate the frame-works but none explored the impact of the frame-works on the app's code quality. Thus, we carried out a preliminary empirical study on 3,566 mobile apps to evaluate the impact of mobile frame-works on the number of bugs and code smells in apps. The results of the study on native Android and React Native indicate that the latter has fewer code smells than native Android apps. Native Android apps generally had worse quality considering the number of bugs and code smells.