Software modernization is a common activity in software engineering, since technologies advance, requirements change, and business models evolve. Differently from conventional software evolution (e.g., adding new features, enhancing performance, or adapting to new requirements), software modernization involves re-engineering entire legacy systems (e.g., changing the technology stack, migrating to a new architecture style, or programming paradigms). Given the pervasive nature of software today, modernizing legacy systems is paramount to provide customers with competitive and innovative products and services, while keeping companies profitable. Despite the prevalent discussion of software modernization in gray literature, and the many papers in the literature, there is no work presenting a “big picture” of contemporary software modernization, describing challenges, and providing a well-defined research agenda. The goal of this work is to describe the state of the art in software modernization in the past 10 years. We collect the state of the art by performing a rapid review (searching five digital libraries), identifying potential 3,460 studies, leading to a final set of 126. We analyzed these studies to understand which strategies are employed, the driving forces that lead organizations to modernize their systems, and the challenges that need to be addressed. The results show that studies in the last 10 years have explored eight strategies for modernizing legacy systems, namely cloudification, architecture redesign, moving to a new programming language, targeting reuse optimization, software modernization for new hardware integration, practices to leverage automation, database modernization, and digital transformation. Modernization is triggered by 14 driving forces, with the most common ones being reducing operational costs, improving performance and scalability, and reducing complexity. In addition, based on the analysis of existing literature, we present a detailed discussion of research opportunities in this field. The main challenges are providing tooling support, followed by defining a modernization process and considering better evaluation metrics. The main contribution of our work is to equip practitioners and researchers with knowledge of the current state of contemporary software modernization so that they are aware of practices and challenges to be addressed when deciding to modernize legacy systems.

The modernization of monolithic legacy systems with microservices has been a trend in recent years. As part of this modernization, identifying microservice candidates starting from legacy code is challenging, as maintainers may consider many criteria simultaneously. Multi-objective search-based approaches represent a promising state-of-the-art solution to support this decision-making process. However, the rationale to adopt each microservice candidate automatically identified by these approaches is poorly investigated in industrial cases. Furthermore, studies with these approaches have not carefully investigated how maintainers reason and make decisions when designing microservice architectures from legacy systems. To address this gap, we conducted an on-site case study with maintainers of an industrial legacy system to investigate the usefulness of automatically generated microservice architectures. We analyze design decisions pointed out by the maintainers when reasoning about microservice candidates using several criteria at the same time. Our study is the first to assess a search-based approach involving actual maintainers conceiving microservice architectures in an industrial setting. Therefore, firstly, we considered individual evaluation of microservice candidates to understand the rationale for identifying a service. Secondly, we conducted a focus group study with maintainers with the goal of investigating design decisions at an architectural level. The results show that: (i) the automated approach is able to identify useful microservices; (ii) the criteria observed by previous studies are, in fact, considered by maintainers; and (iii) the maintainer profiles, i.e., the preferred granularity for microservice, highly affect design decisions. Finally, we observed the maintainers needed little effort in adjusting the automatically identified microservices to make them adoptable. In addition to indicating a promising potential of search-based approaches to generate microservice architectures, our findings highlight the need for: (i) interactive and/or customizable approaches that enable maintainers to include their preferences during the search process, and (ii) flexible or automated selection of criteria that fits the scenario in which the modernization is taking place.