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Background: Understanding dependencies within microservices is essential for maintaining and evolving scalable and efficient software architectures. Dependencies influence how changes in one microservice might propagate to other microservices. With the decentralized nature of microservices, these dependencies might not be explicit to developers and lead to unique challenges in modern software development environments. Objective: The objective of this study is to synthesize existing literature on microservice dependencies, identify the types of dependencies, and examine the strategies employed to manage and analyze these relationships. This effort aims to elucidate how dependencies affect microservice systems and to provide a comprehensive overview of dependency management within microservices. Method: We conducted a multivocal literature review, starting with an initial dataset of 1,733 papers from academic literature (white literature). This corpus was narrowed down through a rigorous filtering process to 45 key publications that address the identification, management, and impacts of dependencies in microservices. Additionally, we incorporated 926 articles from grey literature sources such as Google, Stack Overflow, and Stack Exchange, expanding the scope beyond traditional academic research. After the filtration process, 45 articles were fully synthesized to integrate practical insights and professional experiences into our review. Results: The review identifies several types of dependencies in microservice systems and synthesizes this information into a unified dependency taxonomy. This review highlights a range of approaches to dependency management, revealing a significant gap in systematic catering approaches to generate taxonomies for dependencies and the need for integrated management tools. The findings underscore the fragmented nature of existing dependency management practices and the potential for more holistic approaches. Conclusion: This study provides valuable insights for researchers and practitioners, outlining effective strategies and pointing out areas needing improvement in dependency management. By offering a structured overview of the topic, the study serves as a roadmap for future research and development efforts to enhance the robustness and maintainability of microservices. 

Complex software systems consist of multiple overlapping design structures, such as abstractions, features, crosscutting concerns, or patterns. This is similar to how a human body has multiple interacting subsystems, such as respiratory, digestive, or circulatory. Unlike in the medical domain, software designers do not have an effective way to distinguish, visualize, comprehend, and analyze these interleaving design structures. As a result, developers often struggle through the maze of source code. In this paper, we present an Automated Concept Explanation (ACE) framework that automatically extracts and categorizes major concepts from source code based on the roles that files play in design structures and their topic frequencies. Based on these categorized concepts, ACE recovers four categories of high-level design models using different algorithms and generates a natural language explanation for each. To assess if and how ACE can help developers better understand design structures, we conducted an empirical study where two groups of graduate students were assigned three design comprehension tasks: identifying feature-related files, identifying dependencies among features, and identifying design patterns used, in an open-source project. The results reveal that the students who used ACE can accomplish these tasks much faster and more accurately, and they acknowledged the usefulness of the categorized concepts and structures, multi-type high-level model visualization, and natural language explanations. 

Background: Software practitioners need reliable metrics to monitor software evolution, compare projects, and understand modularity variations. This is crucial for assessing architectural improvement or decay. Existing popular metrics offer little help, especially in systems with implicitly connected but seemingly isolated files. Aim: Our objective is to explore why and how state-of-the-art modularity measures fail to serve as effective metrics and to devise a new metric that more accurately captures complexity changes and is less distorted by sizes or isolated files. Methods: We analyzed metric scores for 1,220 releases across 37 projects to identify the root causes of their shortcomings. This led to the creation of M-score, a new software modularity metric that combines the strengths of existing metrics while addressing their flaws. M-score rewards small, independent modules, penalizes increased coupling, and treats isolated modules and files consistently. Results: Our evaluation revealed that M-score outperformed other modularity metrics in terms of stability, particularly with respect to isolated files, because it captures coupling density and module independence. It also correlated well with maintenance effort, as indicated by historical maintainability measures, meaning that the higher the M-score, the more likely maintenance tasks can be accomplished independently and in parallel. Conclusions: Our research identifies the shortcomings of current metrics in accurately depicting software complexity and proposes M-score, a new metric with superior stability and better reflection of complexity and maintenance effort, making it a promising metric for software architectural assessments, comparison, and monitoring. 

In this paper we reflect on our decade-long journey of creating, evolving, and evaluating a number of software design concepts and technical debt management technologies. These include: a novel maintainability metric, a new model for representing design information, a suite of design anti-patterns, and a formalized model of design debt. All of these concepts are rooted in options theory, and they all share the objective of helping a software project team quantify and visualize major design principles, and address the very real maintainability challenges faced by their organizations in practice. The evolution of our research has been propelled by our continuous interactions with industrial collaborators. For each concept, technology, and supporting tool, we embarked on an ambitious program of empirical validation—in “the lab”, with industry partners, and with open source projects. We reflect on the successes of this research and on areas where significant challenges remain. In particular, we observe that improved software design education, both for students and professional developers, is the prerequisite for our research and technology to be widely adopted. During this journey, we also observed a number of gaps: between what we offer in research and what practitioners need, between management and development, and between debt detection and debt reduction. Addressing these challenges motivates our research moving forward. 

In this paper, we introduce CIDER, a Concept-based Interactive DE-sign Recovery tool that recovers a software design in the form of hierarchically organized concepts. In addition to facilitating design comprehension, it also enables designers to assess design quality and identify design problems. It integrates multiple clustering algorithms to reduce the complexity of the recovered design structure, leverages information retrieval techniques to name each cluster using the most relevant topic terms to ease design comprehension, and identifies and labels highly-coupled file clusters to reveal possible design problems. It enables interactive selection of concepts of interest and recovers partial design structures accordingly. The user can also interactively change the levels of recovered hierarchical structure to visualize the design at different granularities.