Understanding API evolution and the introduction of breaking changes (BCs) in software libraries is essential for library maintainers to manage backward compatibility and for researchers to conduct empirical studies on software library evolution. In Java, tools such as Japicmp and Revapi are commonly used to detect BCs between library releases, but their reliance on binary JARs limits their applicability. This restriction hinders large-scale longitudinal studies of API evolution and fine-grained analyses such as commit-level BC detection. In this paper, we introduce Roseau, a novel static analysis tool that constructs technology-agnostic API models from library code equipped with rich semantic analyses. API models can be analyzed to study API evolution and compared to identify BCs between any two versions of a library (releases, commits, branches, etc.). Unlike traditional approaches, Roseau can build API models from source code or bytecode, and is optimized for large-scale longitudinal analyses of library histories. We assess the accuracy, performance, and suitability of ROSEAU for longitudinal studies of API evolution, using Japicmp and Revapi as baselines. We extend and refine an established benchmark of BCs and show that Roseau achieves higher accuracy ($F_{1}=0.99$) than Japicmp ($F_{1}=0.86$) and Revapi ($F_{1}=0.91$). We analyze 60 popular libraries from Maven Central and find that Roseau delivers excellent performance, detecting BCs between versions in under two seconds, including in libraries with hundreds of thousands of lines of code. We further illustrate the limitations of Japicmp and REVAPI for longitudinal studies and the novel analysis capabilities offered by Roseau by tracking the evolution of Google's Guava API and the introduction of BCs over 14 years and 6,839 commits, reducing analysis times from a few days to a few minutes.

Software developers interact with APIs on a daily basis and, therefore, often face the need to learn how to use new APIs suitable for their purposes. Previous work has shown that recommending usage patterns to developers facilitates the learning process. Current approaches to usage pattern recommendation, however, still suffer from high redundancy and poor run-time performance. In this paper, we reformulate the problem of usage pattern recommendation in terms of a collaborative-filtering recommender system. We present a new tool, FOCUS, which mines open-source project repositories to recommend API method invocations and usage patterns by analyzing how APIs are used in projects similar to the current project. We evaluate FOCUS on a large number of Java projects extracted from GitHub and Maven Central and find that it outperforms the state-of-the-art approach PAM with regards to success rate, accuracy, and execution time. Results indicate the suitability of context-aware collaborative-filtering recommender systems to provide API usage patterns.

OSGi is a module system and service framework that aims to fill Java's lack of support for modular development. Using OSGi, developers divide software into multiple bundles that declare constrained dependencies towards other bundles. However, there are various ways of declaring and managing such dependencies, and it can be confusing for developers to choose one over another. Over the course of time, experts and practitioners have defined "best practices" related to dependency management in OSGi. The underlying assumptions are that these best practices (i) are indeed relevant and (ii) help to keep OSGi systems manageable and efficient. In this paper, we investigate these assumptions by first conducting a systematic review of the best practices related to dependency management issued by the OSGi Alliance and OSGi-endorsed organizations. Using a large corpus of OSGi bundles (1,124 core plug-ins of the Eclipse IDE), we then analyze the use and impact of 6 selected best practices. Our results show that the selected best practices are not widely followed in practice. Besides, we observe that following them strictly reduces classpath size of individual bundles by up to 23% and results in up to ±13% impact on performance at bundle resolution time. In summary, this paper contributes an initial empirical validation of industry-standard OSGi best practices. Our results should influence practitioners especially, by providing evidence of the impact of these best practices in real-world systems.

Large Internet companies like Amazon, Netflix, and LinkedIn are using the microservice architecture pattern to deploy large applications in the cloud as a set of small services that can be developed, tested, deployed, scaled, operated and upgraded independently. However, aside from gaining agility, independent development, and scalability, infrastructure costs are a major concern for companies adopting this pattern. This paper presents a cost comparison of a web application developed and deployed using the same scalable scenarios with three different approaches: 1) a monolithic architecture, 2) a microservice architecture operated by the cloud customer, and 3) a microservice architecture operated by the cloud provider. Test results show that microservices can help reduce infrastructure costs in comparison to standard monolithic architectures. Moreover, the use of services specifically designed to deploy and scale microservices reduces infrastructure costs by 70% or more. Lastly, we also describe the challenges we faced while implementing and deploying microservice applications.

Software Product Line Engineering has introduced feature modeling as a domain analysis technique used to represent the variability of software products and decision-making scenarios. We present a model-based transformation approach to solve conflicts among configurations performed by different stakeholders on feature models. We propose the usage of a domain-specific language named CoCo to specify attributes as non-functional properties of features, and to describe business-related decision rules in terms of costs, time, and human resources. These specifications along with the stakeholders' configurations and the feature model are transformed into a constraint programming problem, on which decision rules are executed to find a non-conflicting set of solution configurations that are aligned to business objectives. We evaluate CoCo's compositionality and model complexity simplification while using a set of motivating decision scenarios.