REpresentational State Transfer (REST) Application Programming Interfaces (APIs) are widely used for the communication between loosely coupled web services. While the loose coupling allows services to evolve independently, it requires development teams to actively identify changes in their REST APIs to notify teams of affected services. If overlooked, changes can result in unexpected breaking changes that lead to failures in the affected service. In this paper, we present AutoGuard, our tool for automatically extracting and reporting breaking changes of REST APIs in services developed with the popular Java Spring Boot framework. AutoGuard consists of two components: the first component generates the OpenAPI descriptions of two versions of a REST API from the source code; the second component extracts and logs the differences between them and reports the API breaking changes. AutoGuard's static analysis does not require running a service to understand its REST API changes, enabling direct integration into the development process. We integrated AutoGuard into GitHub's and GitLab’ s contin-uous integration workflows, where it automatically generates the REST API change logs for pull and merge requests and reports any breaking changes. With this information, developers and code reviewers can make informed decisions on how to proceed with the requests. Video demonstration: https:/Iyoutu.be/3qeWIVfMvWE Tool repository: https:/Igithub.com/MSA-API-Management/AutoGuard

Nowadays, many companies design and develop their software systems as a set of loosely coupled microservices that communicate via their Application Programming Interfaces (APIs). While the loose coupling improves maintainability, scalability, and fault tolerance, it poses new challenges to the API evolution process. Related works identified communication and integration as major API evolution challenges but did not provide the underlying reasons and research directions to mitigate them. In this paper, we aim to identify microservice API evolution strategies and challenges in practice and gain a broader perspective of their relationships. We conducted 17 semi-structured interviews with developers, architects, and managers in 11 companies and analyzed the interviews with open coding used in grounded theory. In total, we identified six strategies and six challenges for REpresentational State Transfer (REST) and event-driven communication via message brokers. The strategies mainly focus on API backward compatibility, versioning, and close collaboration between teams. The challenges include change impact analysis efforts, ineffective communication of changes, and consumer reliance on outdated versions, leading to API design degradation. We defined two important problems in microservice API evolution resulting from the challenges and their coping strategies: tight organizational coupling and consumer lock-in. To mitigate these two problems, we propose automating the change impact analysis and investigating effective communication of changes as open research directions. Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board.

Peak mitigation is of interest to power companies as peak periods may require the operator to over provision supply in order to meet the peak demand. Flattening the usage curve can result in cost savings, both for the power companies and the end users. Integration of renewable energy into the energy infrastructure presents an opportunity to use excess renewable generation to supplement supply and alleviate peaks. In addition, demand side management can shift the usage from peak to off-peak times and reduce the magnitude of peaks. In this work, we present a data driven approach for incentive-based peak mitigation. Understanding user energy profiles is an essential step in this process. We begin by analysing a popular energy research dataset published by the Ausgrid corporation. Extracting aggregated user energy behavior in temporal contexts and semantic linking and contextual clustering give us insight into consumption and rooftop solar generation patterns. We implement, and performance test a blockchain-based prosumer incentivization system. The smart contract logic is based on our analysis of the Ausgrid dataset. Our implementation is capable of supporting 792,540 customers with a reasonably low infrastructure footprint.