REST APIs are widely used in industry, in all different kinds of domains. An example is Volkswagen AG, a German automobile manufacturer. Established testing approaches for REST APIs are time consuming, and require expertise from professional test engineers. Due to its cost and importance, in the scientific literature several approaches have been proposed to automatically test REST APIs. The open-source, search-based fuzzer EVOMASTER is one of such tools proposed in the academic literature. However, how academic prototypes can be integrated in industry and have real impact to software engineering practice requires more investigation. In this paper, we report on our experience in using EVOMASTER at Volkswagen. We share our learnt lessons, and identify real-world research challenges that need to be solved.

Testing is an important task within software development. To write test cases and integrate them into an automated test suite requires a significant amount of work. Given a set of requirements and specifications of a software, testing is needed to verify its correctness. When done manually, it is an expensive and error prone task. To facilitate such work, automated test-case generation via tools could be useful. Test-case generation can be facilitated by deterministic algorithm-driven approaches or non-deterministic approaches such as with AI (e.g., evolutionary and LLM). The different approaches come with their strengths and weaknesses, which must be considered when integrating these approaches into a product test procedure in industry. Several novel testing techniques and tools have been developed in academia and industry, but how effective they are and how to integrate them in real-world large industrial scenarios is still unclear. In this paper, a systematic approach is presented to evaluate test-case generation methodologies and integrate them into a scalable enterprise setup. The specific context is black-box testing of REST APIs, based on their OpenAPI schemas. The aim is to facilitate IT product development and service delivery. The proposed Technology Adoption Performance Evaluation (TAPE) approach is evaluated by a case study within the Group IT of Volkswagen AG. We evaluated existing tools such as OpenAPI Generator, EvoMaster and StarCoder which are built on different technologies. Our results show that these tools are of benefit for test engineers to facilitate test-case specification and design within the Group IT of Volkswagen AG.

ABSTRACT The usage of micro‐services in IT services is increasing. As this growth continues, the importance of eco‐friendly design and operation becomes a significant factor. This study aims to evaluates the potential of Java frameworks that facilitate cloud‐native micro‐services in reducing the energy footprint throughout the release lifecycle. The release lifecycle view also looks into the development phase and its footprint impact respectively potentials to build overall more energy footprint optimized releases. Technology‐driven methods and tools based on micro‐service frameworks can help reduce the energy footprint at the micro‐service level. However, local optimizations at the micro‐service level cannot diminish the importance of more holistic approaches, such as optimizing the overall system, architecture and design of micro‐services. The concepts derived from this analysis can be implemented in industrial settings, as presented by the case study. The effects are measurable and represent a positive step towards more eco‐friendly cloud‐native micro‐service‐based IT service offerings.