Vehicle API testing verifies whether the interactions between a vehicle's internal systems and external applications meet expectations, ensuring that users can access and control various vehicle functions and data. However, this task is inherently complex, requiring the alignment and coordination of API systems, communication protocols, and even vehicle simulation systems to develop valid test cases. In practical industrial scenarios, inconsistencies, ambiguities, and interdependencies across various documents and system specifications pose significant challenges. This paper presents a system designed for the automated testing of in-vehicle APIs. By clearly defining and segmenting the testing process, we enable Large Language Models (LLMs) to focus on specific tasks, ensuring a stable and controlled testing workflow. Experiments conducted on over 100 APIs demonstrate that our system effectively automates vehicle API testing. The results also confirm that LLMs can efficiently handle mundane tasks requiring human judgment, making them suitable for complete automation in similar industrial contexts.

Imagine if your software development team could use one simple testing tool having artificial intelligence to shorten delivery cycles, improve customer experience, update new features regularly and ramp up DevOps with best practices. What if we could share some insights on the growing trend of artificial intelligence in software testing. And how we used an AI plugin with a testing tool for one of our customers to automate functionality in less than a day. All this can be done having a basic knowledge of a programming language and testing of UI and API both on the web and mobile. You would be pumped to witness how artificial intelligence can revolutionize your software testing.

Recently, a simple conversation I had analyzing the current challenges associated with software testing in the modern era led to a key realization: the tools in the software testing industry have not been focused on simplicity for the Agile world. Agile is primarily a development-focused activity. In its most basic terms, Agile is a software development methodology where typical SDLC activities that would traditionally span over the duration of a project are broken down into much smaller pieces called sprints. Typically, a sprint is 2 to 3 weeks and in a sprint, development activities are focused on new features and enhancements. A sprint starts with the design and creation phase, where the new functionality is split up into user stories, scoped, and then development immediately starts building something.

Recently, a simple conversation I had analyzing the current challenges associated with software testing in the modern era led to a key realization: the tools in the software testing industry have not been focused on simplicity for the Agile world. Agile is primarily a development-focused activity. In its most basic terms, Agile is a software development methodology where typical SDLC activities that would traditionally span over the duration of a project are broken down into much smaller pieces called sprints. Typically, a sprint is 2 to 3 weeks and in a sprint, development activities are focused on new features and enhancements. A sprint starts with the design and creation phase, where the new functionality is split up into user stories, scoped, and then development immediately starts building something.