This paper introduces a GenAI-driven approach for automated test case generation, leveraging Large Language Models and Vision-Language Models to translate natural language requirements and system diagrams into structured Gherkin test cases. The methodology integrates Vehicle Signal Specification modeling to standardize vehicle signal definitions, improve compatibility across automotive subsystems, and streamline integration with third-party testing tools. Generated test cases are executed within the digital.auto playground, an open and vendor-neutral environment designed to facilitate rapid validation of software-defined vehicle functionalities. We evaluate our approach using the Child Presence Detection System use case, demonstrating substantial reductions in manual test specification effort and rapid execution of generated tests. Despite significant automation, the generation of test cases and test scripts still requires manual intervention due to current limitations in the GenAI pipeline and constraints of the digital.auto platform.

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.

This paper introduces CRITICAL, a novel closed-loop framework for autonomous vehicle (AV) training and testing. CRITICAL stands out for its ability to generate diverse scenarios, focusing on critical driving situations that target specific learning and performance gaps identified in the Reinforcement Learning (RL) agent. The framework achieves this by integrating real-world traffic dynamics, driving behavior analysis, surrogate safety measures, and an optional Large Language Model (LLM) component. It is proven that the establishment of a closed feedback loop between the data generation pipeline and the training process can enhance the learning rate during training, elevate overall system performance, and augment safety resilience. Our evaluations, conducted using the Proximal Policy Optimization (PPO) and the HighwayEnv simulation environment, demonstrate noticeable performance improvements with the integration of critical case generation and LLM analysis, indicating CRITICAL's potential to improve the robustness of AV systems and streamline the generation of critical scenarios. This ultimately serves to hasten the development of AV agents, expand the general scope of RL training, and ameliorate validation efforts for AV safety.

In computer science education, test cases are an integral part of programming assignments since they can be used as assessment items to test students' programming knowledge and provide personalized feedback on student-written code. The goal of our work is to propose a fully automated approach for test case generation that can accurately measure student knowledge, which is important for two reasons. First, manually constructing test cases requires expert knowledge and is a labor-intensive process. Second, developing test cases for students, especially those who are novice programmers, is significantly different from those oriented toward professional-level software developers. Therefore, we need an automated process for test case generation to assess student knowledge and provide feedback. In this work, we propose a large language model-based approach to automatically generate test cases and show that they are good measures of student knowledge, using a publicly available dataset that contains student-written Java code. We also discuss future research directions centered on using test cases to help students.

Cyber-Physical Systems (CPSs) play a central role in the behavior of a wide range of autonomous physical systems such as medical devices, autonomous vehicles, and smart homes, many of which are safety-critical. CPSs are often specified iteratively as a sequence of models at different levels that can be tested via simulation systems at early stages of their development cycle. One such model is a hybrid automaton; these are used frequently for CPS applications and have the advantage of encapsulating both continuous and discrete CPS behaviors. When testing CPSs, engineers can take advantage of these models to generate test cases that target both types of these behaviors. Moreover, since these models are constructed early in the development process for CPSs, they allow test cases to be generated early in that process for those CPSs, even before simulation models of the CPSs have been designed. One challenge when testing CPSs is that these systems may operate differently even under an identically applied test scenario. In such cases, we cannot employ test oracles that use predetermined deterministic behaviors; instead, test oracles should consider sets of desired behaviors in order to determine whether the CPS has behaved appropriately. In this paper we present a test case generation technique, HYTEST, that generates test cases based on hybrid models, accompanied by appropriate test oracles, for use in testing CPSs early in their development cycle. To evaluate the effectiveness and efficiency of HYTEST, we conducted an empirical study in which we applied the technique to several CPSs and measured its ability to detect faults in those CPSs and the amount of time required to perform the testing process. The results of the study show that HYTEST was able to detect faults more effectively and efficiently than the baseline techniques we compare it to.

There is a constant ask from the customer on how to optimize the overall QA (Quality assurance) activities in terms of reducing cycle time, improving quality by reducing production defects, focused testing to get maximum defects in early development phases. Apart from this, most of the customers are adopting digital platforms such as PaaS (Platform as Services) & SaaS (Software as Services) solutions for faster delivery, so how can the QA Team keep pace with development and subsequent validation activities, by automating test case generation. Can we get insights into what areas to automate? Will there be any prediction on what will be the number of defects found, test cases need to be written based on the release magnitude. To get these answers, let's explore the solutions available which we can leverage.