Use of Large Language Models (LLMs) in Automotive Test Engineering


Large language models (LLMs) trained on AUTOSAR specifications can be used to generate test cases for AUTOSAR systems. AUTOSAR is a set of software standards for automotive embedded systems. It provides a common framework for the development of automotive software, which makes it easier to test and maintain.

LLMs can be used to generate test cases by understanding the AUTOSAR specification and generating code that exercises the different features of the specification. This can be done by using the LLM to generate sequences of input and expected output values for the different functions and services defined in the specification.

LLMs can also be used to generate test cases that are more complex and challenging than those that can be generated manually. For example, LLMs can be used to generate test cases that explore the boundaries of the AUTOSAR specification or that test for specific error conditions.

The use of LLMs for test case generation can help to improve the quality and coverage of AUTOSAR tests. By automating the generation of test cases, LLMs can help to reduce the time and cost of testing, while also improving the accuracy and completeness of the tests.

Here are some of the specific ways that LLMs can be used for test case generation:

  • Understanding the AUTOSAR specification: LLMs can be used to understand the AUTOSAR specification and the behavior of the software system. This can be done by feeding the LLM the AUTOSAR specification and the source code of the software system. The LLM can then use this information to generate test cases that exercise different aspects of the system's functionality.
  • Generating test cases: Once the LLM has understood the AUTOSAR specification and the behavior of the software system, it can generate test cases. This can be done by using the LLM's ability to generate text and code. The LLM can generate test cases that are specific to the system's functionality and that are more comprehensive and exhaustive than those that can be generated manually.
  • Targeting test cases: LLMs can also be used to target test cases to specific parts of the software system. This can be done by using the LLM's ability to understand natural language. The LLM can be given a description of the part of the software system that needs to be tested, and it can then generate test cases that target that specific part of the system.
  • Generating test cases quickly and efficiently: LLMs can generate test cases much more quickly and efficiently than manual methods. This is because LLMs can understand the AUTOSAR specification and the behavior of the software system, and they can generate test cases automatically.

Example 1:

Suppose you are developing an automotive software system that controls the braking system of a car. The AUTOSAR specification for the braking system defines a set of functions that the software must implement. The LLM can be trained on this specification to generate test cases that verify the functionality of the software.

For example, the LLM could be used to generate test cases that verify the following:

  • The software can correctly calculate the braking force for a given set of inputs.
  • The software can correctly apply the braking force to the wheels of the car.
  • The software can correctly handle different braking scenarios, such as emergency braking and gradual braking.

The LLM can generate test cases by using its knowledge of the AUTOSAR specification to create scenarios that exercise the different functions of the software. The LLM can also generate test cases that are designed to detect specific types of errors, such as boundary value errors and invalid input errors.

Example 2:

Here is an example of how an LLM trained in AUTOSAR specifications can be used to generate test cases:

  1. The LLM is first trained on a set of AUTOSAR specifications. This training data can include the AUTOSAR standard itself, as well as examples of test cases that have been written for AUTOSAR-compliant software.
  2. Once the LLM is trained, it can be used to generate test cases for new or existing AUTOSAR-compliant software. The LLM can be given a set of requirements for the software, and it will generate test cases that are designed to verify that the software meets those requirements.
  3. The generated test cases can then be executed on the software to verify that it works as expected. If the test cases fail, this indicates that the software may contain errors. The errors can then be fixed and the test cases can be re-executed to verify that the software is now working correctly.

Here is an example of a test case that could be generated by an LLM trained in AUTOSAR specifications:

Example snippet
Given: A vehicle is driving on a straight road.
When: The vehicle encounters a stop sign.
Then: The vehicle should stop at the stop sign.


This test case ensures that the vehicle can correctly stop at a stop sign. The LLM would have been trained on a set of AUTOSAR specifications that define the behavior of vehicles at stop signs. The LLM would then use this knowledge to generate the test case.

Here are some of the benefits of using LLMs for test case generation for AUTOSAR systems:

  • Increased test coverage: LLMs can be used to generate test cases that cover a wider range of the AUTOSAR specification than can be generated manually. This can help to ensure that all of the features of the specification are tested.
  • Improved test quality: LLMs can be used to generate test cases that are more complex and challenging than those that can be generated manually. This can help to find more bugs and defects in the AUTOSAR system.
  • Reduced test cost: LLMs can automate the process of test case generation, which can save time and money.
  • Increased test speed: LLMs can generate test cases much faster than humans can. This can help to shorten the overall testing cycle.
  • Increased productivity: LLMs can automate the test case generation process, which can save time and resources.
  • Reduced risk: LLMs can help to identify potential software defects early in the development process. This can help to reduce the risk of software defects that could lead to safety hazards.
  • Enhanced innovation: LLMs can be used to explore new design ideas and to generate new test case patterns. This can help to enhance the innovation of automotive software.

Overall, LLMs can be a valuable tool for generating test cases for AUTOSAR systems. They can help to improve the quality, coverage, and speed of testing, which can lead to more reliable and secure automotive software.

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