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Testing Classic AUTOSAR applications has long been a significant challenge due to the reliance on hardware-in-the-loop (HiL) setups, which are costly, complex, and hard to scale. In this free webinar, Khaled Yakdan explains how a Tier-1 automotive supplier implemented Code Intelligence’s AUTOSAR simulator and enabled Software-in-the-Loop testing. Dr. Khaled Yakdan, Chief Product Officer, of Code Intelligence, also explains how this approach helps catch more critical bugs that can be accessed externally, speeds up security testing, and reduces hardware dependency.

In previous videos, you've seen that LLM can generate fuzz tests. But what if AI fails to produce a working test or to cover specific workflows that are unavailable as unit tests or usage examples in the code base? You can prompt AI to make changes. Here is how the "Interactive mode" works in CI Fuzz.

After generating fuzz tests with LLMs, the next important step is verifying that these tests are of high quality and ensuring they run and work as intended. CI Fuzz can now automatically build the generated fuzz test, run it, and perform a health check to assess its quality and refine it further if it doesn't pass the health check. Watch the video to see it in action.

Creating high-quality fuzz tests is essential for efficient fuzz testing. However, crafting these tests is a time-consuming, manual process, which has become a major barrier to the widespread adoption of fuzz testing. Watch the video to see how CI Fuzz can automatically generate high-quality fuzz tests by leveraging LLMs and static analysis.

The first step to start fuzzing is to identify what part of the software you want to fuzz. You definitely want to fuzz the most critical functions/APIs—those that exercise a significant amount of code and trigger key functionalities. However, manually identifying these targets can be time-consuming and challenging. Watch the video to see how CI Fuzz can automatically prioritize functions for fuzzing.