Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Eric Brüggemann, the new CEO of Code Intelligence, stepped into the role in September. We asked him about his vision of the role and future of AI and fuzz testing in securing automotive software.

Out-of-bounds memory access, also known as buffer overflow, occurs when a program tries to read from or write to a memory location outside the bounds of the memory buffer that has been allocated for it. This type of vulnerability is particularly dangerous because it can lead to various issues, including crashes, data corruption, sensitive data leaks, and even the execution of malicious code.

Embedded software development presents unique challenges due to its close integration with hardware, strict real-time requirements, and the need for high reliability and safety. The V-Model, also known as the Verification and Validation model, offers a structured approach that effectively addresses these challenges. This blog post delves into the V-Model's intricacies and elucidates how it enhances the testing of embedded software.

One of the most effective security testing methods for embedded systems is fuzz testing. It’s the fastest way to identify memory corruption errors and their root cause. It enables a shift-left testing approach, recommended by many industry standards, and reaches up to 100% code coverage. Read on for the details.

The United States Food and Drug Administration (FDA) is a federal agency within the Department of Health and Human Services. The FDA is responsible for protecting and promoting public health through the control and supervision of medications, vaccines, biopharmaceuticals, medical devices, and other types of products. To ensure the safety and security of medical devices, the FDA supports a variety of standards and guidelines that medical device manufacturers are highly recommended to follow.

Software-in-the-loop (SiL) testing is a pivotal method in the software development lifecycle, especially for embedded systems and critical applications. By simulating real-world conditions and integrating software components within a controlled virtual environment, SiL allows for the early detection of bugs, ensuring higher code quality and reliability. Read on to learn how to introduce SiL testing in your project.

With AI finding adoption throughout all stages of the development process, the SDLC as we know it is becoming a thing of the past. Naturally, this has many implications for the field of software testing. This article will discuss how the SDLC has evolved over time, going into detail on the impact that AI adoption is having on both software development and software testing.

As AI integrates into every stage of the SDLC, the area of software testing is undergoing transformative and unprecedented changes. In this article, we will discuss the ethical considerations for AI-powered software testing, examining the advantages and potential hurdles generative AI presents as a new technology being applied across the SDLC.

Static code analysis is widely adopted among organizations for its ability to provide fast feedback loops and identify bugs early in development. However, despite its advantages, numerous bugs and vulnerabilities remain undetected and are only found when they've made their way into production or been caught by late-stage penetration testing. The best security practice involves leveraging both static and dynamic testing, such as fuzz testing.