Hardware-independent automotive software testing
Automate your software testing with an AI-driven fuzzing platform for early bug and vulnerability detection, all without hardware dependencies. Enable your developers to reproduce and fix issues in minutes, not weeks.
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1. Static Analysis (SAST) generates many false positives.
It reports issues that aren’t actually a problem and produces duplicates.
2. Static Analysis can’t detect all types of vulnerabilities.
Because it doesn’t analyze the program during execution, SAST can’t detect dynamic or runtime-specific issues such as complex buffer overflows, use-after-free, double-free errors, heap corruption, and others.
Learn more about its limitation and how to overcome them with fuzz testing by downloading a free copy of the white paper.
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Code Intelligence connects to the source code and, unlike static analysis (SAST), analyzes code when executed.
Every flagged issue represents an actual issue in the running code. Most are highly critical, e.g. buffer overflows, memory corruption, and leaks.
All uncovered issues are pinpointed to the exact line of code in the repository and accompanied by inputs that triggered an issue and clear actions to remediate those. So you can quickly identify the root cause, start fixing them, and release features faster.
CI Spark, a built-in AI assistant that leverages large language models (LLMs) and static code analysis, automatically writes thousands of test cases and generates inputs and mocks for all dependencies.
CI Spark is also helpful in identifying top candidates for fuzzing.
Code Intelligence leverages feedback about the software under test to achieve the highest code coverage. Subsequent executions automatically generate new test cases to detect additional paths, thereby increasing code coverage.
This ensures your development teams know how much of their code was actually executed during a test and which parts need additional testing.
Integrate Code Intelligence with your CI/CD pipeline to automatically test your software with every pull request. This ensures regressions and release blockers are identified long before reaching production.
