Cut through the noise with a promise for fewer false positives

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Save time and build trust in your analysis

Tired of chasing false positives? Traditional code analysis wastes time and erodes trust. TrustInSoft eliminates the noise. Our advanced static analysis delivers unparalleled accuracy, ensuring you address real vulnerabilities and build confidence in your code. Stop wasting time on false alarms and proactively mitigate risks with TrustInSoft.

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The Cost of False Positives

Developer and tester time is precious and limited. Employing efficient and accurate exhaustive static analysis brings speed and precision to the testing process. Guarantee the bugs found by your tool are true potential vulnerabilities.

Accuracy You Can Trust

Increased Efficiency: Spend less time triaging and more time fixing real bugs. Improved Developer Trust: Build confidence in the analysis results. Reduced Risk: Ensure you're addressing genuine vulnerabilities, minimizing the risk of security breaches. Faster Remediation: Streamline your workflow and prioritize critical issues.

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Secures even the most critical applications

Eliminate undefined behavior and memory safety vulnerabilities that put embedded code at risk of crash and unexpected outcomes. Integrating directly into your existing CI/CD pipeline, you can benefit from mathematical proof of software safety and security thanks to the power of formal methods.

What is a false positive in code analysis?

A false positive in code analysis is a warning from a static analysis tool that flags a potential problem in code, but it's actually harmless. Like a smoke alarm going off for cooking, it's a false alarm. They happen because tools over-approximate code behavior, lack full context, struggle with complex code, or have their own limitations. False positives waste developer time, reduce trust in the analysis, cause alert fatigue, and can even hide real security risks.

How does TrustInSoft deliver fewer false positives than other tools?

Unlike traditional static analyzers that rely on pattern matching and approximations, TrustInSoft mathematically models all possible program behaviors with abstract interpretation. This rigorous approach provides precise results, eliminating the over-approximations that lead to false positives. By guaranteeing the absence of certain bug classes, TrustInSoft delivers accurate and reliable analysis, focusing developer attention on genuine vulnerabilities.

Why do traditional static analysis tools produce false positives?

Many traditional tools analyze code without executing it, creating an abstract model. This abstraction, while necessary for speed, often leads to over-approximation. The tool might flag a potential issue that could only occur under specific conditions that are never actually met in practice. They also often lack complete context. For example, they might not understand runtime checks or developer assumptions, leading to incorrect inferences. Finally, complex code with many interactions makes accurate analysis difficult, increasing the chance of misinterpretations and false alarms.

What is the impact of false positives on development teams?

They waste valuable time investigating phantom bugs, eroding trust in analysis tools. This leads to alert fatigue, where real vulnerabilities are missed, increasing security risks. False positives also inflate development costs and can delay releases. In short, they hurt productivity, security, and morale.

What is alert fatigue, and how is it related to false positives?

Alert fatigue is the desensitization to a high volume of alerts, causing individuals to ignore or delay responses. False positives, which are alerts indicating a problem when none exists, significantly contribute to this fatigue by overwhelming responders with irrelevant notifications. Consequently, alert fatigue increases the risk of overlooking genuine and critical alerts.

How can I reduce false positives in my code analysis?

Using exhaustive static analysis with TrustInSoft Analyzer, you can explore all possible input paths for a more accurate analysis with fewer false positives than traditional static analysis tools alone can provide.