Dynamic program analysis examines a program while it runs, looking for behavior that signals defects. It can help uncover Linux kernel bugs when tests or fuzzing drive execution through code where a problem occurs—but it only observes the executions that actually happen. Static analysis, by contrast, examines code without running it. The two approaches answer different questions and can complement one another.
What dynamic program analysis means
Dynamic analysis gathers information from a program in execution. In a kernel testing workflow, a runtime analysis tool can monitor selected behavior as the kernel runs and report a condition that may indicate a bug. The result is tied to the run: the program must execute the relevant code, under inputs and conditions that expose the problem.
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That dependence is important. A clean run means no checked problem was observed in that run; it does not establish that every possible execution is correct. Different inputs, schedules, or paths may exercise different behavior.
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Dynamic and static analysis answer different questions
| Question | Dynamic analysis | Static analysis |
|---|---|---|
| Must the program run? | Yes. It observes behavior during execution. | No. It examines code without executing it. |
| What does it depend on? | The inputs, tests, and execution paths that reach the relevant code. | The code being examined and the analysis performed on it. |
| What can a finding show? | A checked runtime condition occurred during an observed run. | A potential issue was identified through analysis of code. |
| What does a clean result mean? | No checked issue was reported in the runs performed; unexercised behavior remains unobserved. | No issue was reported by the analysis used; that alone does not establish correctness. |
This is a practical distinction, not a contest in which one method replaces the other. Runtime tools can provide evidence about actual executions, while static approaches can examine code without needing a test to trigger a path. Each has limits, and a project can use both to seek different kinds of evidence.
Where sanitizers and fuzzing fit
The Linux Foundation’s 2021 event materials connect dynamic analysis with concrete tool families in Dmitry Vyukov’s speaker biography. The listed projects include AddressSanitizer, ThreadSanitizer, MemorySanitizer, similar kernel tools, the Go data race detector, syzkaller/syzbot, go-fuzz, and libFuzzer. The biography lists these projects; it does not establish that every tool was demonstrated in the webinar.
As a workflow concept, runtime checking tools and input-generating testing can work together: tests or fuzzers produce executions, while runtime analysis can check for selected defect signals in those executions. A finding points to behavior observed under the run that exposed it. The event description gives a high-level overview rather than a tool-by-tool explanation, so it does not specify exact configurations, commands, or defect coverage for these tools.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the Linux Foundation webinar covered
The Linux Foundation records Dynamic Program Analysis For Fun And Profit as a webinar held on February 25, 2021, presented by Dmitry Vyukov, whom the event page identified at the time as a Principal Software Engineer at Google. The stated scope was an introduction to dynamic analysis, its strengths and weaknesses, a contrast with static analysis, and practical discussion of Linux kernel tools and bug classes. The format was 45 minutes of overview followed by 45 minutes of Q&A.
The event page says the listed tools “allowed to discover and fix more than 3000 bugs in the Linux kernel.” That is an aggregate claim attributed to the tool set on the event page, not a per-tool count; the page does not provide measurement methodology. The webinar page also provides a slide download and recording information: Linux Foundation webinar page and Linux Foundation event page.
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