Dynamic program analysis examines software while it is running. Instead of inferring every possible behavior from source code, it instruments an actual execution and reports failures that occur on that path. The Linux Foundation’s “Mentorship Session: Dynamic Analysis for Fun and Profit,” presented by Google principal software engineer Dmitry Vyukov on February 25, 2021, used Linux-kernel tools to show how runtime checking, sanitizers, race detectors and fuzzers expose bugs that ordinary testing can miss.
What the LF Mentorship session covered
The session was part of the Linux Foundation’s LF Live: Mentorship Series, a virtual program in which maintainers and community leaders share practical techniques for Linux-kernel and other operating-system projects. Its focus was the difference between dynamic and static analysis, followed by concrete runtime tools used with the Linux kernel.
The event description identified AddressSanitizer, ThreadSanitizer, MemorySanitizer, related kernel sanitizers, Go’s data-race detector, and fuzzers such as syzkaller/syzbot, go-fuzz and libFuzzer. The central idea is complementary use: dynamic tools find failures that a test actually triggers, while static tools reason about source code without executing it.
Dynamic versus static analysis
| Question | Dynamic analysis | Static analysis |
|---|---|---|
| What it observes | Instrumented code during an execution | Source code, intermediate representation or binaries without running the program |
| Coverage | Only paths reached by the workload, tests or fuzzer | Can examine code paths that no test reaches, subject to the analyzer’s model |
| Typical finding | A concrete out-of-bounds access, use-after-free, race or invariant violation with execution context | A warning that a defect may be possible and needs review |
| False-positive burden | Usually low for a correctly diagnosed runtime failure because the event occurred; reports still depend on instrumentation and tool limitations | Often higher because warnings describe potential behavior rather than an observed failure |
| Test-generation requirement | Needs tests, production traces or fuzzing inputs that reach the faulty path | Does not require an input to execute the path |
| Overhead | Can be substantial because checks and metadata run with the program | No runtime cost during the analyzed program’s execution |
Dynamic analysis therefore trades breadth of unexecuted code for certainty about an executed failure. Static analysis remains valuable for finding suspicious paths that no current test exercises; dynamic analysis supplies a reproducible crash, race or corrupted invariant when a workload reaches one.
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A simple runtime failure: an out-of-bounds access
What happens without instrumentation
Consider code that allocates an array of four elements and then writes element five. The write may corrupt adjacent data, appear to work by accident, or crash much later. A normal test may pass if it never reaches that statement, and a crash elsewhere may obscure the original cause.
What a dynamic detector adds
A memory-safety sanitizer places checking metadata around allocations and instruments accesses. When the invalid write executes, it can stop at the offending instruction and report the access, allocation history and stack traces. The result is tied to a real execution rather than a hypothetical path.
Linux-kernel runtime checks
CONFIG_DEBUG_LIST
CONFIG_DEBUG_LIST checks invariants of linked-list operations in the kernel. If a list link is corrupted or an operation violates the expected structure, the check can identify the failure near the operation that damaged the list instead of allowing silent corruption to spread.
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KASAN
KASAN (Kernel Address SANitizer) detects out-of-bounds and use-after-free accesses in heap, stack and global memory. These classes of bugs are especially difficult to diagnose after corruption has propagated; KASAN’s runtime report associates the invalid access with allocation, deallocation and access stacks when available.
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KASAN is a diagnostic configuration, not a free production-hardening switch. A contemporaneous 2021 summary by Desmond Cheong characterized its cost as approximately twice the slowdown and twice the memory overhead. That figure is historical and approximate: actual overhead varies with kernel version, architecture, configuration and workload.
Sanitizers, race detectors and fuzzers
AddressSanitizer and kernel address sanitizers
AddressSanitizer targets memory-safety errors such as out-of-bounds accesses and use-after-free. Kernel adaptations apply the same runtime-observation principle to kernel memory, with KASAN being the named example in the session materials.
ThreadSanitizer and Go’s race detector
ThreadSanitizer and Go’s data-race detector look for conflicting memory accesses from concurrent execution when synchronization does not establish a safe ordering. They require an execution in which the relevant threads and accesses occur; a race in an unexecuted path remains undiscovered.
MemorySanitizer
MemorySanitizer focuses on uses of uninitialized memory. Its reports are useful when a value reaches a computation, branch or API before being initialized, provided the instrumented workload exercises that flow.
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Fuzzers
Fuzzers improve dynamic-analysis coverage by generating or mutating inputs. The session named syzkaller/syzbot for kernel fuzzing, go-fuzz for Go code and libFuzzer for in-process fuzzing. A fuzzer’s value depends on how effectively it reaches new states and on the detector attached to the run: fuzzing can supply the input, while a sanitizer explains the memory error, race or invariant violation.
What the reported bug totals mean
The Linux Foundation event page attributed the named sanitizers, kernel tools, race detector and fuzzers with discovering and fixing more than 3,000 Linux-kernel bugs. That is a historical statement describing Dmitry Vyukov’s work as presented in the 2021 session, not a current annual total or a universal benchmark for every project.
Desmond Cheong’s contemporaneous notes credited KASAN with catching about 1,000 bugs in the preceding few years. This is an author summary from that period, not a guarantee of present-day KASAN yield. Counts depend on which kernel versions, configurations, workloads and deduplication rules are included.
Do dynamic-analysis reports have false positives?
A report showing an actual invalid access, race or violated invariant is generally more actionable than a static warning because the failure occurred during the recorded execution. Cheong summarized the practical benefit as: “complex bugs now become possible to analyze, and all bug reports are true positives because the bug actually happened.”
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That does not mean every report is automatically easy to fix. Developers still need to determine whether the detected behavior is reachable in a supported configuration, reduce a large fuzzer input to a reproducer, and separate one underlying defect from repeated manifestations. Instrumentation bugs, unsupported patterns and incomplete synchronization models can also affect interpretation, so the stack trace and reproducer must be reviewed.
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- Use static analysis first for breadth. Review source-level warnings to identify suspicious paths, missing checks and unsafe interfaces, including code that tests do not currently reach.
- Run targeted dynamic configurations. Build and exercise the relevant kernel or component with the sanitizer or invariant checker suited to the suspected bug class.
- Add concurrency coverage where needed. Use ThreadSanitizer or a language-specific race detector with workloads that create the contested interleavings.
- Feed fuzzers meaningful entry points. syzkaller/syzbot, go-fuzz and libFuzzer can generate the varied inputs and call sequences required to reach deep code.
- Preserve the execution evidence. Keep the report, stack traces, triggering input and configuration so the defect can be reproduced and fixed.
- Retest without assuming universal coverage. A clean run means the exercised paths did not fail under that configuration; it does not prove that unexecuted paths are safe.
Choosing a tool by bug class
| Suspected problem | Useful dynamic method | What it needs | Expected evidence |
|---|---|---|---|
| Heap, stack or global memory bounds; use-after-free | AddressSanitizer or KASAN | An execution that performs the invalid access | Access, allocation and often deallocation stack traces |
| Corrupted linked-list structure | CONFIG_DEBUG_LIST | An operation that encounters the broken invariant | Failure near the list check, with kernel context |
| Data race | ThreadSanitizer or Go’s race detector | Concurrent execution of the conflicting accesses | Conflicting access stacks and synchronization context |
| Uninitialized-memory use | MemorySanitizer | A path carrying the uninitialized value into an instrumented use | Origin and use information, subject to instrumentation coverage |
| Unknown or hard-to-reach input-triggered defects | Fuzzer paired with an appropriate sanitizer | Effective input mutation or generation and a reachable entry point | A minimizing input plus the detector’s runtime report |
Bottom line for kernel developers
Dynamic analysis is most valuable when you need a concrete failure: an access that happened, a race observed under concurrency, or a kernel invariant that was actually broken. Sanitizers and fuzzers make those failures diagnosable, but their coverage is bounded by the executions you provide and their instrumentation has real cost. Keep static analysis in the workflow for broader source-level reach, then use targeted runtime checking and fuzzing to turn the most important suspected defects into reproducible evidence.
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