测试设计、漏洞检测、质量保证（1.3万 个）
Turn saved regression decisions into evidence-linked HTML, Markdown, and JSON reports. Validate records and coverage, preserve assigned ratings, and distinguish confirmed defects from existing bugs, rejected claims, and unresolved work.
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Handle one known local bug or testable bug hypothesis through reproduction, independent validation, repair, and regression checks. Produce a local patch commit and evidence reports that help reviewers judge whether the fix is ready.
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Build and run systems before and after a change, then execute the same reproduction procedure against both. Save environment details, inputs, commands, and separate logs to document observed behavior and any setup blockers.
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Review a specific code change and trace its callers to identify behavior that may have broken. Record suspected regressions, reachable triggers, evidence needs, and coverage gaps so a separate reproduction step can investigate them.
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Prepare a regression investigation by fixing the exact before and after source versions, preserving PR or commit context, and producing a sealed preparation record that explains change boundaries and evidence collection limits.
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Independently rerun an unchanged bug reproduction package before repair. Check whether compiler diagnostics or runtime assertions support the exact reported claim, then preserve execution evidence and record a confirmed, unconfirmed, or inconclusive judgment.
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Independently rerun supplied proof-of-concept code against freshly built versions from before and after a change. Compare observed behavior, verify reachability through real interfaces, and preserve separate logs showing agreement, divergence, or blockers.
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Independently reproduce a reported runtime or compiler bug in a local repository. Build the baseline, exercise the actual artifact or compiler, and save logs and structured records distinguishing reproduction, absence of the behavior, and setup blockers.
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Coordinate investigations into regressions introduced by PRs or commits. Assign preparation, discovery, reproduction, independent verification, decisions, and reporting to separate stages, while preserving evidence and progress records for later resumption.
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Create dictionaries of keywords and identifying byte sequences for fuzzing, or testing with generated inputs. Extract useful entries from code, binaries, or specifications, then configure fuzzers to explore parsing paths beyond initial input validation.
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Audit C/C++ and Rust code for secrets left in memory. It checks missing or incomplete wipes, examines compiler output for removed cleanup operations, and produces reports with evidence and suggested fixes.
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Write and review YARA-X rules, which identify malware through file patterns. Refine detection strings and conditions, check rule quality with included scripts, test against clean files, improve performance, and migrate rules from legacy YARA.
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Measure which code fuzzing reaches as it tries many generated inputs. Use coverage reports to find unreachable areas, compare progress, and guide improvements to test harnesses, starting inputs, or dictionaries of useful input values.
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Compares code relationships before and after a branch, pull request, or release change using Trailmark. Flags structural security regressions, such as removed authorization checks or newly reachable sensitive operations, and provides evidence for reviewers.
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Guides fuzz testing for Ruby libraries and gems with Ruzzy, using varied inputs to find failures. Covers pure Ruby and C extensions, including test harnesses, memory error detection, environment setup, and crash reproduction.
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Assess a single suspected security issue using Trailmark's graph of code relationships. Gather evidence about reachable paths, input flow, and potential impact, then receive a priority verdict and a list of remaining manual checks.
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Checks cryptographic code against known attacks and edge cases using Project Wycheproof test vectors, predefined inputs and expected results. Helps load cases, interpret failures, compare implementations, and integrate checks into automated testing.
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Test cryptographic code for timing side channels, where execution time can reveal secrets. Use dudect to detect statistical differences and Timecop with Valgrind to trace secret-dependent operations, then interpret results and retest changes.
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Set up Atheris to test Python code and C extensions with automatically varied inputs. It guides test harness creation, coverage tracking to explore more code, and AddressSanitizer setup for finding memory errors.
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Turn a Mermaid sequence diagram of a cryptographic protocol into a ProVerif model, a file for formal security checks. It includes queries for secrecy, authentication, replay resistance, and forward secrecy where applicable.
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Evaluate how well existing tests catch code changes, then combine the results with function relationships. Get a report separating harmless findings, missing unit tests, and targets for fuzzing, which tests many generated inputs.
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Generates cryptographic test vectors, inputs with expected outcomes, by deliberately changing implementation code and finding changes existing tests miss. Verifies new vectors across independent implementations and compares detection rates before and after adding them.
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Sets up AFL++ to test C/C++ programs with varied generated inputs, a technique called fuzzing. Covers running tests across CPU cores, monitoring progress, reducing input collections, and reproducing crashes for investigation.
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Uses a known or suspected vulnerability to find related code through Trailmark's graph of calls, types, and implementations. Produces ranked review targets with reasons and limitations for follow-up security analysis.
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