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Fuzz testing: research existing KMP/Kotlin ecosystem tooling

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难度
4/5
预计耗时
3-5 天
新手友好度
42/100
Issue 类型
文档
描述清晰度
描述清楚
活跃度
冷清
技术栈
java, kotlin, machine-learning
领域
ai, developer-experience, machine-learning, testing, testing-qa, tooling

调研方向

This is research for parent #981, not an implementation ticket. Start from the listed candidates (Jazzer, Jazzer.js, kotest property testing, JUnit/kotlin.test generators) and check which run on KMP source sets versus jvmTest only. Note how each reports coverage (JaCoCo-style, Gradle/CI wiring) and how other Kotlin/JVM OSS projects time-box fuzz jobs, persist corpora, and report crashes. Done is a short comparison comment or doc page so #981 can pick tools; do not implement fuzzing here. Companion #982 can run in parallel.

由索引模型根据 Issue 内容生成。

描述

enhancement research sub-issue

Sub-issue of #981 ("Enhance testing by adding fuzzing") — the R: RESEARCH phase for that
proposal. Survey what already exists in the Kotlin/KMP ecosystem before designing anything custom.

Goal

Answer "what can we reuse" before #981 gets to a design/implementation plan.

Tasks

  • Fuzzing engines/harnesses for Kotlin/JVM: Jazzer
    (JVM libFuzzer via coverage-guided bytecode instrumentation, the most mature option),
    Jazzer.js if a JS/Wasm-side target
    ever makes sense, kotlinx property-based testing options
    (kotest property testing),
    and plain JUnit/kotlin.test + custom generators as a baseline comparison.
  • KMP-specific constraints: which of the above actually work across KMP source sets vs.
    JVM-only — most fuzzing engines are JVM-bytecode-based (Jazzer) or Node-based (Jazzer.js),
    so figure out realistic target-set scope (likely jvmTest only, at least initially) rather
    than assuming multiplatform fuzzing is feasible.
  • Coverage measurement: how each candidate reports/measures coverage (Jazzer integrates
    with JaCoCo-style coverage; check what's practical to wire into this repo's existing
    Gradle/CI setup), and whether coverage-guided fuzzing (vs. blind random generation) is
    achievable here.
  • CI integration patterns: how other Kotlin/JVM OSS projects run fuzzing in CI — as a
    time-boxed job (e.g. N minutes/seeds per run, not exhaustive), corpus persistence between
    runs (cached seed corpus so regressions aren't re-discovered from scratch), and how crashes
    get reported/triaged (e.g. auto-filed issues vs. failing the build).
  • Reporting/eval: what output format(s) these tools produce (crash reproducers, minimized
    inputs, coverage deltas) and whether any of it is consumable by this repo's existing docs/
    Antora pipeline or needs a bespoke summary step.
  • OSS-Fuzz feasibility: whether SKaiNET could realistically be onboarded to
    OSS-Fuzz (free continuous fuzzing for open-source
    projects) given it already has Jazzer-based JVM project examples, as a longer-term option
    beyond local/CI-only fuzzing.

Output

A short comparison write-up (as a comment on this issue, or a doc page) — not a recommendation
that's already implemented, just enough for #981 to make an informed tool choice.

Related

  • Parent: #981
  • Companion sub-issue (repo-state analysis, can run in parallel): #982
主要语言
Kotlin
星标
52
派生
15
平均合并
1 天 15 小时
30 天内合并 PR
36

环境准备

  • 没有 Dockerfile 或 Docker Compose 文件
  • 没有 Pull Request 模板
  • 阅读贡献指南

从这里开始

  1. 先读完整个 Issue，再读项目的贡献指南。
  2. 在 Issue 下留言说明你要接手 —— 这能避免两个人做同样的事。
  3. Fork 仓库，在一个分支上完成修改。
  4. 提交 Pull Request，并在描述里引用这个 Issue 编号。

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