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Fuzz testing: research existing KMP/Kotlin ecosystem tooling

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#983 コメント 2 件 リアクション 0 件 担当者 0 名 GitHub で見る

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まだ誰も着手していません。

評価

難易度
4/5
見積もり時間
3〜5日
初心者へのやさしさ
42/100
issue の種類
ドキュメント
明瞭さ
明確に書かれている
活発さ
静か
技術スタック
java, kotlin, machine-learning
領域
ai, developer-experience, machine-learning, testing, testing-qa, tooling

調査の方向性

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.

索引モデルが issue の本文から書いたものです。

説明

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
主要言語
Kotlin
スター
52
フォーク
15
平均マージ
1日 15時間
マージ済み PR（30日）
36

環境構築

はじめの一歩

  1. issue を最後まで読み、次にプロジェクトのコントリビューションガイドを読みます。
  2. 着手することを issue にコメントします — 二人が同じ作業をするのを防げます。
  3. リポジトリをフォークし、ブランチを切って変更します。
  4. issue 番号を参照したプルリクエストを送ります。

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