Files
msime-windows/server/tests
fanlusky ccbaa3a631 fix(engine): 送进 Google 解码器前把 ü 换成 nue/lue/ju 写法
小鹤双拼 ntdddswu 出的是「女蛾黛动物」而不是「虐待动物」:nt 转成全拼后
是 nve(shuangpin_pinyin_set 把 nue 抹掉了,词库与词格那一侧统一用 v 形),
而本地 googlepinyinime-rev 的拼写表和 inputtools 云输入都不认 nve/lve/jv,
只有 nue/lue/ju/jue,也不做 v→ü 的转写。带分隔符的串照样送过去了,解码器
却把 nve 重新切成 nv + e,于是有了「女蛾」。

加一个 quanpin::to_google_spelling,按音节整体改写 nve→nue、lve→lue、
jv→ju 这一类拼写,只在两个 Google 解码器的边界上用:云输入的 query_text、
双拼与全拼的整句回退。词库查询和词格用的仍然是 canonical_syllable 那一侧的
v 形写法,没有动。nv'er、lv'se 这些 Google 也认的写法原样通过。
2026-09-19 12:29:01 +08:00
..
2026-06-20 22:11:00 +08:00
2025-04-20 17:44:33 +08:00
2025-04-21 18:32:45 +08:00

Tests

This project now includes a lightweight regression test target wired into the main CMake build.

What It Covers

Current tests focus on high-value IME behaviors that are easy to accidentally break during refactors:

  • shuangpin scheme request construction
  • quanpin scheme request construction
  • case-preserving shuangpin segmentation
  • full-helpcode detection
  • engine + shuangpin continuation behavior
  • cloud suggestion trigger timing for shuangpin

The goal is not to fully simulate the whole IME UI pipeline yet. The goal is to protect the core input rules and session behavior.

Build and run

Run these commands from the MSIME-Windows repository root after preparing vcpkg and Boost with python server/scripts/prepare_env.py:

python scripts/product_lock.py fetch-dictionaries --staging-root .
if ($LASTEXITCODE -ne 0) { throw 'Dictionary verification failed' }
$env:LOCALAPPDATA = Join-Path $PWD 'build/test-data/user-local'
$env:METASEQUOIA_IME_DATA_DIR = Join-Path $env:LOCALAPPDATA 'metasequoiaime'
New-Item -ItemType Directory -Force $env:METASEQUOIA_IME_DATA_DIR | Out-Null
Copy-Item MetasequoiaImeDict/out/* $env:METASEQUOIA_IME_DATA_DIR -Force
Copy-Item engine/helpcode/helpcodes $env:METASEQUOIA_IME_DATA_DIR -Recurse -Force
Copy-Item server/assets/tables/* $env:METASEQUOIA_IME_DATA_DIR -Force
Copy-Item server/assets/config/config.toml $env:METASEQUOIA_IME_DATA_DIR -Force
.\server\tests\scripts\llaunch.ps1 -Configuration Release

Use a dedicated PowerShell terminal: these environment variables isolate both Windows helpers and the shared Engine from the installed user's data for the rest of that session. The scripts run CTest; they do not launch the interactive Server, install or register the input method. CI additionally runs the native Win32/x64 pipe probe after building the full Server.

Tests belong to the Server CMake project, which provides the Engine, GUI and third-party targets. server/tests/CMakeLists.txt is an add_subdirectory input, not an independent project. The obsolete test presets have been removed; an old locally generated server/tests/CMakePresets.json is no longer used.

Once test data is prepared, the convenience scripts work from any directory:

.\server\tests\scripts\llaunch.ps1 -Configuration Release  # build, then CTest
.\server\tests\scripts\lrun.ps1 -Configuration Release     # CTest only

Omitting -Configuration selects Debug in server/build; Release uses server/build-release. Both compile MetasequoiaImeServerTests and test_webview_contract in the parent build tree. Configuration, compilation, test failures and an empty CTest registry return failure rather than launching a stale executable or reporting success.

The equivalent direct CTest command from the repository root is:

ctest --test-dir server/build-release -C Release --output-on-failure --no-tests=error

Test Layout

  • tests/src/main.cpp
    • tiny custom test runner
  • tests/includes/test_framework.h
    • lightweight assertion helpers
  • tests/src/test_shuangpin_scheme.cpp
    • shuangpin scheme unit tests
  • tests/src/test_quanpin_scheme.cpp
    • quanpin scheme unit tests
  • tests/src/test_shuangpin_query.cpp
    • query/helper function tests
  • tests/src/test_engine_shuangpin_session.cpp
    • session-level behavior tests

When you change input behavior, add a regression test if the change affects:

  • input segmentation
  • space/enter/backspace behavior
  • helpcode handling
  • continuation after candidate selection
  • create-word flow
  • cloud suggestion timing

The best tests in this project are the ones that capture a concrete input example that previously broke.