Implemented qt port + e2e
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Martino Ferrari
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# Streaming-Chain E2E Test Suite — Design
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**Date:** 2026-06-25
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**Status:** Approved (autonomous implementation authorised)
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**Sub-project:** A (StreamHub full-chain waveform E2E)
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## Goal
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A curated end-to-end test suite that validates the streaming chain
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**MARTe2 App (`UDPStreamer`) → `StreamHub` → client** with *waveform* validation,
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across a covering matrix of `UDPStreamer` configuration options, plus
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StreamHub↔client behavioural tests (live stream, zoom in/out, time window, all
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trigger modes). Output: one combined PDF report **and** a machine-readable
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`results.json`.
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## Decisions (from brainstorming)
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1. **Matrix strategy** — curated "covering" set (~40–70 scenarios): every option
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value appears at least once, plus deliberately chosen high-risk interactions.
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2. **Oracle** — hybrid, applied per case:
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- **Analytic (A)**: clean cases reconstruct expected values from the
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generator formula on the received timestamps; tolerance
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`max|err| ≤ quant_step/2 + eps`; decimated streams assert only the samples
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that should survive; LTTB-decimated views use a shape metric (RMSE /
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correlation), not point equality.
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- **Fed-reference (B)**: subtle modes (Accumulate, FullArray, First/LastSample)
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compare received-vs-fed using a `FileWriter` tap of the exact signals MARTe
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fed into the `UDPStreamer`.
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3. **Client testing** — Go mock client (grown from `Test/E2E/streamhub/main.go`)
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is the authoritative functional/waveform gate; plus a thin Qt `QTest` GUI
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smoke test (offscreen) for GUI-level confidence.
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4. **"to disk to client"** — the mock client persists the stream it received to
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disk as a report artifact for offline comparison/plots.
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5. **Output** — one orchestrator + one combined PDF **+** `results.json`.
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## Architecture
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New directory `Test/E2E/chain/`. A shell entry point delegates the matrix to a
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Python driver, matching the existing `Test/E2E/datasources/` and
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`Test/E2E/recorder/` conventions (shell + Python + Typst). Artifacts go to
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`Build/x86-linux/E2E/chain/` (never the source tree).
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### Two-process stack (per scenario)
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```
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gen_data.py -> /tmp/chain_e2e/input_<id>.bin (ground truth + fed reference)
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gen_cfg.py -> marte_<id>.cfg (LinuxTimer + FileReader -> IOGAM -> UDPStreamer
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[+ optional FileWriter tap])
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-> hub_<id>.cfg (StreamHub Source pointing at the UDPStreamer)
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StreamHub.ex -cfg hub_<id>.cfg [process 1, started first]
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MARTeApp.ex -l RealTimeLoader -f marte_<id>.cfg -s Running [process 2]
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chain-client (Go) -hub 127.0.0.1:<wsport> -scenario <id> -out <dir>
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-> records live pushes, runs zoom/window/trigger checks,
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dumps received_<id>.bin + checks_<id>.json
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validate_waveform.py input_<id>.bin received_<id>.bin [+ tap_<id>.bin]
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-> metrics_<id>.json (+ overlay/diff plots)
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```
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### Components & responsibilities
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| File | Responsibility |
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|------|----------------|
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| `Test/E2E/chain/scenarios.py` | Declarative list of ~50 scenario dicts (the covering set) + validity rules |
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| `Test/E2E/chain/gen_data.py` | scenario → deterministic `input_<id>.bin` for all 10 MARTe2 types & shapes; knows the analytic formula per signal |
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| `Test/E2E/chain/gen_cfg.py` | scenario → MARTe2 app `.cfg` and StreamHub `.cfg` (unicast/multicast, Strict/Accumulate/Decimate, per-signal type/shape/timemode/quant, optional FileWriter tap) |
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| `Test/E2E/chain/client/` | Go mock client (grown from `streamhub/main.go`): record live, zoom, window, all trigger modes; dump `received_<id>.bin` + `checks_<id>.json` |
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| `Test/E2E/chain/validate_waveform.py` | ground-truth/fed-reference vs received; per-effect tolerance; emit `metrics_<id>.json` |
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| `Test/E2E/chain/plots.py` | matplotlib overlays (truth/received/diff), trigger-capture, zoom/window figures |
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| `Test/E2E/chain/run_chain_e2e.sh` | orchestrator: build, loop scenarios over the two-process stack, validate, aggregate `results.json`, compile PDF |
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| `Test/E2E/chain/E2E_Report.typ` | Typst report template → `E2E_Report.pdf` |
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| `Client/streamhub-qt/test/` | Qt `QTest` smoke (offscreen), run via ctest |
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## Covering matrix (~50 scenarios)
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Grouped so each option value is hit at least once, with targeted interactions.
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Config-surface reference (verified against source):
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- **Publishing modes**: `Strict`, `Accumulate` (+`MinRefreshRate`), `Decimate` (+`Ratio`).
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- **Network**: unicast (`Port`); multicast (`MulticastGroup` + `DataPort`).
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- **Signal types**: uint8/int8/uint16/int16/uint32/int32/uint64/int64/float32/float64.
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- **Time modes** (per signal): `PacketTime`, `FullArray`, `FirstSample`,
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`LastSample` (+ `TimeSignal`, `SamplingRate`).
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- **Quantisation** (float only): `none`, `uint8`, `int8`, `uint16`, `int16`
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(+`RangeMin`/`RangeMax`).
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- **Shapes**: scalar, 100-element array, 5000-element array (fragmentation).
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- **Payload**: default 1400, small (512, forces fragmentation), jumbo (65507).
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- **Sources**: single and multiple sources (two UDPStreamer blocks → two hub sources).
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Representative buckets (illustrative, ~50 total):
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- Per-type scalar Strict unicast (10) — type fidelity.
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- Time-mode sweep on arrays (FullArray/FirstSample/LastSample/PacketTime × float32/uint64-time) (~8).
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- Quantisation sweep (none/uint8/int8/uint16/int16 with matching RangeMin/Max) (~6).
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- Publishing modes (Strict/Accumulate@{10,100,1000Hz}/Decimate@{1,5,10}) (~8).
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- Multicast variants of the above (~6).
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- Shapes/fragmentation (scalar/100/5000 × payload 512/1400/65507) (~6).
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- Multi-source (2–3 sources, mixed configs) (~3).
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- Edge/robustness (mixed-type multi-signal source, large array + quant) (~3).
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## Oracle details
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Ground truth is the analytic generator. For a received sample at time `t` for
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signal `s`, the expected value is `f_s(t)` reconstructed from the generator's
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per-signal formula and the row/cycle clock.
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- **Type fidelity (no quant)**: integer/float types must be bit-exact after the
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type round-trip (`err == 0`).
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- **Quantised float**: `quant_step = (RangeMax-RangeMin)/levels` (levels = 255,
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254, 65535, 65534 for uint8/int8/uint16/int16). Require
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`max|recv − truth| ≤ quant_step/2 + 1e-6·range`.
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- **Decimate (Ratio R)**: only every R-th producer cycle is transmitted; assert
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the surviving subsequence matches truth; received count ≈ produced/R.
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- **Accumulate**: batched samples must reconstruct the original per-cycle
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sequence within float eps; validated against the **fed reference** tap.
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- **Time modes**: reconstructed timestamps must be monotonic, within wall-clock
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bounds, and spaced by `1/SamplingRate` (First/LastSample) or equal the time
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array (FullArray); compared to fed reference where reconstruction is subtle.
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- **LTTB live/zoom views**: shape metric — Pearson correlation ≥ 0.99 and
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normalised RMSE ≤ tolerance against truth resampled onto returned timestamps.
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## Client behavioural validation (Go mock, authoritative)
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Per scenario the client performs and records:
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- **Live**: ≥N binary v1 frames; per-signal wall-clock + monotonic time; dump
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`received_<id>.bin`; waveform vs truth (shape metric).
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- **Zoom**: ≥2 ranges (narrow "in" and wide "out"); points within `[t0,t1]`;
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count ≤ requested `n`; shape vs truth. Also `historyZoom` when history enabled.
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- **Time window**: set `windowSec`; assert returned/clamped span matches.
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- **Triggers**: matrix of `edge ∈ {rising,falling,both}` × `mode ∈
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{normal,single}` on an oscillating signal; validate triggerState transitions,
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v2 capture window `[trigTime−pre, trigTime+post]`, the edge actually crosses
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threshold in the correct direction at `trigTime`, and captured waveform vs
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truth. Normal mode must re-arm; single must not until `rearm`.
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- Emit `checks_<id>.json` (per-check pass/fail + measured values).
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## Qt GUI smoke (thin)
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`Client/streamhub-qt/test/smoke_test.cpp` using `QTest`, `QT_QPA_PLATFORM=offscreen`:
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construct `MainWindow`, point at a live StreamHub, spin the event loop, assert it
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received ≥1 source and rendered ≥1 frame, exercise a layout change and pause
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toggle without crashing. Built via the existing CMake; run via `ctest`.
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## Report
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`E2E_Report.typ` → `E2E_Report.pdf`:
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- Title + environment + matrix summary table (scenario, config, oracle, result).
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- Per-scenario waveform overlays (truth / received / diff) for a representative
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subset; aggregate pass/fail.
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- Trigger-capture plots (one per edge/mode).
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- Zoom/window plots.
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- Qt smoke result.
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`results.json` — aggregate machine-readable: list of scenarios with config
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summary, per-check metrics, pass/fail, and overall status (for CI gating).
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## Failure & robustness
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- Each scenario runs under `timeout`; stack always torn down via trap.
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- A scenario that fails to produce data fails that scenario only; the suite
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continues and the report records it.
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- Multicast scenarios are skipped (recorded as SKIP) if the loopback multicast
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route is unavailable, rather than hanging.
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## Out of scope (covered elsewhere)
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- Per-component standalone Typst reports → Sub-project C.
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- DebugService E2E → Sub-project B.
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- Real GUI click-automation beyond the QTest smoke.
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