--- pretty_name: AI Service QoS — field evidence for a Qwen3.8-Max / Qoder CN incident tags: - ai-evaluation - observability - reliability - latency - large-language-models - service-quality size_categories: - n<1K language: - en license: - cc-by-4.0 # documented key is `license` (accepts a list); `licenses` is ignored configs: - config_name: request_lifecycle_round1 data_files: - split: train path: evidence/stats/request_lifecycle.csv default: true - config_name: request_lifecycle_round2 data_files: - split: train path: evidence/incident_20260910/request_lifecycle.csv - config_name: session_sizes data_files: - split: train path: evidence/stats/session_size_stats.csv - config_name: context_growth data_files: - split: train path: evidence/stats/context_growth.csv - config_name: timeline_events data_files: - split: train path: evidence/stats/timeline_events.csv - config_name: timeout_events data_files: - split: train path: evidence/logs/timeout_resume_events.csv --- > **Released 2026-09-11 as v1.0.2** (methodology v0.1). This snapshot is frozen: later > corrections and additions ship as a new version with a new tag, never as an edit to the bytes a > citation points at — cite the tag or the Digital Object Identifier, not a branch. Two version > numbers mean two different things here: the tag names this public snapshot, while *methodology > v0.1* names the definitions, which stay unchanged until a second provider has been measured > (section 7). Every figure quoted in this document set is asserted against the shipped evidence by > `scripts/analyze.py`, which fails when a number and the evidence disagree and warns when a file > is edited after the date above. **AI Service QoS — Independent Measurement of What Users Actually Receive** — methodology v0.1, and the first data product of an independent, outside-in measurement framework. The system measured here is **Case Study 1**, not the subject: the framework claims provider neutrality and one provider is not proof of it — a second provider measured with *these same definitions and no new ones* is the test (paper section 7, Stage 3). Machine-checkable evidence for **"AI Service QoS: An Independent Outside-In Measurement Methodology, with Case Study 1 (Qwen3.8-Max / Qoder)"** (`paper_ai_QoS.md`, with the practitioner report `report_qwenAliServiceQuality.md`). It is a request-level measurement of the service quality an ordinary user experiences in an artificial-intelligence coding service, taken entirely from the client's own runtime log, with no provider cooperation and no access to the provider's infrastructure. ## Dataset details ### Where each layer of this project lives This repository is the **data layer**. The method document (`paper_ai_QoS.md`) and **Case Study 1** (`report_qwenAliServiceQuality.md`), the vendor-facing report with the per-event tables and the asks, are in the source repository: — together with the collector, the anonymiser and the log mirror that produced these files. Two scripts ship **here** as well, so that nothing in this card has to be taken on trust inside this repository: `scripts/analyze.py` re-derives and asserts every published figure against `evidence/`, and `scripts/verify_dataset_card.py` asserts this card against the files it declares. ### Motivation Capability benchmarks score answers; provider dashboards report what the provider chooses to measure. Neither answers the user's question — *did my request get a usable answer, and how long did I wait?* This dataset exists so that question can be asked of a service from the outside, and so every number in the paper can be recomputed from these files rather than taken on trust. ### Verification after publication The whole published repository was downloaded back into an empty directory and compared file by file with the tag it was built from — which is exactly what `scripts/check_release_sync.py` does, so this is re-checkable by a reader rather than autobiographical. Measured 2026-09-11 against tag `v1.0.2`: **every data-layer file agrees byte for byte**, `README.md` included — that file is this card with its three internal-instruction lines removed, and the source repository's own `README.md` is a different document, its homepage. The hub holds one file no tag contains: the `.gitattributes` it created with the repository. The generated table ends with a line carrying a content hash and a second-precision timestamp: the hash is what a machine compares, the time is what a person reads, and when the statistics are unchanged the script leaves the file alone — so running the checks cannot dirty a checkout or produce new bytes, and two copies with the same hash hold the same statistics whatever their stamps say. (Incidental finding for anyone scripting the client: `hf download --include` accepts repeated patterns but only honoured the last one, so pass one pattern per invocation.) Both scripts were then executed **inside that download**, with nothing else present: `scripts/verify_dataset_card.py` exits 0, and `scripts/analyze.py` fails nothing — printing a skip for the checks that compare the paper with the case report, since those narratives live in the source repository and their absence is announced rather than passed over as a pass. ### Composition | Path | Contents | |---|---| | `evidence/logs/` | 388 kilobytes: the 33-event timeout extract, 1,252 conversation-lifecycle lines from the client's task tracker, 885 per-interaction phase lines, 50 quota-error log lines with payload variants, the outcome census | | `evidence/config/` | 108 kilobytes: the fetched error-code dictionary entries, the model-registry line and the bring-your-own-key definitions that show the 180,000-versus-1,000,000 contradiction, the selected context presets, the environment record | | `evidence/stats/` | 44 kilobytes: 20-conversation transcript size table, the context-growth curve, the merged timeline, the round-1 request-lifecycle table | | `evidence/incident_20260910/` | 20 kilobytes: the second collection round — 62 request episodes carrying per-request **context occupancy in tokens**, plus its own README | | `evidence/screenshots/` | 152 kilobytes: the operator's screen showing nine consecutive timeout banners in one conversation | | `evidence/error_messages.json` | Every string the paper quotes, verbatim, in one place | | `analysis/summary_tables.md` | The derived statistics, regenerated and asserted by `scripts/analyze.py` | Totals: 25 files, 724 kilobytes under `evidence/`, plus the generated analysis table. Two collection rounds, deliberately separate: 2026-09-01 → 09-09 (frozen bundle, conversation labels `sess-01` … `sess-20`) and 2026-09-10 (labels `S1` …, own frozen log snapshot). ### How to load it, and how to check that this card is telling the truth The six tabular files are declared as named configs, so a reader loads a table directly instead of eyeballing markdown: ```python from datasets import load_dataset round2 = load_dataset("2makeitwork/paper_ai_Qos", name="request_lifecycle_round2") round2["train"].num_rows # 62 request episodes round2["train"][0]["used_tokens_at_send"] ``` The six configs: `request_lifecycle_round1` — 308 rows, `request_lifecycle_round2` — 62 rows, `session_sizes` — 20 rows, `context_growth` — 93 rows, `timeline_events` — 51 rows, `timeout_events` — 33 rows. Everything else under `evidence/` — the log extracts, the configuration records, the screenshot — is deliberately **not** a config: those are line-oriented evidence files meant to be read and grepped, and flattening them into a table would destroy the timestamps that make them verifiable. Two independent checks, one offline and one after upload: ```bash python3 scripts/verify_dataset_card.py # every declared file exists, every # declared column matches its header, # row counts match the card text python3 scripts/analyze.py # re-derives and asserts every published figure ``` ```bash python3 -c "from datasets import load_dataset; \ [load_dataset('2makeitwork/paper_ai_Qos', name=n)['train'] for n in \ ['request_lifecycle_round1','request_lifecycle_round2','session_sizes', \ 'context_growth','timeline_events','timeout_events']]; print('LOADABLE')" ``` **State as published (first upload 2026-09-10, commit `16a35ca`; revised in place since).** Confirmed by the hub: the repository is public, 36 files, and the front matter was parsed — its tags carry `license:cc-by-4.0`, `size_categories:n<1K` and `language:en`. **Confirmed by the hub** later the same day (2026-09-10): its conversion endpoint returned **all six** configs with their `train` splits — `request_lifecycle_round1`, `request_lifecycle_round2`, `session_sizes`, `context_growth`, `timeline_events`, `timeout_events` — which is what `load_dataset` and the dataset Viewer read. Minutes after the first commit the same endpoint returned `500 Internal Server Error`, so the conversion is asynchronous and a single early failure means nothing. One thing this does **not** establish: the `datasets` library is not installed on the workstation that published this, so the Python snippet above has not been executed here — the configs are server-converted, not snippet-verified. The non-tabular part of the bundle stays browsable and downloadable either way; making a file loadable is not the same as making it required for reproduction. ### Collection process A single workstation, a single subscription account, one operator, nine plus one days. The client's runtime log tree was mirrored continuously (append-only, one file per path and inode, each committed region re-hashed on every scan so an in-place edit of already-written log bytes is detectable), frozen as checksummed snapshots, then filtered to named log anchors and anonymised by script. Nothing was typed by hand into any file under `evidence/`. ## Personal and private content **Conversations are not included.** Full chat transcripts carry third-party project content, so they are excluded from the bundle entirely; only statistics, error-related log extracts and configuration records pass through. Identifiers are replaced by a deterministic, script-built map — paths, user names, host names, local network addresses, account and model identifiers, application programming interface keys, and every conversation identifier (report `anonymization.md`). The anonymizer fails with a non-zero exit code if any forbidden pattern survives its own output, and a repository-wide gate re-checks every published file, link and ignored directory: ```bash python3 scripts/analyze.py # re-derives and asserts every published figure python3 scripts/verify_dataset_card.py # asserts this card against the files it declares python3 scripts/check_release_sync.py # compares this repository's bytes with the source # repository and the Zenodo record ``` The first two run offline and exit 0 in either repository — the source repository and this data layer — against the files shipped here. The third reaches the hub (and Zenodo, given a record identifier) and is the release-time check that the copy you are reading is byte-for-byte the copy in the other places: it reports drift rather than asserting agreement, because the three locations are updated by different commands and nothing synchronises them. Anything not listed above is author-side tooling, or needs material deliberately not published here — the raw client logs, which carry local paths and real conversation identifiers. The commands above are what a reader can actually run, so they are what this card asks a reader to run. Pseudonyms (`sess-NN` in round 1, `S1`… in round 2) are numbered by the data's own order; the mapping to real conversation identifiers is not published and cannot be recovered from these files. ## Uses ```bash python3 scripts/analyze.py # re-derives and asserts every published figure from evidence/ ``` The log-side tools are source-repository material and are **not published here**: `scripts/latency_from_logs.py` needs the raw client logs, which carry local paths and real conversation identifiers and are never distributed. What does ship here re-runs from these files, and `methodology.md`, "What re-runs, and from what", states the boundary honestly rather than implying a full rebuild. Use it to reuse the event schema, to check the paper's arithmetic, to compare your own provider against the method, or as training data for nothing at all — see below. ### Out-of-scope uses Not a model-quality benchmark and not a leaderboard: it measures the service, not the answers, and one account over ten days cannot rank providers. Not suitable as model training data — it contains no conversational text by design. Not a certification of the named products; the vendor's replies are collected in `ask_vendor.md` and were not received at publication time. ## Limitations Single workstation, single account, single model pair, ten days, no provider cooperation. Observational except for one within-conversation pair (context compaction). Log retention is size-based and shallow, so the earliest events of a busy conversation are unrecoverable. The client's timeout threshold is inferred from its own state machine, not documented by the vendor. Request **size** is available from the client only as context occupancy; output-token and cached-input-token counts are absent, which is why the mechanism question stays open. State each of these whenever the data is reused. ## Citation ``` 2makeitwork (independent, unaffiliated). "AI Service QoS: An Independent Outside-In Measurement Methodology, with Case Study 1 (Qwen3.8-Max / Qoder)." Dataset snapshot , 2026. ``` **What to cite, in order.** The paper is the object; this dataset is its evidence. Cite `paper_ai_QoS.md` at a tag for the method and the findings, this repository for the observations, and the archived snapshot when the citation needs an identifier that cannot move: concept DOI [10.5281/zenodo.22701430](https://doi.org/10.5281/zenodo.22701430) covers every archived version of the released software, while each version carries its own — `v1.0.0` is [10.5281/zenodo.22701431](https://doi.org/10.5281/zenodo.22701431) and `v1.0.1` is [10.5281/zenodo.22701701](https://doi.org/10.5281/zenodo.22701701). Cite the **snapshot**, name the **collection round**, and cite the **client version plus service** rather than the model alone — a client-side watchdog misattributed to a model is the most likely misquote. `CITATION.cff` carries the machine-readable form; the positioning against prior measurement work is in the paper's section 1.4, with each work's own identifier given there. ## License Text, tables, figures and data: **Creative Commons Attribution 4.0** (`LICENSE-content.md`). The scripts published alongside it, which re-derive and assert every number in this card: **MIT** (`LICENSE-code.md`).