A collaborative bibliography of papers related to property-based testing https://github.com/ngernest/pbt-bibliography
❤6

Channel
@sqaunderhood
On this record: Growth · Engagement · Reactions · Stars · Posts · Citations · Cite this entry
1,888subscribers
+1 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of 1,000–3,162.
| Telegram ID | -1001143811195 |
|---|---|
| Type | Channel |
| Username | @sqaunderhood |
| Created | 22 July 2017 — measured — cross-checked against a third-party dataset (ext.tg_channel) |
| First recorded | 6 August 2026 |
| Last confirmed live | 12 August 2026 |
| Measurements held | 3 |
| Confirmed unchanged | 1 time, most recently 12 August 2026 |
| On Telegram | t.me/sqaunderhood |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 12 Aug 2026, 02:32 | 1,888 | +1 |
| 6 Aug 2026, 11:31 | 1,887 | no change |
| 6 Aug 2026, 09:46 | 1,887 | first reading |
19 posts held, back to 18 December 2025 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 2 pagesof Telegram’s post history, 20 posts per page.
Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 19 posts for this entry, the most recent from 9 July 2026. An engagement rate over an empty window would be a number about nothing.
358 reactions across 19 posts, in 24 distinct kinds. The most used accounts for 20.7% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 🔥 | 74 | 20.7% | |
| 🤮 | 46 | 12.8% | |
| ❤ | 39 | 10.9% | |
| 👍 | 39 | 10.9% | |
| 🤡 | 33 | 9.22% | |
| 💩 | 30 | 8.38% | |
| 👀 | 15 | 4.19% | |
| 😭 | 13 | 3.63% | |
| 😢 | 10 | 2.79% | |
| 👏 | 9 | 2.51% | |
| 👎 | 7 | 1.96% | |
| ❤🔥 | 5 | 1.40% | |
| 🖕 | 5 | 1.40% | |
| 🤔 | 5 | 1.40% | |
| 🤣 | 5 | 1.40% | |
| 🤯 | 5 | 1.40% | |
| ✍ | 4 | 1.12% | |
| 🕊 | 4 | 1.12% | |
| 🙈 | 4 | 1.12% | |
| 😨 | 2 | 0.559% | |
| 4 further kinds | 4 | 1.12% |
No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.
Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.
Coverage. Reactions were read on 19 of the 19 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 358reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 19 most recent posts we hold, published 18 December 2025 to 9 July 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @sqaunderhood. Telegram publishes the count on the public post preview alongside ordinary reactions, and this register reads it there. It is the only figure on this site that measures money moving rather than attention.
Stars are not reactions, and the two are never added. They are rendered in the same strip on Telegram and counted in the same shape, but one is a tap and the other is a purchase. The reaction totals and the engagement rate elsewhere on this page exclude every figure in this section, and no rate here is computed against a reaction count.
This is not revenue, and we publish no currency figure. What a Star costs a reader and what it pays a channel are different numbers, Telegram takes a share we cannot observe, and the terms have changed. Converting a Star count into money would be an estimate dressed as a measurement, so the count is where we stop.
Counted over the 19 most recent posts we hold for this entry, published 18 December 2025 to 9 July 2026. Star counts above 1,000 reach us in Telegram’s short form and carry the same three-significant-figure rounding as everything else on this page.
A collaborative bibliography of papers related to property-based testing https://github.com/ngernest/pbt-bibliography
❤6
В Github UI появился автоматический дедупликатор тикетов, во время создания нового тикета, но ищет похожие по описанию. Впрочем работает пока так себе, в моём случае это был не дупликат.
🔥6👍3❤1
CirrusCI всё: Cirrus CI will shut down effective Monday, June 1, 2026. Перейдут под крыло OpenAI. В прощальном тексте основатель перечисляет инновации: Over the last nine years, we were fortunate to innovate across continuous integration, build tools, and virtualization. In 2018, we introduced what we believe was the first SaaS CI/CD system to support Linux, Windows, and macOS while allowing teams to bring their own…
😢3❤1
Интересная статья про использование сравнительного тестирования для приложений с состоянием - "Vive la Différence: Practical Diff Testing of Stateful Applications". При обновлении stateful-приложений работающих с БД часто возникают трудноуловимые ошибки и традиционные методы (канареечные релизы, роллинг-обновления, cине-зелёное развёртывание) плохо эти ошибки выявляют из-за разделяемого состояния. Стандартные тесты …
👍5🔥4🤝1
Раньше ведь как было: изучаешь теорию языков программирования и формальные методы, формальные грамматики, лексический и синтаксический анализ, работу c AST (обход дерева, трансформация), атрибутные грамматики и синтезируемые/наследуемые атрибуты, изучаешь системы типов, семантику программ, чтобы понимать, что означает программа, изучаешь межпроцедурный анализ. Изучаешь алгоритмы статического анализа: анализ потоков д…
🤯5👍1
Sashiko мониторит список рассылки с патчами в ядро Linux (LKML) и запускает ревью этих патчей с помощью LLM (сейчас, как я понял, это Gemini). Результаты публикует в симпатичном WebUI. https://sashiko.dev/
✍4👀2😨2❤1
Если вы хотели погрузиться в тему SDLC, то вот вам знак свыше: в весеннем семестре в ВМК МГУ проходит спецкурс по РБПО. Лабораторные работы будут доступны только студентам, лекции будут транслироваться в Jitsi по ссылке, прослушивание доступно всем желающим просто по ссылке, записи лекций (кроме первой) обещают выкладывать. Лекторы из ИСП РАН и индустрии. 3 марта Вводная лекция. 10 марта Неопределенное поведение и …
❤🔥5❤4🔥3
5 марта умер сэр Тони Хоар Некролог: https://blog.computationalcomplexity.org/2026/03/tony-hoare-1934-2026.html
😭13🕊4👌1🙏1
Поиск точек разладки (changepoint) это популярная задача в разных областях. Это канал про тестирование ПО, поэтому ограничимся поиском точек разладки при анализе результатов тестирования производительности. В отличие от функционального тестирования результаты тестов производительности нужно постоянно анализировать и выявлять причины отклонений результатов, в некоторых компаниях этим занимаются отдельные команды (Андр…
❤5👍5
После анонса задачи про верификацию оптимизаций в LuaJIT в лаборатории Tarantool мне написал студент Физтеха Алексей и сказал, что хочет взять эту задачу в качестве дипломной работы и с сентября прошлого года мы продолжаем делать верификатор для LuaJIT. Много успели сделать и есть результаты, которыми можно поделиться. Напомню основную идею - научиться моделировать семантику LuaJIT IR с помощью SMT-LIB, декларативно…
🔥19👍2
Обычно для оценки степени покрытия кода фаззинг-тестированием используют покрытие по строкам/функциями и редко по ветвлениям. Метрика покрытия MC/DC редко используется для оценка покрытия регресионными тестами вообще и фаззинга в частности, хотя она позволяет получить доказательство того, что логика надежно тестируется в коде. Я сделал патч, чтобы можно было для кода PUC Rio Lua и LuaJIT собирать MC/DC покрытие, мне …
🔥11🤔4❤1
In this work, we address this issue by proposing an efficient white-box checker, Emme. Our key idea is to use information that is easily provided by database systems to efficiently check the isolation level of a given transaction history. We present version certificate recovery, a method of recovering the version order and each operation’s version from the database system under test. For efficiency, we also propose t…
👍6
Showing the 12 most recent of 19 posts we hold for @sqaunderhood. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
Citation-graph rank — 936,247 of 1,340,412entries in the measured graph. A weighted position computed from the forward and mention edges below — republished posts weigh more than named mentions — and recomputed periodically, over the whole graph. Published only as this ordinal position, never as a score: a position is a fact, and a score printed beside one channel’s name would read as a verdict this register does not make. The two counts beneath stay separate for the same reason mentions are never summed with forwards anywhere else on this page — a named-by count costs nothing to manufacture. The top 100 by this measure, or how it is computed.
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.
Named by 2 registered channels — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it.
Named by
Channels on the register whose posts name this channel's handle.
A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 12 August 2026 — this entry's latest reading, not the date you are reading this.
“Протестировал” (@sqaunderhood), 1,888 subscribers as measured 12 August 2026. Telegram Register, tgregister.com/channel/sqaunderhood.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.