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mristin
icontract-hypothesis
Python2021–2026
mristin / icontract-hypothesis

icontract-hypothesis

Combine contracts and automatic testing.

Testing & Quality
Explore on GitHubRead Hacker News discussions
2Distinct people
6Mentions
0Explicit recommenders
92GitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Combine contracts and automatic testing.

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

Mentioned in the same Hacker News threads. That is not proof they are substitutes.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year6 dated mentions

Observed mentions: 2021-02-03 20:00:01 UTC to 2026-03-17 06:11:17 UTC.

0122021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments20212022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments20232024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2025-01-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-01-01 00:00:00 UTC to 2027-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments. Incomplete calendar period.2026*

* An incomplete calendar period is marked with an outlined bar. Its count is not directly comparable with a full period.

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC202
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC000
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC101
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC202
20252025-01-01 00:00:00 UTC2026-01-01 00:00:00 UTC000
2026 (partial)2026-01-01 00:00:00 UTC2027-01-01 00:00:00 UTC101

What Hacker News says

6 sources
westurner·Mar 17, 2026
Parquery/icontract: https://github.com/Parquery/icontract mristin/icontract-hypothesis: https://github.com/mristin/icontract-hypothesis : > The result is a powerful combination that allows you to automatically test your code. Instead of writing manually the Hypothesis search strategies for a function, icontract-hypothesis infers them based on the function's precondition. This makes automatic testing as effortless as it goes. pschanely/CrossHair: An analysis tool for Python that blurs the line between testing and type systems https://github.com/pschanely/CrossHair : > If you have a function with type annotations and add a contract in a supported syntax, CrossHair will attempt to find counterexamples for you: [gif] > CrossHair works by repeatedly calling your functions with symbolic inputs. It uses an SMT solver (a kind of theorem prover)
Read the rest
to explore viable execution paths and find counterexamples for you
In Comparing Python Type Checkers: Typing Spec Conformance
westurner·Feb 18, 2024
"Automated Unit Test Improvement using Large Language Models at Meta" (2024) https://arxiv.org/abs/2402.09171 : > This paper describes Meta's TestGen-LLM tool, which uses LLMs to automatically improve existing human-written tests. TestGen-LLM verifies that its generated test classes successfully clear a set of filters that assure measurable improvement over the original test suite, thereby eliminating problems due to LLM hallucination. [...] We believe this is the first report on industrial scale deployment of LLM-generated code backed by such assurances of code improvement. Coverage-guided unit test improvement might [with LLMs] be efficient too. https://github.com/topics/coverage-guided-fuzzing : - e.g. Google/syzkaller is a coverage-guided syscall fuzzer: https://github.com/google/syzkaller - Gitlab CI supports coverage-guided fuzzing
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: https://docs.gitlab.com/ee/user/application_security/coverag... - oss-fuzz, osv Additional ways to improve tests: Hypothesis and pynguin generate tests from type annotations. There are various tools to generate type annotations for Python code; > pytype (Google) [1], PyAnnotate (Dropbox) [2], and MonkeyType (Instagram) [3] all do dynamic / runtime PEP-484 type annotation type inference [4] to generate type annotations. https://news.ycombinator.com/item?id=39139198 icontract-hypothesis generates tests from icontract DbC Design by Contract type, value, and invariance constraints specified as precondition and postcondition @decorators: https://github.com/mristin/icontract-hypothesis Nagini and deal-solver attempt to Formally Verify Python code with or without unit tests: https://news.ycombinator.com/item?id=39139198 Additional research: "Fuzz target generation using LLMs" (2023) https://google.github.io/oss-fuzz/research/llms/target_gener... https://security.googleblog.com/2023/08/ai-powered-fuzzing-b... https://hn.algolia.com/?q=AI-Powered+Fuzzing%3A+Breaking+the... OSSF//fuzz-introspector//doc/Features.md: https://github.com/ossf/fuzz-introspector/blob/main/doc/Feat... https://scholar.google.com/scholar?hl=en&as_sdt=0%2C43&q=Fuz... : - "Large Language Models Based Fuzzing Techniques: A Survey" (2024) https://arxiv.org/abs/2402.00350 : > This survey provides a systematic overview of the approaches that fuse LLMs and fuzzing tests for software testing. In this paper, a statistical analysis and discussion of the literature in three areas, namely LLMs, fuzzing test, and fuzzing test generated based on LLMs, are conducted by summarising the state-of-the-art methods up until 2024
In Automated Unit Test Improvement Using Large Language Models at Meta
westurner·Jan 1, 2023
icontract is one implementation of Design by Contract for Python; which is also like Eiffel, which is considered ~the origin of DbC. icontract is fancier than compile-time macros can be. In addition to Invariant checking at runtime, icontract supports inheritance-aware runtime preconditions and postconditions to for example check types and value constraints. Here are the icontract Usage docs: https://icontract.readthedocs.io/en/latest/usage.html#invari... For unit testing, there's icontract-hypothesis; with the Preconditions and Postconditions delineated by e.g. decorators, it's possible to generate many of the fuzz tests from the additional Design by Contract structure of the source. From https://github.com/mristin/icontract-hypothesis : > icontract-hypothesis combines design-by-contract with automatic testing. > It is an integration bet
Read the rest
ween icontract library for design-by-contract and Hypothesis library for property-based testing. > The result is a powerful combination that allows you to automatically test your code. Instead of writing manually the Hypothesis search strategies for a function, icontract-hypothesis infers them based on the function’s [sic] precondition
In Adding design-by-contract conditions to C++ via a GCC plugin

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