Search Results for "p-unit"

...Confused by SBT? Frustrated by Maven? Perplexed by Gradle? Give Mill a try. In-process tests live in the .test sub-modules of the various Mill modules. These range from tiny unit tests, to larger integration tests that instantiate a TestUtil.BaseModule in-process and a TestEvaluator to evaluate tasks on it. Note that the in-memory tests compile the BaseModule together with the test suite, and do not exercise the Mill script-file bootstrapping, transformation, and compilation process.

Deequ is a library built atop Apache Spark that enables defining “unit tests for data” — that is, formal constraints or checks on datasets to ensure data quality along dimensions such as completeness, uniqueness, value ranges, correlations, etc. It can scale to large datasets (billions of rows) by translating those data checks into Spark jobs. Deequ supports advanced features like a metrics repository for storing computed statistics over time, anomaly detection of data quality metrics, and the suggestion of likely constraints automatically for new datasets. ...

...This is an attempt to bypass the problem by generating Scala code directly. It also uses Scala syntax so the generated code is much more compact. There is a fairly comprehensive set of unit tests, which actually generate code, compile it, and execute it to verify expectations.

Rocket Chip is a parameterized RISC-V SoC generator written in Chisel that produces synthesizable RTL for a wide range of cores and configurations. At its heart is the Rocket core, a simple, in-order, five-stage RISC-V implementation, but the generator composes much more: coherent caches, MMUs, interrupt controllers, and buses via the TileLink interconnect. A diplomacy framework (LazyModules) lets designers wire components with negotiated parameters, enabling reuse and rapid exploration of...

...It acts as a proxy that fans out real production requests to three backends: the current “primary,” a “candidate” (new build), and a “shadow” baseline, then compares responses to detect behavioral differences. By using live traffic rather than synthetic tests, Diffy surfaces edge cases and data-dependent discrepancies that unit tests often miss. It provides mechanisms to normalize noise (timestamps, IDs, ordering) so diffs reflect meaningful regressions rather than incidental variance. Results are aggregated and visualized so teams can triage by endpoint, response field, or error type, turning release validation into a data-driven process. Built on Twitter’s Finagle stack, Diffy demonstrates a practical pattern for continuous verification and safer deployments in microservice environments.

Spray is a suite of Scala libraries built on top of Akka that offers a modular, asynchronous, and non-blocking toolkit for building and consuming RESTful and HTTP services. The core philosophy behind Spray is that it should act as a “library” for integration and HTTP layers, not as a full application framework—it gives you the building blocks to handle HTTP and REST but doesn’t impose heavy structure. It includes modules for low-level HTTP I/O, routing, client and server APIs, HTTP model...

This domain specific language lets you declare complex java objects with a Json-like notation. In addition to that, it lets you specify in a natural way what the mockito mocks should return when called with a given value.