| commit | e4f5c82209dc939857815cefb52933bf249cc52d | [log] [tgz] |
|---|---|---|
| author | Googler <noreply@google.com> | Thu Nov 17 16:01:26 2016 +0000 |
| committer | Kristina Chodorow <kchodorow@google.com> | Thu Nov 17 18:18:39 2016 +0000 |
| tree | 362989d76a2bc6f8388cd3b4871fe8af250001dc | |
| parent | a2bbe67ecf5a95777e13820c165f2955037a14fd [diff] |
Add ctx.coverage_instrumented function to Skylark Skylark already has ctx.configuration.coverage_enabled to determine if coverage data collection is on for an entire run. But that does not reveal which targets specifically are supposed to be instrumented (based on the values of --instrumentation_filer and --instrument_test_targets). This is inefficient for languages which add coverage instrumentation at compile-time, though correct coverage output can still be produced by instrumenting everything and filtering later. By default, this function returns whether the rule represented by ctx should be instrumented. If a Skylark Target (e.g. from a label or label_list attribute in ctx.attr) is passed to the function, it instead returns whether that Target is a rule whose sources should be instrumented. Rules that directly incorporate source-files from their dependencies before compilation (e.g. header files) may need to know if those source files need to be instrumented when compiled. Expanded the documentation of instrumented_files to be a more general section on implementing code coverage instrumentation in Skylark. Also tweaked the code comment and variable names for the version of shouldIncludeLocalSources that takes a TransitiveInfoCollection. RELNOTES: Add ctx.coverage_instrumented function to Skylark, to indicate whether a specific targets should be instrumented for code coverage data collection. -- MOS_MIGRATED_REVID=139460989
{Fast, Correct} - Choose two
Bazel is a build tool that builds code quickly and reliably. It is used to build the majority of Google‘s software, and thus it has been designed to handle build problems present in Google’s development environment, including:
A massive, shared code repository, in which all software is built from source. Bazel has been built for speed, using both caching and parallelism to achieve this. Bazel is critical to Google's ability to continue to scale its software development practices as the company grows.
An emphasis on automated testing and releases. Bazel has been built for correctness and reproducibility, meaning that a build performed on a continuous build machine or in a release pipeline will generate bitwise-identical outputs to those generated on a developer's machine.
Language and platform diversity. Bazel's architecture is general enough to support many different programming languages within Google, and can be used to build both client and server software targeting multiple architectures from the same underlying codebase.
Find more background about Bazel in our FAQ.