Speeding up tests with machine learning and distributed execution

⬢ Sr. Principal Software Engineer at Gradle Inc. ⬢ JUnit team lead Marc Philipp Email: marc@gradle.com Mastodon: @marcphilipp@chaos.social About me

Open Source Build Automation Commercial Offering Developer Productivity Engineering Supports Gradle, Maven, and Bazel Gradle Inc.

What is Developer Productivity Engineering? Tomorrow 15:15

Gradle Enterprise API Gradle Enterprise Technical Overview

Table of Contents Motivation Test Distribution Predictive Test Selection

Motivation 1

Testing usually dominates build times

Why is testing so slow? Databases Web servers Directories Virtual machines Network latency Network latency

Reduced test time yields increase in productivity More build and test executions Shift-left testing from CI to local builds Less context-switching

Local parallelization lacks historical information

Single-machine parallelism does not scale

CI Fanout Idea: run subset of tests on CI agents in parallel ⬢ Grouping of tests often manual ⬢ Large overhead because each CI agent has to run build up to test task ⬢ Test results are scattered over multiple CI jobs ⬢ Does not support local builds

Test Distribution 2

Test Distribution Overview ⬢ Broker component included in Gradle Enterprise server ⬢ Agents connect to the broker ⬢ Builds connect to the broker and request agents ⬢ Works for local and CI builds On-premises inside your network

Running Test Distribution agents ⬢ The agent comes in two flavors: Jar and Docker image ⬢ Runs on Java 11+, requires about 128 MB of memory ⬢ Runs on Windows, macOS, and Linux ⬢ Detects its environment (JDKs, OS) during startup ⬢ Administrators can pass additional capabilities as command line parameters java -jar gradle-enterprise-test-distribution-agent.jar \ --server https://ge.example.com \ --api-key «api-key» \ --capabilities docker,postgres=14 docker run \ --env TEST_DISTRIBUTION_AGENT_SERVER=https://ge.example.com \ --env TEST_DISTRIBUTION_AGENT_API_KEY=«api-key» \ --env TEST_DISTRIBUTION_AGENT_CAPABILITIES=postgres=14 \ gradle/gradle-enterprise-test-distribution-agent Runs on-premises inside your own network infrastructure

Gradle: Integrates with default Test task tasks.test { useJUnitPlatform() distribution { enabled.set(true) } } Code coverage and other output files are transferred back and merged automatically Input files (e.g. classpath) are automatically transferred to remote agents plugins { id("com.gradle.enterprise") version "3.11.4" }

Maven: integrates with Surefire/Failsafe plugins maven-surefire-plugin 2.22.2 true ⬢ Requires Gradle Enterprise Maven extension ⬢ Integrates with Surefire and Failsafe plugins

Test Distribution requires JUnit Platform ⬢ Most test frameworks with JUnit Platform test engines are supported: ○ JUnit 5 (Jupiter) ✔ ○ JUnit 3/4 and Spock 1.x (via junit-vintage-engine included in JUnit 5) ✔ ○ Spock 2.x ✔ ○ TestNG (via testng-engine) ✔ ○ ScalaTest (via scalatest-junit-runner) ✔ ○ ArchUnit ✔ ○ jqwik ✔ ⬢ Currently unsupported (we have plans to add support where possible): ○ Kotest ○ Spek ○ Cucumber

Checking compatibility ⬢ Compatibility can be checked before adopting Test Distribution by applying a custom build script that adds custom values to the Build Scan https://github.com/gradle/gradle-enterprise-build-config-samples/pull/469

Run build only once, distribute test execution

Automatic distribution based on previous execution times

Resilience against temporary network failures ⬢ Actively manage connections using WebSocket pings ⬢ Reconnect if connection is lost or unresponsive ⬢ Reschedule work on other agents if agent disappears ⬢ Retry file uploads on non-client errors ⬢ Avoid builds from breaking and causing disruption

Adaptive scheduling ⬢ Be able to react to additional agents becoming available during test execution ⬢ Increases agent utilization ⬢ Reduces testing time

Auto-scaling Test Distribution agents ⬢ Agent pools with min/max size and capabilities for horizontal scaling ⬢ HTTP endpoint provides metrics indicating the target number of agents for each pool, based on demand. ⬢ Step-by-step instructions for Kubernetes in docs ⬢ Real-time and historical usage can be visualized by Gradle Enterprise administrators { "id": "sosmbpbr", "name": "Linux", "capabilities": [ "jdk=8", "os=linux" ], "minimumAgents": 1, "maximumAgents": 90, "connectedAgents": 2, "idleAgents": 0, "desiredAgents": 8 }

Demo

PR Build w/ Integration tests 30 min → 3 min

PR Build Times with Integration Tests went from 60 minutes to 5 Danny Thomas Developer Productivity Team For one project we reduced the build time from 62 minutes to under 5 minutes just using Test Distribution across multiple machines. So we think that Test Distribution will really move the needle and improve the test experience for everyone.

Local Build Times: From 54 min to 5. CI PR Builds: Way More Reliable Cédric Champeau Technical Staff, Oracle (former Gradle Build Tool engineer) My test suites finished in 5 minutes instead of 54. The 10X developer is finally here!

Gradle Enterprise Acceleration Features

Gradle Enterprise Acceleration Features Gradle Enterprise 2022.2

Predictive Test Selection (PTS) 3

Avoid wasting time and resources ⬢ Typically less than 1% of codebase affected by any given change ⬢ Typically fewer than 1% of tests affected by any given change ⬢ Yet, most reported test failures are not regressions 💡 Skip irrelevant tests for a given change set using machine learning

Not a new concept ⬢ Predictive Test Selection — Meta 2019 ⬢ Improving Test Effectiveness Using Test Executions History: An Industrial Experience Report — Ericsson 2019 ⬢ Taming Google-Scale Continuous Testing — Google 2017 ⬢ Test Re-prioritization in Continuous Testing Environments — Concordia Univ. 2016 ⬢ The Art of Testing Less without Sacrificing Quality — Microsoft 2015 ⬢ Improving the effectiveness of test suite through mining historical data — ACM 2014 ⬢ … and more

1. When a test run starts, Gradle Enterprise submits a test input snapshot and test set to a machine learning model. 2. Gradle Enterprise automatically develops a test selection strategy by learning from historical code changes and test outcomes from your Build Scan™ data to predict a subset of relevant tests, which are then executed by your build. 3. Code change and test results data are processed immediately after a Build Scan is uploaded to Gradle Enterprise and updates the test selection strategy based on new results. How it works…

Predictive Test Selection Simulator ⬢ Simulates what tests would have been selected had PTS been enabled ⬢ Compares full test results to selected test results ⬢ Allows to assess risk and savings of PTS per task/goal before adopting it ⬢ Requires at least 50 executions over a 14-day period before starting to make predictions

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maven-surefire-plugin 2.22.2 true Enabling Predictive Test Selection tasks.test { predictiveSelection { enabled.set(true) } } plugins { id("com.gradle.enterprise") version "3.11.4" }

Must-run tests import com.gradle.enterprise.testing.annotations.MustRun; @MustRun public class ImportantTests { // ... } Using gradle-enterprise-testing-annotations from Maven Central. tasks.test { predictiveSelection { mustRun { includeClasses.add("example.ImportantTests") } } } example.ImportantTests

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Usage patterns Our recommendation is to run all tests post-merge or at least periodically. 1. Apply to existing pre-merge verification (run all tests post-merge) 2. Apply to existing pre- and post-merge verification (move “all tests” run to nightly build) 3. Run some high value tests pre-merge (change CI jobs to run costly tests sooner) 4. Two-pass pre-merge (split pre-merge CI jobs into two steps to get faster feedback)

https://ge.spring.io https://ge.junit.org https://ge.micronaut.io https://ge.gradle.org Predictive Test Selection in Action

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