Parameterized tests allow you to write a test once, but allow it to be called with multiple parameters. This means it is creating more methods than you’ve written, but without you having to write and maintain each one individually.

Here is a trivial example of writing a single test with TestParameterInjector, but having it run twice with true and false.

@RunWith(TestParameterInjector::class)
class MyTest {

    @Test
    fun test(@TestParameter isOwner: Boolean) {
        // Your test logic here
    }
}

This example results in the following tests being run:

• MyTest#test[isOwner=true]
• MyTest#test[isOwner=false]

TestParameterInjector has been the defacto way to do this on the JVM and is still a good solution for those projects. The original Burst 1.x project was archived and pointed users to use TestParameterInjector.

Burst 2.0 bursts onto the Scene!

Fast forward to October 2024 and Kotlin Multiplatform in full swing. There is no solution for parameterized tests for Kotlin Multiplatform, so in order to solve the problem, Burst 2.0 was created as a Kotlin Compiler Plugin.

It Works on Android! 🎉

While Kotlin Multiplatform support is fantastic, my biggest need at work is to use it for Parameterized Android Instrumentation tests, so I tried it out on my open source project ShoppingApp and here are my experiences.

It ran successfully via the ./gradlew :app:connectedDebugAndroidTest --info command.

Despite IDE support issues (red highlighting), the underlying library compiles and executes on the command line 😄 .

Executing the Android Tests with the IDE also causes failures for some reason even though it works.

The Problem of Massively Sharding Android Tests

TestParameterInjector has been a solution for Android Parameterized Tests but falls short when wanting to statically compute all tests methods for sharding purposes.

Cloud based device farms like Firebase Test Lab can run your tests on _ number of devices at once and combine the results. This is facilitated in many projects through Flank (Fladle – Flank Support in Gradle). The problem is that TestParameterInjector computes the test method names at runtime, and therefore these device farms can only get an entire class to run (which might end up having 100 permutations with parameterized tests).

Parameterized tests are powerful, reduce boilerplate and ensure exhaustive coverage. To unlock this capability and support massively sharding tests, I personally spent 2 weeks developing a solution that DOES work with TestParameterInjector to compute all test names via static analysis of the APK via this multi-step process described here:

This has been super helpful in allowing my team to run parameterized tests in a massively sharded device farm, but it is a complex solution (but runs in 3 seconds), and while there are tons of integration tests for the library to ensure it works, because it’s an involved set of steps, it still feels like it could fall over at some point.

I could open source this solution, but it has it’s limitations, and if we do have Burst 2.0 at our disposal now. I’m going to endorse their project and avoid having to maintain a project with a pretty equal feature set.

The big hurdle with TestParameterInjector and computing a deterministic list of tests statically (using limited features like enums, strings, etc) is hard because it’s determining the tests to run via the JUnit Runner itself at runtime, instead of when code is being compiled. This means the full list of methods are not in the final Test APK, but need to be loaded into JUnit and computed in a JVM runtime.

Burst 2.0 uses the same limitations as my solution (enums, booleans and list of strings), but does it at the Kotlin Compiler phase Because of this different method, computing all android tests to be run can be done via static analysis of the APK by tools like https://github.com/linkedin/dex-test-parser which computes a full list of methods from an APK. This also means that it will work in Firebase Test Lab and other sharding by method runners. Related Issue of TestParameterInjector -> https://github.com/google/TestParameterInjector/issues/27 to allow that functionality.

Kotlin Compiler Plugin

This what the resulting classes look like for a compiled class after getting processed by Burst.

Required Dependencies

Transitively brings in Kotlin 2.0.21 to your build classpath.

Conclusion

Burst 2.0 seems like the right solution for Android Developers to run parameterized tests and have it possible for sharded test runners to deterministically compute all test method names using static analysis.

If you are someone out there that really wants the implementation I have for TestParameterInjector, then reach out to me a threads.net/@handstandsam and I’ll see if I can share it in open source (but won’t maintain the library).

For now, to use Burst 2.0, you need Kotlin 2.0, but that’s already the case in the majority of projects since it was released as stable 6 months ago. You might have some IDE support issues for a bit, but I’m excited about Burst 2.0, and I’d encourage you to try it out on your Android projects!

I wrote this Kotlin K2 FIR guide because I was not able to find any guides or examples to get started with FIR for static analysis. K2 is now finally stable in Kotlin 2.0.0 and future versions, so FIR will be the recommended way of doing static analysis.

Background

Starting Kotlin 1.x you could only use PSI (Program Structure Interface) to create an AST (Abstract Syntax Tree) to run static analysis.

Kotlin’s K2 Compiler is powered by a new Frontend Intermediate Representation (FIR). It still uses PSI to create the initial model, but transforms that into FIR which is a semantic model that is independent from any compiler backend (JVM, JS, Native, etc).

Step 1

Add the Kotlin Embedded Compiler Dependency

implementation("org.jetbrains.kotlin:kotlin-compiler-embeddable:2.0.0")

Step 2

Use the code in the Gist: https://gist.github.com/handstandsam/9a561fc78b593039d1dd500fae14b355

Yes, this is a really short post, but it’s more so here so that you’ll discover the Gist from a search and be able to get started using K2’s FIR model!

Conclusion

FIR is much more useful in understanding what the code is instead of the original syntax. That being said, you can still reach back into the code that the FIR model is derived from to gain more context as needed. If you are building any Static Analysis tooling, it’s HIGHLY suggested to start building it on FIR going forward.

Credits

This implementation is based on:

• https://github.com/cashapp/zipline/blob/4fa1014c833c46fd8c4b6b6add83786a2e4ea618/zipline-api-validator/src/main/kotlin/app/cash/zipline/api/validator/fir/KotlinFirLoader.kt
• https://github.com/cashapp/redwood/blob/afe1c9f5f95eec3cff46837a4b2749cbaf72af8b/redwood-tooling-schema/src/main/kotlin/app/cash/redwood/tooling/schema/schemaParserFir.kt
• https://github.com/cashapp/redwood/pull/992

As a former web-developer myself, I still gravitate back to browser-based UIs. They are so easy to access from anywhere, and are globally available. It’s hard to argue the utility of the amazing web platform. Compose for Web (WASM) is the latest technology in Kotlin Multiplatform and I’m pretty bullish about it.

In this post I’ll take you through what it is and why I think it’s going to be pretty big.

My Past Explorations into Kotlin + Web

I have previously dabbled in Kotlin Multiplatform for JavaScript and Compose HTML in my ShoppingApp project. I find them both very exciting, but I’ve only seen them useful in limited use cases.

I’ve started digging to Compose for Web (WASM), but wanted to give some context to start as there are similarly named things and disambiguation is needed.

Kotlin/JS

This JavaScript (Kotlin/JS) compilation target is here to stay, and is useful not only in the browser, but for backends using Node.js. This technology allows code to be compiled to JavaScript, but doesn’t have any concept of UI itself.

Kotlin/JS is great for complex business logic so it doesn’t have to be re-written and tested in multiple languages.

Kotlin/JS is not so great at size (KBs), and therefore a hard sell to use in Web UIs. I pitched some password validation rule logic to a set of Angular web devs and was told that the JavaScript that was generated was over 10x the size it would be if they wrote it in typescript. That was fair, but I can see the argument tipping the other way if there is really complex business logic. At that point, the consistency and maintainence costs can be more important than page load time (which should get better as time goes on). Note: The reason why the generated JavaScript is so large is because it needs to bring along the Kotlin Standard Library (stdlib), implemented in JS. That is an upfront cost though, so adding additional logic should be fairly linear.

Compose HTML (Previously “Compose for Web”)

Compose HTML allows the compose runtime to render HTML elements. While very cool from a technical standpoint, the marketing behind this has fizzled out (rightfully so) to try and make room for “Compose for Web” (WASM), and create less ambiguity. You can see in this JetBrains blog post first announcing it where it was branded “Compose for Web”, but is now specifically named Compose HTML to disambiguate.

This reminds me of Mosaic, which allows you to leverage the Compose runtime to create terminal apps.

Both are very cool from a technical standpoint, and allow you to leverage the Compose runtime, but you have to bind to platform specific UI elements. Because of this additional work, the developer friction will most likely prevent broader adoption.

Convergence on Compose Multiplatform UI

Jetpack Compose for Android had incredible investment for multiple years and created really solid foundational UI components. The goal is to reuse code that is already written for Jetpack Compose, and bring it to other platforms. That’s why JetBrains uses the same package and classnames in compose-multiplatform. These implementations of compose-multiplatform enable Kotlin Multiplatform UI for Desktop, iOS and now Web (WASM).

These additional (non-Android) multiplatform implementations render to a 2 Dimensional (2D) canvas using SKIA (similar technique used by Flutter). Note: As of very recently, Flutter now leverages the Impeller as the 2D rendering for better performance.

Because a developer can now code against a single set of Compose APIs that can render UIs across multiple platforms, the value is there to start seeing more use cases for Compose Multiplatform UI.

What is Compose for Web (WASM)?

Compose for Web (WASM) leverages Web-Assembly (WASM) to run native binaries in the browser. For browser based UIs, this is a very real future as it’s already supported in Chrome, Safari, Firefox and more. For myself (someone who does Compose UI development for Android), Compose for Web allows me to re-use my existing skills, and create UIs that can be shared via a URL. Not only that, but it is code that will be familiar to other developers on my team, and therefore make it more approachable and have better chance for success.

This Compose for Web technology is current incubating, but is really powerful. Run the samples and check it out, but there are animations and images, and gradients. It’s beautiful.

Getting a mobile developer to learn React requires a full paradigm shift and isn’t scalable. The same could be said about shifting to coding in Swift and iOS. Being able to create a consistent set of solutions on a single technology will drive adoption.

Why Compose for Web (WASM)?

I think this will be big for Kotlin developers to get their code running in a browser. I am currently building developer tools for small projects, and making them accessible to everyone in an organization is so much easier to do via a web url instead of having to download an APK and run it.

I have always wanted to share a design system library with others in an organization via a URL. That friction for installing an application is just so cumbersome.

Additionally, we can start to do many test/run cycles in the browser if that test/debug cycle story gets better. I already do that today with Compose for Desktop as it removes the need for a device at all. Android Studio’s Compose previews work great in some use cases, but other times it is nicer to run a bit more code via Compose for Desktop.

With WASM, we will get native performance with this technology, and soon be able to access system level APIs to make very powerful applications.

Next: Under The Surface of Compose for Web (WASM)

I want to dive deeper into Compose for Web (WASM). I’ve been running the samples locally and dived into the generated html, js and wasm files generated by the Compose for Web implementation. As I learn more I’ll share what I learn.

Other related articles I found after writing this:

• The huge potential of Kotlin/Wasm
• https://news.ycombinator.com/item?id=34779746
• https://www.reddit.com/r/Kotlin/comments/12nar6u/on_the_compose_html_rebranding_tldr_everything_is/

To be continued…

As the documentation says, you can combine both Espresso and Compose in an Android instrumentation test. In order to interact with Compose in an instrumentation test you need a ComposeTestRule.

Problem

Typically you would create a ComposeTestRule with createComposeRule() in a part of your app that is compose only, but that will create a blank ComponentActivity and launch it showing a blank screen.

@get:Rule
val composeTestRule = createComposeRule()

This is great if you are looking to just use composeTestRule.setContent { Text("Hi")} in your test, but if you are integrating with an existing Espresso test, this will not be the case.

You could use the createAndroidComposeRule<MyActivity>(), however that will also use an ActivityTestRule underneath the hood and launch the Activity, which will change the behavior of your existing test. 🤔

@get:Rule
val composeTestRule = createAndroidComposeRule<MyActivity>()

Solution

If all you want to do is keep the Espresso test the same way it is, but also interact with some compose elements, use createEmptyComposeRule() and it will all work! 🎉

@get:Rule
val composeTestRule = createEmptyComposeRule()

Conclusion

Now you can interact with compose elements along with view elements, exactly like the documentation says. 😃

composeTestRule.onNodeWithText("Something").assertIsDisplayed()

Kotlin companion objects allow you to add static data and methods associated with a class. This is similar to how Java has static fields and methods. The problem is that Java doesn’t really know what a companion object it, so trying to access one using standard Java reflection might make you go crazy. 🤪

package com.handstandsam

/** This is contrived example of a companion object */
class SpecialFeature {
  companion object {
    var enabled: Boolean = false
  }
}

The decompiled Java class (created by the Kotlin Compiler) results in this:

package com.handstandsam;

import kotlin.Metadata;
import kotlin.jvm.internal.DefaultConstructorMarker;
import org.jetbrains.annotations.NotNull;

public final class SpecialFeature {
   private static boolean enabled;

   @NotNull
   public static final Companion Companion = new Companion((DefaultConstructorMarker)null);

   public static final class Companion {
      public final boolean getEnabled() {
         return SpecialFeature.enabled;
      }

      public final void setEnabled(boolean var1) {
         SpecialFeature.enabled = var1;
      }

      private Companion() {
      }

      public Companion(DefaultConstructorMarker $constructor_marker) {
         this();
      }
   }
}

The Kotlin Standard Lib for Java has a really cool method called companionObjectInstance that allows you to grab an instance of the declared companion object from the KClass object.

Why is companionObjectInstance helpful?

When Kotlin is compiled to Java Class files, the companion object has a fully qualified class name of com.handstandsam.SpecialFeature$Companion.

Mapping Kotlin -> Java Byte Code can make your head hurt, so by using this companionObjectInstance helper method, we don’t have to figure out how to get an instance of the companion object, or figure out the fully qualified class name.

val companionObjectJavaClass = com.handstandsam.SpecialFeature::class.java

val companionObjectInstance = companionObjectJavaClass.kotlin
        .companionObjectInstance!!

Now that we have an instance of the companion object class, and know the Java class, we can use reflection to set the value of the enabled property on the companion object.

companionObjectInstance::class.java
    .methods
    .first { it.name == "setEnabled" }
    .invoke(companionObjectInstance, true)

Note: setEnabled is the name, and it is a method here. You might expect this to just be a property which is what I assumed, but when compiled to java byte code, it is marked private and has a getter and a setter.

Bonus: Accessing private properties using Java Reflection

You could alternatively use Java reflection to change the backing private static boolean enabled field directly if you choose.

If you wanted to set the private static field value itself, rather than calling the setter, you can grab the declared field, and set it to accessible which allows us to bypass the private visibility. This sort of thing is why the JVM can’t be considered secure as it can be modified at runtime.

val privateEnabledField = SpecialFeature::class.java.getDeclaredField("enabled")
privateEnabledField.isAccessible=true
privateEnabledField.set(companionObjectInstance, true)

Conclusion

Reflection is powerful, but confusing. I could have probably done this cleaner JUST using Kotlin Reflect and not Java Reflection, but in my case I wanted to use Java Reflection, but needed to interact with a Kotlin companion object. There is a lot of documentation on how to mix Kotlin + Reflection, so feel free to read up more there. Cheers!