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What is Cypress Studio?

What is Cypress Studio?

Introduction

Cypress Studio has been around for some time. It was introduced in Cypress v6.3.0, removed in v10, and reintroduced in v10.7.0. This blog post will dive into its current state and how to use it today.

What is Cypress Studio

Cypress Studio is a tool built on top of Cypress. It offers an interface for creating tests as if you were using your site without requiring code to be written. It adds functionality to query elements and to add assertions from the interface. It's important to note that Cypress Studio is still under an experimental flag and lacks support for Component Testing (another feature of Cypress).

Creating an app to test

We first need a working app because the tool we will use is meant for end-to-end tests. I'll pick Svelte and Vite for this demo, but you can choose any framework. Cypress is agnostic in that matter.

npm create vite@latest cypress-studio-svelte

Make sure to select the Svelte and Typescript options to follow along.

term-1terminal

Now open your project location, install dependencies, and start your app to ensure everything works correctly.

cd cypress-studio-svelte
npm i
npm run dev --open
app

Installing and setting up Cypress Studio

Now it's time to add Cypress to our dev dependencies.

npm i -D cypress

With our dependency installed, let's launch it so we can start configuring it. But first let’s add an entry under scripts in our package.json file.

// ...
    "scripts": {
		// ...
		"cypress": "cypress"
	}
// ...
npm run cypress open

A new window will open that will guide us through the initial setup.

welcome to cypress

Select the "E2E Testing" option to create the required configuration files. Then, you can close the window. A new folder called cypress and a configuration file cypress.config.ts are created at the root of our project.

To enable Cypress Studio, we must open the newly created configuration file and add the experimentalStudio property.

import { defineConfig } from "cypress";

export default defineConfig({
  e2e: {
	experimentalStudio: true // add this option!
  },
});

For this project, we need to ensure that TypeScript is configured properly for Cypress when it runs, as it conflicts with the one in our root folder. We will extend the original typescript configuration and override some properties. Create a new tsconfig.json file inside the' cypress' folder.

{
    "extends": "../tsconfig.json",
    "include": [
      "../node_modules/cypress",
      "**/*.cy.ts"
    ],
    "exclude": [],
    "compilerOptions": {
      "noEmit": false,
      "sourceMap": false,
      "target": "es5",
      "lib": ["es5", "dom"],
      "types": ["cypress"]
    }
}

You can find more information on setting up TypeScript with Cypress in their documentation

Creating tests

Now that our setup is complete let's write our first test. We will create a folder called e2e and a new test file.

	mkdir cypress/e2e
	touch cypress/e2e/home.cy.ts

Open the new file and create an outline of your tests. Don't add anything to them. I initialized my file with a few tests.

describe("Home Page", () => {
    it("there's a button with a counter",  () => {

    });

    it("when clicking on the counter button, the count increases", ()=> {

    });

    it("when reloading the page, the count is reset to 0", () => {

    });
})

There is an option to create spec files from the interface, but it will contain some initial content that you most likely remove. There’s third option to scaffold multiple specs files, these can serve as a learning resource for writing tests as it contains many different scenarios tested.

Make sure your app is running and, in another terminal, start cypress.

npm run cypress open

Select E2E testing and Chrome. You should now see a list with all your test files.

cy-tests-list

Click on home.cy.ts. Your tests will run and pass because we have not made any assertions.

green-no-assert

This looks the same as if we haven't enabled Studio, but there's a new detail added when you hover on any of the tests: a magic wand that you can click to start recording events and adding assertions directly in the UI.

Let's start with our first tests and check what assertions we can make. The first step is to navigate to a page. This interaction will be recorded and added to the test. To make assertions, right-click on an element and select the ones you want to add.

vid1

After we complete our test, let's review our generated code. Go to the test file and see the changes made to it. It should look something like this.

it("there's a button with a counter",  () => {
    /* ==== Generated with Cypress Studio ==== */
    cy.visit('localhost:5173/');
    cy.get('button').should('be.visible');
    cy.get('button').should('be.enabled');
    cy.get('button').should('have.text', 'count is 0');
    /* ==== End Cypress Studio ==== */
});

Cypress Studio will attempt to pick the best selector for the element. In this case, it was able to choose button because it is unique to the page. If there were more buttons, the selector would've been different.

You can interact with elements as if you were using the page regularly. Note that Studio supports a limited set of commands: check, click, select, type, and uncheck.

Let's record our second test and verify that the button will increase its count when clicked.

vid2

That was quick. Let's review our generated test.

 it("when clicking on the counter button, the count increases", ()=> {
    /* ==== Generated with Cypress Studio ==== */
    cy.visit('localhost:5173/');
    cy.get('button').should('have.text', 'count is 0');
    cy.get('button').click();
    cy.get('button').should('have.text', 'count is 1');
    cy.get('button').click();
    cy.get('button').should('have.text', 'count is 2');
    cy.get('button').click();
    cy.get('button').should('have.text', 'count is 3');
    /* ==== End Cypress Studio ==== */
});

So far, our tests have been very accurate and didn't need any modifications. However, take Studio as a helper to write tests visually and their selector as suggestions. Let's take the bottom link with the text "SvelteKit" as an example.

skitlink

It will generate the assertion:

cy.get(':nth-child(4) > .s-XsEmFtvddWTw').should('have.text', 'SvelteKit');

This selector is accurate, but modifying the elements' structure or order would break the tests.

We don’t want tests to break for reasons that do not affect our users. A change in order does not have the same impact for the user as changing the text of a button or changing the url of a link. These selectors should be as specific as possible, but rely as less as possible on implementation details. Selecting images by its alt attribute it’s a way to find a specific element and at the same time ensure that element is accessible. (Accessibility has a direct impact on users)

As a counterpart, when clicking any of the logos above, we will get very interesting selectors (and assertions).

cy.get('[href="https://vitejs.dev"] > .logo').should('have.attr', 'alt', 'Vite Logo');
cy.get('[href="https://svelte.dev"] > .logo').should('have.attr', 'alt', 'Svelte Logo');

With these assertions, we check that our images have an alt attribute set and are tied to a specified link.

Conclusion

As with any other automated tool, check the output and solve any issues that you may find. Results depend on the structure of the page and Cypress Studio's ability to choose an adequate selector. Overall, this tool can still help you write complex tests with plenty of interactions, and you can later modify any selectors. Besides the selector, I found it helpful in writing the assertions. If you’re new to Cypress or e2e testing, it can be of great help to go from an idea to a working test. Reviewing these tests can be a great way to learn how these tests are built.

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This might involve: - Reproducing the environment: Set up a staging/local system that closely mimics production. For instance with the same cache layer configurations. - Varying one condition at a time: For example, only adjust cache invalidation policies or TTLs and then observe how data freshness changes. - Monitoring metrics: In our example, such monitoring would involve tracking user profile updates, cache hits/misses, and response times. These metrics should lead to confirming or rejecting your explanation. These plans become your blueprint for experiments in further debugging stages. Collecting and Evaluating Evidence After formulating a clear, testable hypothesis, the next crucial step is to gather data that can either support or refute it. This mirrors how scientists collect observations in a literature review or initial experiments. 1. Identify "Primary Sources" (Logs, Stack Traces, Code History): - Logs and Stack Traces: These are your direct pieces of evidence - treat them like raw experimental data. For instance, look closely at timestamps, caching-related events (e.g., invalidation triggers), and any error messages related to stale reads. - Code History: Look for related changes in your source control, e.g. using Git bisect. In our example, we would look for changes to caching mechanisms or references to cache libraries in commits, which could pinpoint when the inconsistency was introduced. Sometimes, reverting a commit that altered cache settings helps confirm whether the bug originated there. 2. Corroborate with "Secondary Sources" (Documentation, Q&A Forums): - Documentation: Check official docs for known behavior or configuration details that might differ from your assumptions. - Community Knowledge: Similar issues reported on GitHub or StackOverflow may reveal known pitfalls in a library you're using. 3. Assess Data Quality and Relevance: - Look for Patterns: For instance, does stale data appear only after certain update frequencies or at specific times of day? - Check Environmental Factors: For instance, does the bug happen only with particular deployment setups, container configurations, or memory constraints? - Watch Out for Biases: Avoid seeking only the data that confirms your hypothesis. Look for contradictory logs or metrics that might point to other root causes. You keep your hypothesis grounded in real-world system behavior by treating logs, stack traces, and code history as primary data - akin to raw experimental results. This evidence-first approach reduces guesswork and guides more precise experiments. Designing and Running Experiments With a hypothesis in hand and evidence gathered, it's time to test it through controlled experiments - much like scientists isolate variables to verify or debunk an explanation. 1. Set Up a Reproducible Environment: - Testing Environments: Replicate production conditions as closely as possible. In our example, that would involve ensuring the same caching configuration, library versions, and relevant data sets are in place. - Version Control Branches: Use a dedicated branch to experiment with different settings or configuration, e.g., cache invalidation strategies. This streamlines reverting changes if needed. 2. Control Variables One at a Time: - For instance, if you suspect data inconsistency is tied to cache invalidation events, first adjust only the invalidation timeout and re-test. - Or, if concurrency could be a factor (e.g., multiple requests updating user data simultaneously), test different concurrency levels to see if stale data issues become more pronounced. 3. Measure and Record Outcomes: - Automated Tests: Tests provide a great way to formalize and verify your assumptions. For instance, you could develop tests that intentionally update user profiles and check if the displayed data matches the latest state. - Monitoring Tools: Monitor relevant metrics before, during, and after each experiment. In our example, we might want to track cache hit rates, TTL durations, and query times. - Repeat Trials: Consistency across multiple runs boosts confidence in your findings. 4. Validate Against a Baseline: - If baseline tests manifest normal behavior, but your experimental changes manifest the bug, you've isolated the variable causing the issue. E.g. if the baseline tests show that data is consistently fresh under normal caching conditions but your experimental changes cause stale data. - Conversely, if your change eliminates the buggy behavior, it supports your hypothesis - e.g. that the cache configuration was the root cause. Each experiment outcome is a data point supporting or contradicting your hypothesis. Over time, these data points guide you toward the true cause. Analyzing Results and Iterating In scientific debugging, an unexpected result isn't a failure - it's valuable feedback that brings you closer to the right explanation. 1. Compare Outcomes to the hypothesis. For instance: - Did user data stay consistent after you reduced the cache TTL or fixed invalidation logic? - Did logs show caching events firing as expected, or did they reveal unexpected errors? - Are there only partial improvements that suggest multiple overlapping issues? 2. Incorporate Unexpected Observations: - Sometimes, debugging uncovers side effects - e.g. performance bottlenecks exposed by more frequent cache invalidations. Note these for future work. - If your hypothesis is disproven, revise it. For example, the cache may only be part of the problem, and a separate load balancer setting also needs attention. 3. Avoid Confirmation Bias: - Don't dismiss contrary data. For instance, if you see evidence that updates are fresh in some modules but stale in others, you may have found a more nuanced root cause (e.g., partial cache invalidation). - Consider other credible explanations if your teammates propose them. Test those with the same rigor. 4. Decide If You Need More Data: - If results aren't conclusive, add deeper instrumentation or enable debug modes to capture more detailed logs. - For production-only issues, implement distributed tracing or sampling logs to diagnose real-world usage patterns. 5. Document Each Iteration: - Record the results of each experiment, including any unexpected findings or new hypotheses that arise. - Through iterative experimentation and analysis, each cycle refines your understanding. By letting evidence shape your hypothesis, you ensure that your final conclusion aligns with reality. Implementing and Verifying the Fix Once you've identified the likely culprit - say, a misconfigured or missing cache invalidation policy - the next step is to implement a fix and verify its resilience. 1. Implementing the Change: - Scoped Changes: Adjust just the component pinpointed in your experiments. Avoid large-scale refactoring that might introduce other issues. - Code Reviews: Peer reviews can catch overlooked logic gaps or confirm that your changes align with best practices. 2. Regression Testing: - Re-run the same experiments that initially exposed the issue. In our stale data example, confirm that the data remains fresh under various conditions. - Conduct broader tests - like integration or end-to-end tests - to ensure no new bugs are introduced. 3. Monitoring in Production: - Even with positive test results, real-world scenarios can differ. Monitor logs and metrics (e.g. cache hit rates, user error reports) closely post-deployment. - If the buggy behavior reappears, revisit your hypothesis or consider additional factors, such as unpredicted user behavior. 4. Benchmarking and Performance Checks (If Relevant): - When making changes that affect the frequency of certain processes - such as how often a cache is refreshed - be sure to measure the performance impact. Verify you meet any latency or resource usage requirements. - Keep an eye on the trade-offs: For instance, more frequent cache invalidations might solve stale data but could also raise system load. By systematically verifying your fix - similar to confirming experimental results in research - you ensure that you've addressed the true cause and maintained overall software stability. Documenting the Debugging Process Good science relies on transparency, and so does effective debugging. Thorough documentation guarantees your findings are reproducible and valuable to future team members. 1. Record Your Hypothesis and Experiments: - Keep a concise log of your main hypothesis, the tests you performed, and the outcomes. - A simple markdown file within the repo can capture critical insights without being cumbersome. 2. Highlight Key Evidence and Observations: - Note the logs or metrics that were most instrumental - e.g., seeing repeated stale cache hits 10 minutes after updates. - Document any edge cases discovered along the way. 3. List Follow-Up Actions or Potential Risks: - If you discover additional issues - like memory spikes from more frequent invalidation - note them for future sprints. - Identify parts of the code that might need deeper testing or refactoring to prevent similar issues. 4. Share with Your Team: - Publish your debugging report on an internal wiki or ticket system. A well-documented troubleshooting narrative helps educate other developers. - Encouraging open discussion of the debugging process fosters a culture of continuous learning and collaboration. By paralleling scientific publication practices in your documentation, you establish a knowledge base to guide future debugging efforts and accelerate collective problem-solving. Conclusion Debugging can be as much a rigorous, methodical exercise as an art shaped by intuition and experience. By adopting the principles of scientific inquiry - forming hypotheses, designing controlled experiments, gathering evidence, and transparently documenting your process - you make your debugging approach both systematic and repeatable. The explicitness and structure of scientific debugging offer several benefits: - Better Root-Cause Discovery: Structured, hypothesis-driven debugging sheds light on the _true_ underlying factors causing defects rather than simply masking symptoms. - Informed Decisions: Data and evidence lead the way, minimizing guesswork and reducing the chance of reintroducing similar issues. - Knowledge Sharing: As in scientific research, detailed documentation of methods and outcomes helps others learn from your process and fosters a collaborative culture. Ultimately, whether you are diagnosing an intermittent crash or chasing elusive performance bottlenecks, scientific debugging brings clarity and objectivity to your workflow. By aligning your debugging practices with the scientific method, you build confidence in your solutions and empower your team to tackle complex software challenges with precision and reliability. But most importantly, do not get discouraged by the number of rigorous steps outlined above or by the fact you won't always manage to follow them all religiously. Debugging is a complex and often frustrating process, and it's okay to rely on your intuition and experience when needed. Feel free to adapt the debugging process to your needs and constraints, and as long as you keep the scientific mindset at heart, you'll be on the right track....

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