Refactoring with Codemods to Automate API Adjustments

Clear a Stale Function Toggle

Let’s begin with a easy but sensible instance to display the
energy of codemods. Think about you’re utilizing a function
toggle in your
codebase to manage the discharge of unfinished or experimental options.
As soon as the function is stay in manufacturing and dealing as anticipated, the following
logical step is to wash up the toggle and any associated logic.

For example, take into account the next code:

const knowledge = featureToggle('feature-new-product-list') ? { identify: 'Product' } : undefined;

As soon as the function is absolutely launched and not wants a toggle, this
could be simplified to:

const knowledge = { identify: 'Product' };

The duty entails discovering all situations of featureToggle within the
codebase, checking whether or not the toggle refers to
feature-new-product-list, and eradicating the conditional logic surrounding
it. On the identical time, different function toggles (like
feature-search-result-refinement, which can nonetheless be in growth)
ought to stay untouched. The codemod must perceive the construction
of the code to use adjustments selectively.

Understanding the AST

Earlier than we dive into writing the codemod, let’s break down how this
particular code snippet appears to be like in an AST. You should utilize instruments like AST
Explorer to visualise how supply code and AST
are mapped. It’s useful to know the node sorts you are interacting
with earlier than making use of any adjustments.

The picture beneath reveals the syntax tree by way of ECMAScript syntax. It
comprises nodes like Identifier (for variables), StringLiteral (for the
toggle identify), and extra summary nodes like CallExpression and
ConditionalExpression.

Determine 2: The Summary Syntax Tree illustration of the function toggle examine

On this AST illustration, the variable knowledge is assigned utilizing a
ConditionalExpression. The take a look at a part of the expression calls
featureToggle('feature-new-product-list'). If the take a look at returns true,
the consequent department assigns { identify: 'Product' } to knowledge. If
false, the alternate department assigns undefined.

For a activity with clear enter and output, I want writing assessments first,
then implementing the codemod. I begin by defining a detrimental case to
guarantee we don’t unintentionally change issues we need to go away untouched,
adopted by an actual case that performs the precise conversion. I start with
a easy state of affairs, implement it, then add a variation (like checking if
featureToggle is known as inside an if assertion), implement that case, and
guarantee all assessments move.

This strategy aligns properly with Take a look at-Pushed Improvement (TDD), even
in case you don’t observe TDD usually. Figuring out precisely what the
transformation’s inputs and outputs are earlier than coding improves security and
effectivity, particularly when tweaking codemods.

With jscodeshift, you possibly can write assessments to confirm how the codemod
behaves:

const rework = require("../remove-feature-new-product-list");

defineInlineTest(
  rework,
  {},
  `
  const knowledge = featureToggle('feature-new-product-list') ? { identify: 'Product' } : undefined;
  `,
  `
  const knowledge = { identify: 'Product' };
  `,
  "delete the toggle feature-new-product-list in conditional operator"
);

The defineInlineTest perform from jscodeshift lets you outline
the enter, anticipated output, and a string describing the take a look at’s intent.
Now, operating the take a look at with a standard jest command will fail as a result of the
codemod isn’t written but.

The corresponding detrimental case would make sure the code stays unchanged
for different function toggles:

defineInlineTest(
  rework,
  {},
  `
  const knowledge = featureToggle('feature-search-result-refinement') ? { identify: 'Product' } : undefined;
  `,
  `
  const knowledge = featureToggle('feature-search-result-refinement') ? { identify: 'Product' } : undefined;
  `,
  "don't change different function toggles"
);

Writing the Codemod

Let’s begin by defining a easy rework perform. Create a file
known as rework.js with the next code construction:

module.exports = perform(fileInfo, api, choices) {
  const j = api.jscodeshift;
  const root = j(fileInfo.supply);

  // manipulate the tree nodes right here

  return root.toSource();
};

This perform reads the file right into a tree and makes use of jscodeshift’s API to
question, modify, and replace the nodes. Lastly, it converts the AST again to
supply code with .toSource().

Now we will begin implementing the rework steps:

1. Discover all situations of featureToggle.
2. Confirm that the argument handed is 'feature-new-product-list'.
3. Change the whole conditional expression with the consequent half,
   successfully eradicating the toggle.

Right here’s how we obtain this utilizing jscodeshift:

module.exports = perform (fileInfo, api, choices) {
  const j = api.jscodeshift;
  const root = j(fileInfo.supply);

  // Discover ConditionalExpression the place the take a look at is featureToggle('feature-new-product-list')
  root
    .discover(j.ConditionalExpression, {
      take a look at: {
        callee: { identify: "featureToggle" },
        arguments: [{ value: "feature-new-product-list" }],
      },
    })
    .forEach((path) => {
      // Change the ConditionalExpression with the 'consequent'
      j(path).replaceWith(path.node.consequent);
    });

  return root.toSource();
};

The codemod above:

• Finds ConditionalExpression nodes the place the take a look at calls
  featureToggle('feature-new-product-list').
• Replaces the whole conditional expression with the resultant (i.e., {
identify: 'Product' }), eradicating the toggle logic and leaving simplified code
  behind.

This instance demonstrates how simple it’s to create a helpful
transformation and apply it to a big codebase, considerably lowering
handbook effort.

You’ll want to write down extra take a look at circumstances to deal with variations like
if-else statements, logical expressions (e.g.,
!featureToggle('feature-new-product-list')), and so forth to make the
codemod strong in real-world eventualities.

As soon as the codemod is prepared, you possibly can check it out on a goal codebase,
such because the one you are engaged on. jscodeshift gives a command-line
instrument that you should use to use the codemod and report the outcomes.

$ jscodeshift -t transform-name src/

After validating the outcomes, examine that every one useful assessments nonetheless
move and that nothing breaks—even in case you’re introducing a breaking change.
As soon as glad, you possibly can commit the adjustments and lift a pull request as
a part of your regular workflow.

Codemods Enhance Code High quality and Maintainability

Codemods aren’t simply helpful for managing breaking API adjustments—they’ll
considerably enhance code high quality and maintainability. As codebases
evolve, they usually accumulate technical debt, together with outdated function
toggles, deprecated strategies, or tightly coupled parts. Manually
refactoring these areas could be time-consuming and error-prone.

By automating refactoring duties, codemods assist hold your codebase clear
and freed from legacy patterns. Frequently making use of codemods lets you
implement new coding requirements, take away unused code, and modernize your
codebase with out having to manually modify each file.

Refactoring an Avatar Part

Now, let’s take a look at a extra advanced instance. Suppose you’re working with
a design system that features an Avatar part tightly coupled with a
Tooltip. Every time a person passes a identify prop into the Avatar, it
routinely wraps the avatar with a tooltip.

Determine 3: A avatar part with a tooltip

Right here’s the present Avatar implementation:

import { Tooltip } from "@design-system/tooltip";

const Avatar = ({ identify, picture }: AvatarProps) => {
  if (identify) {
    return (
      <Tooltip content material={identify}>
        <CircleImage picture={picture} />
      </Tooltip>
    );
  }

  return <CircleImage picture={picture} />;
};

The objective is to decouple the Tooltip from the Avatar part,
giving builders extra flexibility. Builders ought to be capable of resolve
whether or not to wrap the Avatar in a Tooltip. Within the refactored model,
Avatar will merely render the picture, and customers can apply a Tooltip
manually if wanted.

Right here’s the refactored model of Avatar:

const Avatar = ({ picture }: AvatarProps) => {
  return <CircleImage picture={picture} />;
};

Now, customers can manually wrap the Avatar with a Tooltip as
wanted:

import { Tooltip } from "@design-system/tooltip";
import { Avatar } from "@design-system/avatar";

const UserProfile = () => {
  return (
    <Tooltip content material="Juntao Qiu">
      <Avatar picture="/juntao.qiu.avatar.png" />
    </Tooltip>
  );
};

The problem arises when there are a whole lot of Avatar usages unfold
throughout the codebase. Manually refactoring every occasion can be extremely
inefficient, so we will use a codemod to automate this course of.

Utilizing instruments like AST Explorer, we will
examine the part and see which nodes symbolize the Avatar utilization
we’re concentrating on. An Avatar part with each identify and picture props
is parsed into an summary syntax tree as proven beneath:

Determine 4: AST of the Avatar part utilization

Writing the Codemod

Let’s break down the transformation into smaller duties:

• Discover Avatar utilization within the part tree.
• Verify if the identify prop is current.
• If not, do nothing.
• If current:
• Create a Tooltip node.
• Add the identify to the Tooltip.
• Take away the identify from Avatar.
• Add Avatar as a toddler of the Tooltip.
• Change the unique Avatar node with the brand new Tooltip.

To start, we’ll discover all situations of Avatar (I’ll omit among the
assessments, however it’s best to write comparability assessments first).

defineInlineTest(
    { default: rework, parser: "tsx" },
    {},
    `
    <Avatar identify="Juntao Qiu" picture="/juntao.qiu.avatar.png" />
    `,
    `
    <Tooltip content material="Juntao Qiu">
      <Avatar picture="/juntao.qiu.avatar.png" />
    </Tooltip>
    `,
    "wrap avatar with tooltip when identify is supplied"
  );

Just like the featureToggle instance, we will use root.discover with
search standards to find all Avatar nodes:

root
  .discover(j.JSXElement, {
    openingElement: { identify: { identify: "Avatar" } },
  })
  .forEach((path) => {
    // now we will deal with every Avatar occasion
  });

Subsequent, we examine if the identify prop is current:

root
  .discover(j.JSXElement, {
    openingElement: { identify: { identify: "Avatar" } },
  })
  .forEach((path) => {
    const avatarNode = path.node;

    const nameAttr = avatarNode.openingElement.attributes.discover(
      (attr) => attr.identify.identify === "identify"
    );

    if (nameAttr) {
      const tooltipElement = createTooltipElement(
        nameAttr.worth.worth,
        avatarNode
      );
      j(path).replaceWith(tooltipElement);
    }
  });

For the createTooltipElement perform, we use the
jscodeshift API to create a brand new JSX node, with the identify
prop utilized to the Tooltip and the Avatar
part as a toddler. Lastly, we name replaceWith to
substitute the present path.

Right here’s a preview of the way it appears to be like in
Hypermod, the place the codemod is written on
the left. The highest half on the correct is the unique code, and the underside
half is the reworked end result:

Determine 5: Run checks inside hypermod earlier than apply it to your codebase

This codemod searches for all situations of Avatar. If a
identify prop is discovered, it removes the identify prop
from Avatar, wraps the Avatar inside a
Tooltip, and passes the identify prop to the
Tooltip.

By now, I hope it’s clear that codemods are extremely helpful and
that the workflow is intuitive, particularly for large-scale adjustments the place
handbook updates can be an enormous burden. Nonetheless, that is not the entire
image. Within the subsequent part, I’ll make clear among the challenges
and the way we will tackle these less-than-ideal elements.

Leveraging Supply Graphs and Take a look at-Pushed Codemods

To deal with these complexities, codemods ought to be used alongside different
methods. For example, a couple of years in the past, I participated in a design
system parts rewrite venture at Atlassian. We addressed this difficulty by
first looking out the supply graph, which contained nearly all of inside
part utilization. This allowed us to know how parts have been used,
whether or not they have been imported beneath completely different names, or whether or not sure
public props have been often used. After this search section, we wrote our
take a look at circumstances upfront, guaranteeing we coated nearly all of use circumstances, and
then developed the codemod.

In conditions the place we could not confidently automate the improve, we
inserted feedback or “TODOs” on the name websites. This allowed the
builders operating the script to deal with particular circumstances manually. Often,
there have been solely a handful of such situations, so this strategy nonetheless proved
useful for upgrading variations.

Using Present Code Standardization Instruments

As you possibly can see, there are many edge circumstances to deal with, particularly in
codebases past your management—resembling exterior dependencies. This
complexity signifies that utilizing codemods requires cautious supervision and a
evaluate of the outcomes.

Nonetheless, in case your codebase has standardization instruments in place, resembling a
linter that enforces a selected coding fashion, you possibly can leverage these
instruments to scale back edge circumstances. By imposing a constant construction, instruments
like linters assist slim down the variations in code, making the
transformation simpler and minimizing sudden points.

For example, you may use linting guidelines to limit sure patterns,
resembling avoiding nested conditional (ternary) operators or imposing named
exports over default exports. These guidelines assist streamline the codebase,
making codemods extra predictable and efficient.

Moreover, breaking down advanced transformations into smaller, extra
manageable ones lets you deal with particular person points extra exactly. As
we’ll quickly see, composing smaller codemods could make dealing with advanced
adjustments extra possible.

Codemod Composition

Let’s revisit the function toggle elimination instance mentioned earlier. Within the code snippet
we’ve a toggle known as feature-convert-new have to be eliminated:

import { featureToggle } from "./utils/featureToggle";

const convertOld = (enter: string) => {
  return enter.toLowerCase();
};

const convertNew = (enter: string) => {
  return enter.toUpperCase();
};

const end result = featureToggle("feature-convert-new")
  ? convertNew("Howdy, world")
  : convertOld("Howdy, world");

console.log(end result);

The codemod for take away a given toggle works high-quality, and after operating the codemod,
we wish the supply to seem like this:

const convertNew = (enter: string) => {
  return enter.toUpperCase();
};

const end result = convertNew("Howdy, world");

console.log(end result);

Nonetheless, past eradicating the function toggle logic, there are further duties to
deal with:

• Take away the unused convertOld perform.
• Clear up the unused featureToggle import.

In fact, you may write one large codemod to deal with all the things in a
single move and take a look at it collectively. Nonetheless, a extra maintainable strategy is
to deal with codemod logic like product code: break the duty into smaller,
impartial items—identical to how you’ll usually refactor manufacturing
code.

Breaking It Down

We are able to break the massive transformation down into smaller codemods and
compose them. The benefit of this strategy is that every transformation
could be examined individually, masking completely different circumstances with out interference.
Furthermore, it lets you reuse and compose them for various
functions.

For example, you may break it down like this:

• A metamorphosis to take away a selected function toggle.
• One other transformation to wash up unused imports.
• A metamorphosis to take away unused perform declarations.

By composing these, you possibly can create a pipeline of transformations:

import { removeFeatureToggle } from "./remove-feature-toggle";
import { removeUnusedImport } from "./remove-unused-import";
import { removeUnusedFunction } from "./remove-unused-function";

import { createTransformer } from "./utils";

const removeFeatureConvertNew = removeFeatureToggle("feature-convert-new");

const rework = createTransformer([
  removeFeatureConvertNew,
  removeUnusedImport,
  removeUnusedFunction,
]);

export default rework;

On this pipeline, the transformations work as follows:

1. Take away the feature-convert-new toggle.
2. Clear up the unused import assertion.
3. Take away the convertOld perform because it’s not used.

Determine 6: Compose transforms into a brand new rework

You may also extract further codemods as wanted, combining them in
varied orders relying on the specified final result.

Determine 7: Put completely different transforms right into a pipepline to type one other rework

The createTransformer Operate

The implementation of the createTransformer perform is comparatively
easy. It acts as a higher-order perform that takes an inventory of
smaller rework features, iterates by means of the checklist to use them to
the foundation AST, and eventually converts the modified AST again into supply
code.

import { API, Assortment, FileInfo, JSCodeshift, Choices } from "jscodeshift";

kind TransformFunction = { (j: JSCodeshift, root: Assortment): void };

const createTransformer =
  (transforms: TransformFunction[]) =>
  (fileInfo: FileInfo, api: API, choices: Choices) => {
    const j = api.jscodeshift;
    const root = j(fileInfo.supply);

    transforms.forEach((rework) => rework(j, root));
    return root.toSource(choices.printOptions || { quote: "single" });
  };

export { createTransformer };

For instance, you may have a rework perform that inlines
expressions assigning the function toggle name to a variable, so in later
transforms you don’t have to fret about these circumstances anymore:

const shouldEnableNewFeature = featureToggle('feature-convert-new');

if (!shouldEnableNewFeature && someOtherLogic) {
  //...
}

Turns into this:

if (!featureToggle('feature-convert-new') && someOtherLogic) {
  //...
}

Over time, you may construct up a set of reusable, smaller
transforms, which may tremendously ease the method of dealing with tough edge
circumstances. This strategy proved extremely efficient in our work refining design
system parts. As soon as we transformed one package deal—such because the button
part—we had a couple of reusable transforms outlined, like including feedback
in the beginning of features, eradicating deprecated props, or creating aliases
when a package deal is already imported above.

Every of those smaller transforms could be examined and used independently
or mixed for extra advanced transformations, which accelerates subsequent
conversions considerably. Consequently, our refinement work grew to become extra
environment friendly, and these generic codemods are actually relevant to different inside
and even exterior React codebases.

Since every rework is comparatively standalone, you possibly can fine-tune them
with out affecting different transforms or the extra advanced, composed ones. For
occasion, you may re-implement a rework to enhance efficiency—like
lowering the variety of node-finding rounds—and with complete take a look at
protection, you are able to do this confidently and safely.

Utilizing JavaParser in a Java Codebase

JavaParser could be helpful for making breaking API adjustments or refactoring
giant Java codebases in a structured, automated manner.

Assume we’ve the next code in FeatureToggleExample.java, which
checks the toggle feature-convert-new and branches accordingly:

public class FeatureToggleExample {
    public void execute() {
        if (FeatureToggle.isEnabled("feature-convert-new")) {
          newFeature();
        } else {
          oldFeature();
        }
    }

    void newFeature() {
        System.out.println("New Function Enabled");
    }

    void oldFeature() {
        System.out.println("Previous Function");
    }
}

We are able to outline a customer to search out if statements checking for
FeatureToggle.isEnabled, after which substitute them with the corresponding
true department—much like how we dealt with the function toggle codemod in
JavaScript.

// Customer to take away function toggles
class FeatureToggleVisitor extends VoidVisitorAdapter<Void> {
    @Override
    public void go to(IfStmt ifStmt, Void arg) {
        tremendous.go to(ifStmt, arg);
        if (ifStmt.getCondition().isMethodCallExpr()) {
            MethodCallExpr methodCall = ifStmt.getCondition().asMethodCallExpr();
            if (methodCall.getNameAsString().equals("isEnabled") &&
                methodCall.getScope().isPresent() &&
                methodCall.getScope().get().toString().equals("FeatureToggle")) {

                BlockStmt thenBlock = ifStmt.getThenStmt().asBlockStmt();
                ifStmt.substitute(thenBlock);
            }
        }
    }
}

This code defines a customer sample utilizing
JavaParser to traverse and manipulate the AST. The
FeatureToggleVisitor appears to be like for if statements
that decision FeatureToggle.isEnabled() and replaces the whole
if assertion with the true department.

You may also outline guests to search out unused strategies and take away
them:

class UnusedMethodRemover extends VoidVisitorAdapter<Void> {
    personal Set<String> calledMethods = new HashSet<>();
    personal Checklist<MethodDeclaration> methodsToRemove = new ArrayList<>();

    // Accumulate all known as strategies
    @Override
    public void go to(MethodCallExpr n, Void arg) {
        tremendous.go to(n, arg);
        calledMethods.add(n.getNameAsString());
    }

    // Accumulate strategies to take away if not known as
    @Override
    public void go to(MethodDeclaration n, Void arg) {
        tremendous.go to(n, arg);
        String methodName = n.getNameAsString();
        if (!calledMethods.comprises(methodName) && !methodName.equals("foremost")) {
            methodsToRemove.add(n);
        }
    }

    // After visiting, take away the unused strategies
    public void removeUnusedMethods() {
        for (MethodDeclaration technique : methodsToRemove) {
            technique.take away();
        }
    }
}

This code defines a customer, UnusedMethodRemover, to detect and
take away unused strategies. It tracks all known as strategies within the calledMethods
set and checks every technique declaration. If a way isn’t known as and isn’t
foremost, it provides it to the checklist of strategies to take away. As soon as all strategies are
processed, it removes any unused strategies from the AST.

Composing Java Guests

You’ll be able to chain these guests collectively and apply them to your codebase
like so:

public class FeatureToggleRemoverWithCleanup {
    public static void foremost(String[] args) {
        attempt {
            String filePath = "src/take a look at/java/com/instance/Instance.java";
            CompilationUnit cu = StaticJavaParser.parse(new FileInputStream(filePath));

            // Apply transformations
            FeatureToggleVisitor toggleVisitor = new FeatureToggleVisitor();
            cu.settle for(toggleVisitor, null);

            UnusedMethodRemover remover = new UnusedMethodRemover();
            cu.settle for(remover, null);
            remover.removeUnusedMethods();

            // Write the modified code again to the file
            attempt (FileOutputStream fos = new FileOutputStream(filePath)) {
                fos.write(cu.toString().getBytes());
            }

            System.out.println("Code transformation accomplished efficiently.");
        } catch (IOException e) {
            e.printStackTrace();
        }
    }
}

Every customer is a unit of transformation, and the customer sample in
JavaParser makes it simple to compose them.

Variations Between OpenRewrite and JavaParser or jscodeshift

The important thing distinction between OpenRewrite and instruments like JavaParser or
jscodeshift lies of their strategy to code transformation:

• OpenRewrite’s Lossless Semantic Bushes (LSTs) seize each the
  syntactic and semantic which means of the code, enabling extra correct
  transformations.
• JavaParser and jscodeshift depend on conventional ASTs, which focus
  totally on the syntactic construction. Whereas highly effective, they could not all the time
  seize the nuances of how the code behaves semantically.

Moreover, OpenRewrite gives a big library of community-driven
refactoring recipes, making it simpler to use frequent transformations with out
needing to write down customized codemods from scratch.

Hypermod

Hypermod introduces AI help to the codemod writing course of.
As a substitute of manually crafting the codemod logic, builders can describe
the specified transformation in plain English, and Hypermod will generate
the codemod utilizing jscodeshift. This makes codemod creation extra
accessible, even for builders who might not be conversant in AST
manipulation.

You’ll be able to compose, take a look at, and deploy a codemod to any repository
linked to Hypermod. It may run the codemod and generate a pull
request with the proposed adjustments, permitting you to evaluate and approve
them. This integration makes the whole course of from codemod growth
to deployment rather more streamlined.

Codemod.com

Codemod.com is a community-driven platform the place builders
can share and uncover codemods. Should you want a selected codemod for a
frequent refactoring activity or migration, you possibly can seek for present
codemods. Alternatively, you possibly can publish codemods you’ve created to assist
others within the developer neighborhood.

Should you’re migrating an API and want a codemod to deal with it,
Codemod.com can prevent time by providing pre-built codemods for
many frequent transformations, lowering the necessity to write one from
scratch.