Refactoring with Codemods to Automate API Modifications

Clear a Stale Function Toggle

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

As an illustration, take into account the next code:

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

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

const information = { identify: 'Product' };

The duty entails discovering all cases 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 characteristic 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 modifications 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 need to use 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 modifications.

The picture beneath exhibits the syntax tree when it comes to ECMAScript syntax. It
incorporates 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 characteristic toggle test

On this AST illustration, the variable information 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 information. If
false, the alternate department assigns undefined.

For a process 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 by accident change issues we wish 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 go.

This method aligns properly with Check-Pushed Growth (TDD), even
for those who don’t follow TDD recurrently. Understanding 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 remodel = require("../remove-feature-new-product-list");

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

The defineInlineTest perform from jscodeshift permits you to 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 characteristic toggles:

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

Writing the Codemod

Let’s begin by defining a easy remodel perform. Create a file
known as remodel.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 are able to begin implementing the remodel steps:

1. Discover all cases of featureToggle.
2. Confirm that the argument handed is 'feature-new-product-list'.
3. Exchange all the 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) => {
      // Exchange 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 all the conditional expression with the resultant (i.e., {
identify: 'Product' }), eradicating the toggle logic and leaving simplified code
  behind.

This instance demonstrates how straightforward it’s to create a helpful
transformation and apply it to a big codebase, considerably decreasing
guide effort.

You’ll want to jot 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 situations.

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

$ jscodeshift -t transform-name src/

After validating the outcomes, test that every one purposeful assessments nonetheless
go and that nothing breaks—even for those who’re introducing a breaking change.
As soon as happy, you possibly can commit the modifications 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 modifications—they’ll
considerably enhance code high quality and maintainability. As codebases
evolve, they usually accumulate technical debt, together with outdated characteristic
toggles, deprecated strategies, or tightly coupled elements. 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. Usually making use of codemods permits you to
implement new coding requirements, take away unused code, and modernize your
codebase with out having to manually modify each file.

Refactoring an Avatar Element

Now, let’s have a look at a extra complicated instance. Suppose you’re working with
a design system that features an Avatar part tightly coupled with a
Tooltip. At any time when a consumer passes a identify prop into the Avatar, it
mechanically 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 (
      
        
      
    );
  }

  return ;
};

The aim 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 ;
};

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 (
    
      
    
  );
};

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

Utilizing instruments like AST Explorer, we are able to
examine the part and see which nodes characterize 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.
• Test 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.
• Exchange the unique Avatar node with the brand new Tooltip.

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

defineInlineTest(
    { default: remodel, parser: "tsx" },
    {},
    `
    
    `,
    `
    
      
    
    `,
    "wrap avatar with tooltip when identify is offered"
  );

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

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

Subsequent, we test 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
change 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 fitting is the unique code, and the underside
half is the reworked consequence:

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

This codemod searches for all cases 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 modifications the place
guide updates could 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 are able to tackle these less-than-ideal points.

Leveraging Supply Graphs and Check-Pushed Codemods

To deal with these complexities, codemods must be used alongside different
strategies. As an illustration, a number of years in the past, I participated in a design
system elements rewrite mission at Atlassian. We addressed this problem by
first looking out the supply graph, which contained nearly all of inner
part utilization. This allowed us to know how elements had been used,
whether or not they had been imported underneath completely different names, or whether or not sure
public props had been ceaselessly used. After this search part, we wrote our
take a look at circumstances upfront, making certain 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 cases, so this method 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—similar to 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, similar to a
linter that enforces a specific coding model, you possibly can leverage these
instruments to cut back edge circumstances. By implementing a constant construction, instruments
like linters assist slender down the variations in code, making the
transformation simpler and minimizing surprising points.

As an illustration, you possibly can use linting guidelines to limit sure patterns,
similar to avoiding nested conditional (ternary) operators or implementing named
exports over default exports. These guidelines assist streamline the codebase,
making codemods extra predictable and efficient.

Moreover, breaking down complicated transformations into smaller, extra
manageable ones permits you to sort out particular person points extra exactly. As
we’ll quickly see, composing smaller codemods could make dealing with complicated
modifications extra possible.

Codemod Composition

Let’s revisit the characteristic toggle removing instance mentioned earlier. Within the code snippet
we’ve got a toggle known as feature-convert-new should be eliminated:

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

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

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

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

console.log(consequence);

The codemod for take away a given toggle works superb, and after operating the codemod,
we wish the supply to appear like this:

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

const consequence = convertNew("Howdy, world");

console.log(consequence);

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

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

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

Breaking It Down

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

As an illustration, you may break it down like this:

• A change to take away a particular characteristic toggle.
• One other transformation to wash up unused imports.
• A change 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 remodel = createTransformer([
  removeFeatureConvertNew,
  removeUnusedImport,
  removeUnusedFunction,
]);

export default remodel;

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 remodel

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

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

The createTransformer Operate

The implementation of the createTransformer perform is comparatively
simple. It acts as a higher-order perform that takes an inventory of
smaller remodel capabilities, iterates via the record to use them to
the foundation AST, and at last converts the modified AST again into supply
code.

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

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

export { createTransformer };

For instance, you possibly can have a remodel perform that inlines
expressions assigning the characteristic 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 might enormously ease the method of dealing with tough edge
circumstances. This method proved extremely efficient in our work refining design
system elements. As soon as we transformed one package deal—such because the button
part—we had a number of reusable transforms outlined, like including feedback
at the beginning of capabilities, 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 complicated 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 inner
and even exterior React codebases.

Since every remodel is comparatively standalone, you possibly can fine-tune them
with out affecting different transforms or the extra complicated, composed ones. For
occasion, you may re-implement a remodel to enhance efficiency—like
decreasing 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 modifications or refactoring
massive Java codebases in a structured, automated approach.

Assume we’ve got 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 will outline a customer to seek out if statements checking for
FeatureToggle.isEnabled, after which change them with the corresponding
true department—much like how we dealt with the characteristic toggle codemod in
JavaScript.

// Customer to take away characteristic toggles
class FeatureToggleVisitor extends VoidVisitorAdapter {
    @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.change(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 all the
if assertion with the true department.

You may as well outline guests to seek out unused strategies and take away
them:

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

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

    // Gather 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.incorporates(methodName) && !methodName.equals("essential")) {
            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
essential, it provides it to the record 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 essential(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 straightforward 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 method to code transformation:

• OpenRewrite’s Lossless Semantic Bushes (LSTs) seize each the
  syntactic and semantic that 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 at all times
  seize the nuances of how the code behaves semantically.

Moreover, OpenRewrite presents a big library of community-driven
refactoring recipes, making it simpler to use frequent transformations with out
needing to jot 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 will not be aware of AST
manipulation.

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

Codemod.com

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

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