Refactoring with Codemods to Automate API Modifications

Clear a Stale Characteristic Toggle

Let’s begin with a easy but sensible instance to show 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 stay in manufacturing and dealing as anticipated, the following
logical step is to wash up the toggle and any associated logic.

As an example, take into account the next code:

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

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

const knowledge = { title: 'Product' };

The duty includes 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 under exhibits the syntax tree by way of ECMAScript syntax. It
accommodates nodes like Identifier (for variables), StringLiteral (for the
toggle title), and extra summary nodes like CallExpression and
ConditionalExpression.

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

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

For a process with clear enter and output, I choose writing assessments first,
then implementing the codemod. I begin by defining a unfavorable case to
guarantee we don’t by chance change issues we wish to go away untouched,
adopted by an actual case that performs the precise conversion. I start with
a easy situation, implement it, then add a variation (like checking if
featureToggle is named inside an if assertion), implement that case, and
guarantee all assessments go.

This strategy aligns effectively with Check-Pushed Improvement (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 may write assessments to confirm how the codemod
behaves:

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

defineInlineTest(
  remodel,
  {},
  `
  const knowledge = featureToggle('feature-new-product-list') ? { title: 'Product' } : undefined;
  `,
  `
  const knowledge = { title: '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 check’s intent.
Now, working the check with a standard jest command will fail as a result of the
codemod isn’t written but.

The corresponding unfavorable case would make sure the code stays unchanged
for different characteristic toggles:

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

Writing the Codemod

Let’s begin by defining a easy remodel perform. Create a file
referred to 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. Substitute your entire 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 check is featureToggle('feature-new-product-list')
  root
    .discover(j.ConditionalExpression, {
      check: {
        callee: { title: "featureToggle" },
        arguments: [{ value: "feature-new-product-list" }],
      },
    })
    .forEach((path) => {
      // Substitute the ConditionalExpression with the 'consequent'
      j(path).replaceWith(path.node.consequent);
    });

  return root.toSource();
};

The codemod above:

• Finds ConditionalExpression nodes the place the check calls
  featureToggle('feature-new-product-list').
• Replaces your entire conditional expression with the ensuing (i.e., {
title: '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 check instances to deal with variations like
if-else statements, logical expressions (e.g.,
!featureToggle('feature-new-product-list')), and so forth to make the
codemod sturdy in real-world eventualities.

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

$ jscodeshift -t transform-name src/

After validating the outcomes, examine that each one practical assessments nonetheless
go and that nothing breaks—even for those who’re introducing a breaking change.
As soon as glad, you may 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 might be time-consuming and error-prone.

By automating refactoring duties, codemods assist maintain your codebase clear
and freed from legacy patterns. Repeatedly 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 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 title 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 = ({ title, picture }: AvatarProps) => {
  if (title) {
    return (
      
        
      
    );
  }

  return ;
};

The purpose 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 lots of 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 signify the Avatar utilization
we’re concentrating on. An Avatar part with each title and picture props
is parsed into an summary syntax tree as proven under:

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 title prop is current.
• If not, do nothing.
• If current:
• Create a Tooltip node.
• Add the title to the Tooltip.
• Take away the title from Avatar.
• Add Avatar as a toddler of the Tooltip.
• Substitute the unique Avatar node with the brand new Tooltip.

To start, we’ll discover all cases of Avatar (I’ll omit a few of the
assessments, however you must write comparability assessments first).

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

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

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

Subsequent, we examine if the title prop is current:

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

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

    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 title
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 remodeled end result:

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

This codemod searches for all cases of Avatar. If a
title prop is discovered, it removes the title prop
from Avatar, wraps the Avatar inside a
Tooltip, and passes the title 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
handbook updates could be an enormous burden. Nevertheless, that is not the entire
image. Within the subsequent part, I’ll make clear a few of the challenges
and the way we are able to tackle these less-than-ideal features.

Leveraging Supply Graphs and Check-Pushed Codemods

To deal with these complexities, codemods needs to be used alongside different
strategies. As an example, a number of years in the past, I participated in a design
system elements rewrite mission at Atlassian. We addressed this situation by
first looking out the supply graph, which contained the vast majority of inner
part utilization. This allowed us to know how elements had been used,
whether or not they had been imported beneath totally different names, or whether or not sure
public props had been continuously used. After this search section, we wrote our
check instances upfront, making certain we lined the vast majority of use instances, 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 working the script to deal with particular instances manually. Often,
there have been solely a handful of such cases, so this strategy nonetheless proved
helpful for upgrading variations.

Using Present Code Standardization Instruments

As you may see, there are many edge instances to deal with, particularly in
codebases past your management—reminiscent of exterior dependencies. This
complexity signifies that utilizing codemods requires cautious supervision and a
overview of the outcomes.

Nevertheless, in case your codebase has standardization instruments in place, reminiscent of a
linter that enforces a specific coding model, you may leverage these
instruments to scale back edge instances. By implementing a constant construction, instruments
like linters assist slim down the variations in code, making the
transformation simpler and minimizing surprising points.

As an example, you would use linting guidelines to limit sure patterns,
reminiscent of 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 lets you 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 a toggle referred to 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("Hiya, world")
  : convertOld("Hiya, world");

console.log(end result);

The codemod for take away a given toggle works nice, and after working the codemod,
we would like the supply to appear to be this:

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

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

console.log(end result);

Nevertheless, 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 would write one huge codemod to deal with every part in a
single go and check it collectively. Nevertheless, a extra maintainable strategy is
to deal with codemod logic like product code: break the duty into smaller,
impartial items—similar 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
might be examined individually, protecting totally different instances with out interference.
Furthermore, it lets you reuse and compose them for various
functions.

As an example, you would possibly break it down like this:

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

By composing these, you may 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

It’s also possible to extract further codemods as wanted, combining them in
varied orders relying on the specified consequence.

Determine 7: Put totally 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 by the checklist 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";

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

export { createTransformer };

For instance, you would 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 instances anymore:

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

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

Turns into this:

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

Over time, you would possibly construct up a group of reusable, smaller
transforms, which may enormously ease the method of dealing with tough edge
instances. This strategy proved extremely efficient in our work refining design
system elements. As soon as we transformed one bundle—such because the button
part—we had a number of reusable transforms outlined, like including feedback
at first of capabilities, eradicating deprecated props, or creating aliases
when a bundle is already imported above.

Every of those smaller transforms might be examined and used independently
or mixed for extra complicated transformations, which quickens 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 may fine-tune them
with out affecting different transforms or the extra complicated, composed ones. For
occasion, you would possibly re-implement a remodel to enhance efficiency—like
lowering the variety of node-finding rounds—and with complete check
protection, you are able to do this confidently and safely.

Utilizing JavaParser in a Java Codebase

JavaParser might be helpful for making breaking API modifications 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 Characteristic Enabled");
    }

    void oldFeature() {
        System.out.println("Outdated Characteristic");
    }
}

We are able to outline a customer to seek out if statements checking for
FeatureToggle.isEnabled, after which substitute them with the corresponding
true department—just 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.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 your entire
if assertion with the true department.

It’s also possible to outline guests to seek out unused strategies and take away
them:

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

    // Gather all referred to 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 referred to as
    @Override
    public void go to(MethodDeclaration n, Void arg) {
        tremendous.go to(n, arg);
        String methodName = n.getNameAsString();
        if (!calledMethods.accommodates(methodName) && !methodName.equals("fundamental")) {
            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 referred to as strategies within the calledMethods
set and checks every technique declaration. If a technique isn’t referred to as and isn’t
fundamental, 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 fundamental(String[] args) {
        strive {
            String filePath = "src/check/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
            strive (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 Timber (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 all the time
  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 write down customized codemods from scratch.

Hypermod

Hypermod introduces AI help to the codemod writing course of.
As an alternative 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 conversant in AST
manipulation.

You’ll be able to compose, check, and deploy a codemod to any repository
linked to Hypermod. It might run the codemod and generate a pull
request with the proposed modifications, permitting you to overview and approve
them. This integration makes your entire course of from codemod growth
to deployment way 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 process or migration, you may seek for current
codemods. Alternatively, you may publish codemods you’ve created to assist
others within the developer group.

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.