Refactoring with Codemods to Automate API Adjustments

Clear a Stale Function 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 manage 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 scrub 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 characteristic is totally launched and not wants a toggle, this
may 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 similar time, different characteristic toggles (like
feature-search-result-refinement, which can nonetheless be in improvement)
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 seems to be in an AST. You need to use instruments like AST
Explorer to visualise how supply code and AST
are mapped. It’s useful to grasp the node sorts you are interacting
with earlier than making use of any adjustments.

The picture beneath exhibits the syntax tree by way of 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 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 { identify: 'Product' } to knowledge. If
false, the alternate department assigns undefined.

For a activity with clear enter and output, I favor writing exams 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 exams move.

This method aligns properly with Check-Pushed Improvement (TDD), even
in the event you don’t apply TDD commonly. 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 exams to confirm how the codemod
behaves:

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

defineInlineTest(
  remodel,
  {},
  `
  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 operate from jscodeshift lets you outline
the enter, anticipated output, and a string describing the check’s intent.
Now, operating the check with a traditional 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 knowledge = featureToggle('feature-search-result-refinement') ? { identify: 'Product' } : undefined;
  `,
  `
  const knowledge = 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 operate. Create a file
known as remodel.js with the next code construction:

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

  // manipulate the tree nodes right here

  return root.toSource();
};

This operate 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 remodel steps:

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

Right here’s how we obtain this utilizing jscodeshift:

module.exports = operate (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: { 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 check calls
  featureToggle('feature-new-product-list').
• Replaces your complete conditional expression with the ensuing (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 decreasing
handbook effort.

You’ll want to jot down extra check 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 sturdy in real-world situations.

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 offers a command-line
device that you should utilize to use the codemod and report the outcomes.

$ jscodeshift -t transform-name src/

After validating the outcomes, examine that every one useful exams nonetheless
move and that nothing breaks—even in the event you’re introducing a breaking change.
As soon as glad, you may 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 will
considerably enhance code high quality and maintainability. As codebases
evolve, they typically accumulate technical debt, together with outdated characteristic
toggles, deprecated strategies, or tightly coupled elements. Manually
refactoring these areas may be time-consuming and error-prone.

By automating refactoring duties, codemods assist maintain your codebase clear
and freed from legacy patterns. Recurrently 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 Element

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. At any time when a person 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 (
      <Tooltip content material={identify}>
        <CircleImage picture={picture} />
      </Tooltip>
    );
  }

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

The aim is to decouple the Tooltip from the Avatar part,
giving builders extra flexibility. Builders ought to be capable of determine
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 lots of of Avatar usages unfold
throughout the codebase. Manually refactoring every occasion could 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 signify the Avatar utilization
we’re focusing 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 baby of the Tooltip.
• Exchange the unique Avatar node with the brand new Tooltip.

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

defineInlineTest(
    { default: remodel, 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 offered"
  );

Much 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 operate, 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 baby. Lastly, we name replaceWith to
exchange the present path.

Right here’s a preview of the way it seems to be 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 outcome:

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 could be an enormous burden. Nevertheless, that is not the entire
image. Within the subsequent part, I’ll make clear among the challenges
and the way we will handle these less-than-ideal elements.

Leveraging Supply Graphs and Check-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 elements rewrite challenge at Atlassian. We addressed this subject by
first looking out the supply graph, which contained nearly all of inside
part utilization. This allowed us to grasp how elements have been used,
whether or not they have been imported below totally different names, or whether or not sure
public props have been often used. After this search section, we wrote our
check 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. Normally,
there have been solely a handful of such situations, so this method nonetheless proved
helpful for upgrading variations.

Using Present Code Standardization Instruments

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

Nevertheless, in case your codebase has standardization instruments in place, equivalent to a
linter that enforces a specific coding model, you may leverage these
instruments to cut 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 possibly can use linting guidelines to limit sure patterns,
equivalent to 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 sort out 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 characteristic toggle elimination instance mentioned earlier. Within the code snippet
we’ve got 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 outcome = featureToggle("feature-convert-new")
  ? convertNew("Whats up, world")
  : convertOld("Whats up, world");

console.log(outcome);

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

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

const outcome = convertNew("Whats up, world");

console.log(outcome);

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

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

In fact, you possibly can write one large codemod to deal with every little thing in a
single move and check it collectively. Nevertheless, 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
may be examined individually, masking totally different circumstances with out interference.
Furthermore, it lets you reuse and compose them for various
functions.

For example, you would possibly break it down like this:

• A change to take away a particular characteristic toggle.
• One other transformation to scrub up unused imports.
• A change to take away unused operate 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 operate because it’s not used.

Determine 6: Compose transforms into a brand new remodel

You too can 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 kind one other remodel

The createTransformer Operate

The implementation of the createTransformer operate is comparatively
easy. It acts as a higher-order operate that takes a listing of
smaller remodel features, iterates by way of the record to use them to
the basis 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 possibly can have a remodel operate 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 would possibly construct up a set of reusable, smaller
transforms, which may enormously ease the method of dealing with difficult 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 couple of reusable transforms outlined, like including feedback
at the beginning of features, eradicating deprecated props, or creating aliases
when a package deal is already imported above.

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

Since every remodel is comparatively standalone, you may fine-tune them
with out affecting different transforms or the extra advanced, composed ones. For
occasion, you would possibly re-implement a remodel to enhance efficiency—like
decreasing 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 may be helpful for making breaking API adjustments or refactoring
massive Java codebases in a structured, automated means.

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 search out if statements checking for
FeatureToggle.isEnabled, after which exchange 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<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.exchange(thenBlock);
            }
        }
    }
}

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

You too can outline guests to search out unused strategies and take away
them:

class UnusedMethodRemover extends VoidVisitorAdapter<Void> {
    non-public Set<String> calledMethods = new HashSet<>();
    non-public Listing<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.incorporates(methodName) && !methodName.equals("essential")) {
            methodsToRemove.add(n);
        }
    }

    // After visiting, take away the unused strategies
    public void removeUnusedMethods() {
        for (MethodDeclaration methodology : methodsToRemove) {
            methodology.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 methodology declaration. If a technique 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) {
        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 method to code transformation:

• OpenRewrite’s Lossless Semantic Timber (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 at all times
  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 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 is probably not accustomed to AST
manipulation.

You’ll be able to compose, check, and deploy a codemod to any repository
related to Hypermod. It could run the codemod and generate a pull
request with the proposed adjustments, permitting you to evaluation and approve
them. This integration makes your complete course of from codemod improvement
to deployment rather more streamlined.

Codemod.com

Codemod.com is a community-driven platform the place builders
can share and uncover codemods. In case you want a particular codemod for a
frequent refactoring activity or migration, you may seek for current
codemods. Alternatively, you may publish codemods you’ve created to assist
others within the developer group.

In case 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, decreasing the necessity to write one from
scratch.