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 function
toggle in your
codebase to manage the discharge of unfinished or experimental options.
As soon as the function is dwell in manufacturing and dealing as anticipated, the following
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 function is totally launched and now not wants a toggle, this
will be simplified to:

const information = { identify: '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 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 modifications selectively.

Understanding the AST

Earlier than we dive into writing the codemod, let’s break down how this
particular code snippet seems in an AST. You should 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 modifications.

The picture under reveals 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 function toggle verify

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

For a process with clear enter and output, I choose writing exams first,
then implementing the codemod. I begin by defining a damaging 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 situation, implement it, then add a variation (like checking if
featureToggle is known as inside an if assertion), implement that case, and
guarantee all exams go.

This method aligns effectively with Take a look at-Pushed Growth (TDD), even
in case you don’t follow 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 may write exams to confirm how the codemod
behaves:

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

defineInlineTest(
  rework,
  {},
  `
  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 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 damaging case would make sure the code stays unchanged
for different function toggles:

defineInlineTest(
  rework,
  {},
  `
  const information = featureToggle('feature-search-result-refinement') ? { identify: 'Product' } : undefined;
  `,
  `
  const information = 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
referred to 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 are able to begin implementing the rework steps:

1. Discover all cases of featureToggle.
2. Confirm that the argument handed is 'feature-new-product-list'.
3. Substitute 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 check is featureToggle('feature-new-product-list')
  root
    .discover(j.ConditionalExpression, {
      check: {
        callee: { identify: "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 the whole 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
guide 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 strong 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 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, verify that each one purposeful exams nonetheless
go and that nothing breaks—even in case you’re introducing a breaking change.
As soon as happy, 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 will
considerably enhance code high quality and maintainability. As codebases
evolve, they typically accumulate technical debt, together with outdated function
toggles, deprecated strategies, or tightly coupled parts. Manually
refactoring these areas will be time-consuming and error-prone.

By automating refactoring duties, codemods assist preserve your codebase clear
and freed from legacy patterns. Commonly 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 complicated instance. Suppose you’re working with
a design system that features an Avatar part tightly coupled with a
Tooltip. Each time a person passes a identify prop into the Avatar, it
robotically 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 purpose 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 a whole lot of Avatar usages unfold
throughout the codebase. Manually refactoring every occasion can 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 identify 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 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.
• Substitute the unique Avatar node with the brand new Tooltip.

To start, we’ll discover all cases of Avatar (I’ll omit a number of the
exams, however you need to write comparability exams 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 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 verify 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 seems 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 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 can be an enormous burden. Nonetheless, that is not the entire
image. Within the subsequent part, I’ll make clear a number of the challenges
and the way we are able to deal with these less-than-ideal elements.

Leveraging Supply Graphs and Take a look at-Pushed Codemods

To deal with these complexities, codemods needs to be used alongside different
methods. As an illustration, just a few years in the past, I participated in a design
system parts rewrite mission at Atlassian. We addressed this subject by
first looking out the supply graph, which contained nearly all of inner
part utilization. This allowed us to grasp how parts had been used,
whether or not they had been imported below totally different names, or whether or not sure
public props had been ceaselessly used. After this search part, we wrote our
check instances upfront, making certain we coated nearly all 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. Normally,
there have been solely a handful of such cases, so this method 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—equivalent to exterior dependencies. This
complexity implies that utilizing codemods requires cautious supervision and a
evaluation of the outcomes.

Nonetheless, 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 instances. By implementing a constant construction, instruments
like linters assist slender down the variations in code, making the
transformation simpler and minimizing sudden points.

As an illustration, you would use linting guidelines to limit sure patterns,
equivalent 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 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 function toggle elimination instance mentioned earlier. Within the code snippet
we’ve a toggle referred to 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 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 positive, and after working the codemod,
we wish the supply to appear like this:

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

const end result = convertNew("Hiya, 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 would write one large codemod to deal with all the things in a
single go and check it collectively. Nonetheless, a extra maintainable method 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 large transformation down into smaller codemods and
compose them. The benefit of this method is that every transformation
will be examined individually, overlaying totally different instances with out interference.
Furthermore, it lets you reuse and compose them for various
functions.

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

• A metamorphosis to take away a particular function 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 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 now not used.

Determine 6: Compose transforms into a brand new rework

You can too extract further codemods as wanted, combining them in
numerous orders relying on the specified consequence.

Determine 7: Put totally 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 a listing of
smaller rework capabilities, iterates by means of the checklist 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";

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

export { createTransformer };

For instance, you would 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 instances 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 group of reusable, smaller
transforms, which might vastly ease the method of dealing with difficult edge
instances. This method 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 just a few 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 will be examined and used independently
or mixed for extra complicated transformations, which quickens subsequent
conversions considerably. In consequence, our refinement work turned extra
environment friendly, and these generic codemods are actually relevant to different inner
and even exterior React codebases.

Since every rework is comparatively standalone, you may fine-tune them
with out affecting different transforms or the extra complicated, composed ones. For
occasion, you may re-implement a rework 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 will be helpful for making breaking API modifications or refactoring
giant Java codebases in a structured, automated method.

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("Previous 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 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 seems for if statements
that decision FeatureToggle.isEnabled() and replaces the whole
if assertion with the true department.

You can too outline guests to seek 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 referred to 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 referred to 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("important")) {
            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 referred to as strategies within the calledMethods
set and checks every methodology declaration. If a way isn’t referred to as and isn’t
important, 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 important(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 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 might not at all times
  seize the nuances of how the code behaves semantically.

Moreover, OpenRewrite provides a big library of community-driven
refactoring recipes, making it simpler to use widespread 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 aware of AST
manipulation.

You’ll be able to compose, check, and deploy a codemod to any repository
linked to Hypermod. It might probably run the codemod and generate a pull
request with the proposed modifications, permitting you to evaluation and approve
them. This integration makes the whole 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. In case you want a particular codemod for a
widespread 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.

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 widespread transformations, decreasing the necessity to write one from
scratch.