Refactoring with Codemods to Automate API Modifications

Clear a Stale Function Toggle

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

As an illustration, take into account the next code:

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

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

const knowledge = { 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 similar 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 appears in an AST. You should utilize instruments like AST
Explorer to visualise how supply code and AST
are mapped. It’s useful to know the node varieties you are interacting
with earlier than making use of any modifications.

The picture under 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 function toggle verify

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

For a process with clear enter and output, I desire writing checks first,
then implementing the codemod. I begin by defining a unfavorable case to
guarantee we don’t by chance change issues we wish to depart 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 named inside an if assertion), implement that case, and
guarantee all checks move.

This strategy aligns effectively with Check-Pushed Improvement (TDD), even
in case you don’t follow TDD usually. 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 checks 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 perform from jscodeshift means that you can outline
the enter, anticipated output, and a string describing the take a look at’s intent.
Now, working the take a look at with a traditional 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 function 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 function 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 will 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 complete 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) => {
      // Substitute 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 your complete conditional expression with the ensuing (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
handbook effort.

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

As soon as the codemod is prepared, you may try it out on a goal codebase,
such because the one you are engaged on. jscodeshift supplies a command-line
instrument 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 useful checks nonetheless
move 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 usually accumulate technical debt, together with outdated function
toggles, deprecated strategies, or tightly coupled parts. Manually
refactoring these areas might 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 means that you can
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 element tightly coupled with a
Tooltip. Every time a person passes a identify prop into the Avatar, it
routinely wraps the avatar with a tooltip.

Determine 3: A avatar element 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 objective is to decouple the Tooltip from the Avatar element,
giving builders extra flexibility. Builders ought to be capable to 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 ;
};

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

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

Determine 4: AST of the Avatar element utilization

Writing the Codemod

Let’s break down the transformation into smaller duties:

• Discover Avatar utilization within the element 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.
• 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
checks, however it is best to write comparability checks first).

defineInlineTest(
    { default: remodel, parser: "tsx" },
    {},
    `
    
    `,
    `
    
      
    
    `,
    "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 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
element as a toddler. Lastly, we name replaceWith to
change the present path.

Right here’s a preview of the way it appears 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 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
handbook updates can be an enormous burden. Nonetheless, that is not the entire
image. Within the subsequent part, I’ll make clear a few of the challenges
and the way we will handle these less-than-ideal points.

Leveraging Supply Graphs and Check-Pushed Codemods

To deal with these complexities, codemods needs to be used alongside different
strategies. As an illustration, just a few years in the past, I participated in a design
system parts rewrite challenge at Atlassian. We addressed this challenge by
first looking the supply graph, which contained the vast majority of inside
element utilization. This allowed us to know how parts had been used,
whether or not they had been imported underneath totally different names, or whether or not sure
public props had been incessantly used. After this search part, we wrote our
take a look at circumstances upfront, making certain we coated the vast majority 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 working the script to deal with particular circumstances manually. Often,
there have been solely a handful of such cases, so this strategy nonetheless proved
useful 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—comparable to exterior dependencies. This
complexity signifies that utilizing codemods requires cautious supervision and a
evaluation of the outcomes.

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

As an illustration, you possibly can use linting guidelines to limit sure patterns,
comparable 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 means that you can sort out particular person points extra exactly. As
we’ll quickly see, composing smaller codemods could make dealing with advanced
modifications extra possible.

Codemod Composition

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

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

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

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

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

console.log(end result);

The codemod for take away a given toggle works superb, 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);

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.

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

Breaking It Down

We will break the large transformation down into smaller codemods and
compose them. The benefit of this strategy is that every transformation
might be examined individually, protecting totally different circumstances with out interference.
Furthermore, it means that you can reuse and compose them for various
functions.

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

• A change to take away a selected function toggle.
• One other transformation to scrub up unused imports.
• A change 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 now not used.

Determine 6: Compose transforms into a brand new remodel

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

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 basis AST, and eventually converts the modified AST again into supply
code.

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

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

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

    transforms.forEach((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 function toggle name to a variable, so in later
transforms you don’t have to fret about these circumstances anymore:

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

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

Turns into this:

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

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

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

Since every remodel is comparatively standalone, you may fine-tune them
with out affecting different transforms or the extra advanced, 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 might be helpful for making breaking API modifications or refactoring
giant Java codebases in a structured, automated means.

Assume now we have 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 change 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 {
    @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 for if statements
that decision FeatureToggle.isEnabled() and replaces your complete
if assertion with the true department.

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

class UnusedMethodRemover extends VoidVisitorAdapter {
    non-public Set calledMethods = new HashSet();
    non-public Checklist 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("primary")) {
            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
primary, 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 may chain these guests collectively and apply them to your codebase
like so:

public class FeatureToggleRemoverWithCleanup {
    public static void primary(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 strategy 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 might 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 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 conversant in AST
manipulation.

You may compose, take a look at, and deploy a codemod to any repository
related 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 your complete 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. In the event you want a selected codemod for a
widespread refactoring process or migration, you may seek for present
codemods. Alternatively, you may publish codemods you’ve created to assist
others within the developer neighborhood.

In the event 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.