Refactoring with Codemods to Automate API Adjustments

Clear a Stale Characteristic Toggle

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

As an illustration, contemplate the next code:

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

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

const information = { identify: 'Product' };

The duty includes 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 identical 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 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 should utilize 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 when it comes to ECMAScript syntax. It
accommodates 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 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 activity with clear enter and output, I want writing exams first,
then implementing the codemod. I begin by defining a adverse 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 known as inside an if assertion), implement that case, and
guarantee all exams cross.

This strategy aligns effectively with Take a look at-Pushed Growth (TDD), even
in case you don’t observe 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’ll be able to write exams to confirm how the codemod
behaves:

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

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

The defineInlineTest perform from jscodeshift 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 adverse case would make sure the code stays unchanged
for different characteristic toggles:

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

Writing the Codemod

Let’s begin by defining a easy remodel perform. Create a file
known as remodel.js with the next code construction:

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

  // manipulate the tree nodes right here

  return root.toSource();
};

This perform reads the file right into a tree and makes use of jscodeshift’s API to
question, modify, and replace the nodes. Lastly, it converts the AST again to
supply code with .toSource().

Now we 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 the 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 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 the 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
guide effort.

You’ll want to jot 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 situations.

As soon as the codemod is prepared, you’ll be able to try it out on a goal codebase,
such because the one you are engaged on. jscodeshift gives a command-line
software that you should use to use the codemod and report the outcomes.

$ jscodeshift -t transform-name src/

After validating the outcomes, examine that every one practical exams nonetheless
cross and that nothing breaks—even in case you’re introducing a breaking change.
As soon as glad, you’ll be able to 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 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 hold 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 advanced instance. Suppose you’re working with
a design system that features an Avatar element tightly coupled with a
Tooltip. Each time a consumer passes a identify prop into the Avatar, it
robotically 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 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 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 symbolize 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.
• 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.
• Exchange the unique Avatar node with the brand new Tooltip.

To start, we’ll discover all situations of Avatar (I’ll omit a number of the
exams, however you must write comparability exams 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 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 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 to be like in
Hypermod, the place the codemod is written on
the left. The highest half on the best is the unique code, and the underside
half is the remodeled consequence:

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 modifications the place
guide updates can be an enormous burden. Nevertheless, that is not the entire
image. Within the subsequent part, I’ll make clear a number of the challenges
and the way we will handle these less-than-ideal facets.

Leveraging Supply Graphs and Take a look at-Pushed Codemods

To deal with these complexities, codemods must be used alongside different
methods. As an illustration, a couple of years in the past, I participated in a design
system elements rewrite challenge at Atlassian. We addressed this situation by
first looking out the supply graph, which contained the vast majority of inner
element utilization. This allowed us to know how elements have been used,
whether or not they have been imported below completely different names, or whether or not sure
public props have been often used. After this search part, we wrote our
check instances upfront, guaranteeing we coated 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 operating the script to deal with particular instances manually. Normally,
there have been solely a handful of such situations, so this strategy nonetheless proved
useful for upgrading variations.

Using Present Code Standardization Instruments

As you’ll be able to see, there are many edge instances to deal with, particularly in
codebases past your management—corresponding to 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, corresponding to a
linter that enforces a selected coding type, you’ll be able to 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 might use linting guidelines to limit sure patterns,
corresponding 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 advanced transformations into smaller, extra
manageable ones lets you deal with 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 characteristic toggle elimination instance mentioned earlier. Within the code snippet
we have now 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 consequence = featureToggle("feature-convert-new")
  ? convertNew("Howdy, world")
  : convertOld("Howdy, world");

console.log(consequence);

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

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

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

console.log(consequence);

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

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

In fact, you might write one massive codemod to deal with every part in a
single cross 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 large transformation down into smaller codemods and
compose them. The benefit of this strategy is that every transformation
might be examined individually, overlaying completely 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 selected characteristic toggle.
• One other transformation to scrub up unused imports.
• A metamorphosis to take away unused perform declarations.

By composing these, you’ll be able to create a pipeline of transformations:

import { removeFeatureToggle } from "./remove-feature-toggle";
import { removeUnusedImport } from "./remove-unused-import";
import { removeUnusedFunction } from "./remove-unused-function";

import { createTransformer } from "./utils";

const removeFeatureConvertNew = removeFeatureToggle("feature-convert-new");

const remodel = createTransformer([
  removeFeatureConvertNew,
  removeUnusedImport,
  removeUnusedFunction,
]);

export default remodel;

On this pipeline, the transformations work as follows:

1. Take away the feature-convert-new toggle.
2. Clear up the unused import assertion.
3. Take away the convertOld perform because it’s not used.

Determine 6: Compose transforms into a brand new remodel

You may as well extract extra codemods as wanted, combining them in
varied orders relying on the specified consequence.

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

The createTransformer Perform

The implementation of the createTransformer perform is comparatively
simple. It acts as a higher-order perform that takes a listing of
smaller remodel capabilities, iterates via the record to use them to
the foundation 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 might 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 may construct up a set of reusable, smaller
transforms, which might tremendously 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 package deal—such because the button
element—we had a couple of reusable transforms outlined, like including feedback
at the beginning of capabilities, eradicating deprecated props, or creating aliases
when a package deal is already imported above.

Every of those smaller transforms might be examined and used independently
or mixed for extra advanced transformations, which hurries up subsequent
conversions considerably. In consequence, 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’ll be able to 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 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 method.

Assume we have now 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 change them with the corresponding
true department—much like how we dealt with the characteristic toggle codemod in
JavaScript.

// Customer to take away characteristic toggles
class FeatureToggleVisitor extends VoidVisitorAdapter {
    @Override
    public void go to(IfStmt ifStmt, Void arg) {
        tremendous.go to(ifStmt, arg);
        if (ifStmt.getCondition().isMethodCallExpr()) {
            MethodCallExpr methodCall = ifStmt.getCondition().asMethodCallExpr();
            if (methodCall.getNameAsString().equals("isEnabled") &&
                methodCall.getScope().isPresent() &&
                methodCall.getScope().get().toString().equals("FeatureToggle")) {

                BlockStmt thenBlock = ifStmt.getThenStmt().asBlockStmt();
                ifStmt.change(thenBlock);
            }
        }
    }
}

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

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

class UnusedMethodRemover extends VoidVisitorAdapter {
    non-public Set calledMethods = new HashSet<>();
    non-public Listing 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.accommodates(methodName) && !methodName.equals("predominant")) {
            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
predominant, 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 predominant(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 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 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 provides a big library of community-driven
refactoring recipes, making it simpler to use widespread transformations with out
needing to jot 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 accustomed to 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 the complete course of from codemod improvement
to deployment far more streamlined.

Codemod.com

Codemod.com is a community-driven platform the place builders
can share and uncover codemods. For those who want a selected codemod for a
widespread refactoring activity or migration, you’ll be able to seek for present
codemods. Alternatively, you’ll be able to publish codemods you’ve created to assist
others within the developer neighborhood.

For those who’re migrating an API and wish 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.