Efficient Code Review
During my days at VMware I was reviewing multiple UI code Pull Requests (also known as Merge Requests or Change Lists) per day. Changes could have anything from a small and isolated feature to high scale refactoring spread all over the code base.
Initially I didn’t have any specific approach to the reviewing process and was simply going file by file, line by line. However, as number of PRs coming my way grew I couldn’t keep up with them, and felt like the quality of my comments was degrading.
That made me stop and think if there is a better way. And I believe there is one (at least) which I’d like to share here as well as the reasoning behind it.
Let’s start with the basics — how does code review help the team? I believe good code reviews help to:
- increase the code quality
- preserve code consistency by ensuring that adopted practices are followed
- increase familiarity with the codebase and more importantly share knowledge within the team
- notice recurring coding/logic patterns — something which can be optimized later on
- notice common pitfalls and documentation gaps causing them
- increase visibility of the work progress
Generally speaking to preserve code consistency we use a certain technical stack
Redux and some UI kit) and various automated linters/formatters.
Unfortunately, that’s not enough.
Not every naming convention can be enforced with
eslint and no two React+Redux application
architectures are same.
Every app tends to have its own list of coding patterns and best practices which aren’t
eslint or even senior developer which has recently joined your team.
Then every team member has his/her own area of expertise and code reviews facilitate the knowledge spread. That raises the experience level of the whole team. For example someone who knows lodash library very well can easily spot custom code snippets which could be replaced with lodash methods to make the code dry and more readable. Another member who has been on the team for a few years can share the knowledge of the internal library with commonly used utilities to avoid reimplementation and code bloat.
And of course impact of bugs, typos and serious performance bottlenecks resolved before code is merged can’t be overestimated.
The list of code review benefits goes on and on, and it helps to focus on your personal strengths while reviewing someone else’s changes. Your unique experience and expertise might give the story reviewed an unexpected but valuable twist.
Also, good code reviewing process is in fact a two-way road — it is supposed to be as beneficial to the reviewer as it is to the author of changes.
To improve any process we need to come up with parameters we can (ideally) measure and track over the time, to make sure we are ot the right track.
- value: things improved based on the review: typos, incomplete solutions, poor architecture, bugs, inefficient code blocks, conventions not followed, etc. Disclaimer: things listed aren’t equal in their value
- concepts, approaches and patterns learned by all parties involved in the process
- review pending time. Obviously the shorter it is, the better.
- time (and effort) spent by the reviewer on reading, analyzing and commenting on the code as well time spent by the author on evaluating the feedback, commenting back or/and introducing changes
- quality of communication between the author and reviewer(s). Another way of saying that code review process should not induce any tension.
- number of review-update cycles (as well as time spent on them) before PR is merged
- number of issues opened in the tracker — not everything has to be fixed or improved right away
Maximizing value of the review is a very complex and broad subject dependent on the project, a feature under development, team and the reviewer. Let’s focus on the process instead: risks and common pitfalls of the naive line-by-line code reviewing process.
Let’s face it — reviewing someone’s code is not the easiest nor particular enjoyable activity. It requires fairly deep understanding of the original intent, suggested implementation and at least some idea on pieces involved and/or related to proposed changes. It also requires an inner strength and certain skill to question someone else’s solution and ask difficult questions without being aggressive or negative. Very often you would see or, indeed, write an approving review highlighting some syntactic issues and a few typos. It would look real but unfortunately would not provide a lot of value.
Another risk is to focus on implementation details instead of overall solution architecture. For instance reviewing an authentication service implementation is useless when such a service already exists in the codebase but PR author was not aware of it. Another suboptimal approach example would be reviewing the code of UI components before understanding the whole tree of components and components API (props/bindings).
Thorough implementation review without at least some understanding of the whole picture is all but impossible and even useless in case if the solution architecture (design) has to be significantly altered.
And of course the backbone of any review is communication. It could be comments on the code review system web page, follow-up messages on slack or in-person meeting (which in fact is preferred for complex cases). Regardless of the form, it is important to stay focused on the task. You have to be ready to explain logic and thinking behind any of your comments. Needless to say reviews are there not to exercise (or build) your authority and not every decision has to be mutually agreed upon. It’s ok to agree to disagree after you’ve exchanged opinions and understood positions of each other.
As you might have guessed at this point, code review would better be performed in the top-down fashion. Once you understand the intent and limitations of the task you can to go over the list of changed files to build the bird-view mental map of the proposed solution.
First, we will be looking at file names, folder structure, file-level comment blocks,
implements statements. We don’t need to read the code of every file but might
search for public methods of services and component bindings/props.
For huge PRs it is usually easier to start with more abstract entities. We are building the dependency graph in our head. To simplify it we can group some tightly related nodes together, i.e. abstract class and it’s implementations or parent component and it’s child components (if those aren’t used anywhere else).
We will be answering following questions:
- Does this tree of components/classes/services make sense? How well does it align with existent trees and common design approaches?
- Do all the nodes appear to be justified (useful and reasonable)? I.e. if new service is used by one component (or another service) only — do we really need that service? Maybe we do because next ticket of the same Epic will introduce another component relying on a new service being suggested.
- Filenames and service/class/constants/component names — do they follow accepted conventions and how well do they communicate the functionality provided? Often the clarity of naming and file-level comments is inversely proportional to the mental effort you spend on the first stage of the review.
The next step is to look into public APIs and usage patterns of components and services. Implementation of those doesn’t matter just yet but method signatures and names do. We will check from where and when these methods are getting called (and components rendered) for better understanding of respective scenarios.
Here we are evaluating:
- Components tree: are components small enough but not too small?
- Components API
- what exactly is being passed as props and if that is justified and concise enough
- what is the callbacks/actions payload
- Services API: public methods signatures, if these have to be exposed as public, etc.
- How well is communication between components and services organized?
- Do we have pre-existent pieces which could have been modified and used instead of introducing new ones? In order to follow single responsibility and DRY principles.
- Is test coverage present and sufficient? Especially for reusable services and components which generally require the highest test coverage.
- Does the proposed solution appear complete from user experience perspective?
Examples of incomplete solutions I came across:
- an object creation flow is implemented, but subsequent edits are not (even though available in UI)
- a new user role type is added in one place (i.e. user role modal) but not in the UI authentication/authorization service (which is affected by the change)
- Folder and module/package structure — do all the pieces fall into places they belong?
Once the implementation graph and relations between nodes are clear we can move on to review implementation details.
On the last and most laborious stage we will check for the proper usage of pre-existent
libraries, utilities and ES/TS language features. Basically we don’t want to re-implement
_.each method or
Record interface every time we need them.
For code changes coming from recent joinees it is extremely important to share the knowledge of the codebase which they might not yet have — things like module-level services, abstract classes, style mixins and variables, shared types, optimizations and build procedures relevant for the functionality introduced. Even if the external documentation does exist and is relevant (which doesn’t happen very often) initially it is hard to know what to look for.
We will verify code style conventions not covered by linters, such as the substance of comments, variable namings, internationalization of readable strings as well as test cases covered, proper clean up on components/service destroy, etc.
Common pitfalls I came across more than once:
- orphan CSS styles left after related components removal or refactoring
- orphan services and classes left after removing components and services which were using those
- files missing from the pull request, i.e. component styles or an html file
- random files accidentally added to the pull request
The main advantage of the top-down review approach is the fact that we do the most impactful and scalable checks (a list of files is always shorter than a list of lines changed) first. And by doing so reduce the risk of wasting everyone’s time and effort.
We invented computers to simplify our lives. Whatever can be automated needs to be automated. In relation to code review we can automate builds, test runs, code linting, various reports, etc. All these help the author to make sure his/her code is fine before the review and at same time significantly reduce the mental load of the reviewer.
As reviewers, we do not have to manually test the functionality delivered — there should be a certain level of trust and professionalism in the team which makes it works a safe assumption for every code change coming your way. Said that more often than not automated UI builds do help to understand the functionality and behavior suggested in the pull request (and sometimes catch a bug).
Code reviews are complex and demanding, but the result is well worth the effort. The above three-stage technique significantly reduced the time and effort I’m spending on reviews and greatly improved my comments quality (simultaneously reducing their quantity). It feels simpler too hence now I tend to procrastinate less before jumping on to the next review.