Refactoring at Scale
Maude Lemaire
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Maude Lemaire
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Making significant changes to large, complex codebases is a daunting task--one that's nearly impossible to do successfully unless you have the right team, tools, and mindset. If your application is in need of a substantial overhaul and you're unsure how to go about implementing those changes in a sustainable way, then this book is for you.
Software engineer Maude Lemaire walks you through the entire refactoring process from start to finish. You'll learn from her experience driving performance and refactoring efforts at Slack during a period of critical growth, including two case studies illustrating the impact these techniques can have in the real world. This book will help you achieve a newfound ability to productively introduce important changes in your codebase.
What Is Refactoring?
Process by which we restructure existing code (the factoring) without changing its external behavior
Refactored version
What Is Refactoring at Scale?
One that affects a substantial surface area of your systems
Typically (but not exclusively) involves a large codebase (a million or more lines of code)
powering applications with many users
About identifying a systemic problem in your codebase
Conceiving of a better solution
Executing on that solution in a strategic, disciplined way
To identify systemic problems and their corresponding solutions :
need a solid understanding of one or more broad sections of an application
need high stamina to propagate the solution properly to the entire affected area
Benefits of Refactoring
2 majors :
Increased developer productivity
Greater ease identifying bugs
Risks of Refactoring
Serious Regressions : with every change, large or small, we disrupt the equilibrium of the system in a measurable way
Might lead to unanticipated regression
Unearthing Dormant Bugs
Scope Creep :
The larger the surface area of the planned refactor
the more problems you’ll encounter that you likely haven’t anticipated
Keeping to a well-defined plan
WHEN to refactor ?
Small Scope
When looking to refactor a small, straightforward section of well-tested code,
Should be very little holding you back
Code Complexity Actively Hinders Development
Every time we read over the code our brows furrow, our hearts pound, our neurons start
firing
Shift in Product Requirements : can frequently map to drastic shifts in code
Performance
Using a new Technology
WHEN NOT TO refactor ?
For Fun or Out of Boredom : "Is it the most important thing you could be doing right now?"
Because You Happened to Be Passing By
To Making Code More Extendable : rewriting code for the sake of future malleability is likely unwise
When You Don’t Have Time
a high level of vigilance over the state of the codebase and any external influences is key to minimizing setbacks and ultimately ensuring a smooth path to the finish line.
Code has degraded when its perceived utility has decreased
2 ways in which code can degrade either :
The requirements for what the code needs to do or how it needs to behave have changed,
Or your organization has been cutting corners in an attempt to achieve more in a short
period
We refer to these as “requirement shifts” and “tech debt,” respectively
Requirement shifts : Scalability, Accessibility, Device Compatibility, Environmental changes, External Dependencies, Unused code, Changes in Product Requirements
Tech Debt : Working around Tech Choices, Persistent Lack of organization, Moving too quickly
Part 2 : Planning
Quantifying the impact of refactoring motivated by performance is often the easiest
Halstead Metrics (1975) : counting the number of operators and operands in a given computer program
Operators are constructs that behave like functions, but differ syntactically or semantically from typical functions.
These include arithmetic symbols like - and +, logical
operators like &&, comparison operators like >, and assignment operators like =
Operands : any entities we operate on, using our set of operators
He proposed a set of metrics to calculate a set of characteristics :
Program’s volume : or how much information the reader of the code has to
absorb in order to understand its meaning.
Program’s difficulty : or the amount of mental effort required to re-create the
software; also commonly referred to as the Halstead effort metric.
The number of bugs you are likely to find in the system.
Cyclomatic Complexity (1976) : it is a quantitative measure of the number of linearly independent paths through a program’s source code
Essentially a count of the number of control flow statements within a program
This includes if statements, while and for loops, and case statements in side
switch blocks
NPath Complexity (1988) : Nejmeh asserts that not all control flow structures are equal; some are more difficult to understand use properly than others
For example, a while loop might be trickier for a developer to reason about than a switch statement.
Nesting can influence the psychological complexity of the function
Psychological complexity can have a large impact on our ability to maintain software
quality
The calculation is recursive and can quickly balloon
Lines of Code : Control flow graph metrics can be difficult to calculate
Program size can help us locate likely pain points in our application
If we’re looking for a pragmatic, low-effort approach to quantifying the complexity of our code, then size-based metrics are the way to go
LOC (lines of code) per file : capture the psychological overhead required to
understand their contents and responsibilities when a developer pops them open
in their editor.
Function length : Measuring the length of functions or methods within your application can be a helpful way of approximating their individual complexities.
Average function length per file, module, or class : Knowing the average length of the smaller logical components contained within it can give you an indication of the relative complexity of that unit as a whole.
Whatever our approach, the desired outcome is the same: a new feature, fully backed by a quality set of tests
We can evaluate test coverage in two ways: quantitatively and qualitatively
Quantitatively : calculate a percentage representing the proportion of code that is executed when the test suite is run against it
We can collect metrics for both the number of functional lines of code and the number of execution paths tested
test coverage alone is not an indication of how well-tested something is
Qualitatively : suitable test quality has been attained if the following points hold true:
The tests are reliable. From one run to the next, they consistently produce passing results when run against unchanged code and catch bugs during
development.
The tests are resilient. They are not so tightly coupled to implementation that
they stifle change.
A range of test types exercise the code. Having unit, integration, and end-to-end
tests can help us assert that our code is functioning as intended with different levels of fidelity.
Formal Documentation : everything you most likely think of as documentation
We can use things like technical specs as evidence that our refactor is necessary or useful by referencing design decisions, assumptions, or other designs considered or
rejected.
Informal Documentation :
Chat and email transcripts : can provide insightful information about the code you’re
seeking to refactor
Bug Tracking system
Project Management System
Commit Messages : set of keywords or by isolating commit messages associated with changes to a set of files we’re interested in
Commits in Aggregate : ref Software Design X-Rays
Change frequencies : are the number of commits made to each file over the complete version history of your application
Code authorship
A simple, low-effort means of collecting reputation data is to interview fellow developers :
I recommend picking one metric from every category :
Generate some test coverage metrics to make sure you start off on the right foot
Identify a source of formal documentation you can use to illustrate the problems your refactor aims to solve; back it up with some informal documentation as well.
Gather information about your hotspots and programming patterns by slicing and dicing version control data.
Consider the code’s reputation by chatting with your colleagues
Defining your end state : Our execution plan should clearly outline all starting metrics and target end metrics
Feel free to provide both an ideal end state and an acceptable end state. Sometimes, getting 80 percent of the way there gives you 99 percent of the benefit of the refactor.
Mapping the Shortest Distance : a few alternatives
Open a blank document :
For 15 to 20 minutes : write down every step you can come up with
Set the document aside for at the very least a few hours (ideally a day or two)
Open it up again and try to order each step in chronological order
Gather a few coworkers who are either interested in the project or you know will
be contributing
Set aside an hour or so : grab a pack of sticky notes and a pen for
each of you
For 15 to 20' (or until everyone’s pens are down), write
down every step you think is required, each on individual sticky notes
Then, have a first person lay out their steps in chronological order
Subsequent teammates go through each of their own sticky notes and either :
Pair them up with their duplicates
or insert them into the appropriate spot within the timeline
Once everyone’s organized all of their notes :
Go through each step
Ask the room whether they believe that the step is absolutely required in order to reach the goal
If not, discard it
The final product should be a reasonable set of minimal steps
Final output of the exercise should be a written document that is easy to distribute and collaboratively improve
Identifying Strategic Intermediate Milestones :
1) Does this step feel attainable in a reasonable period?
2) Is this step valuable on its own?
3) If something comes up, could we stop at this step and pick it back up easily later?
Choosing a Rollout Strategy :
One of the key success metrics is that no behavior has changed
Dark Mode / Light Mode : We can compare pre-refactor and post-refactor behavior by employing what we’ve coined at Slack as the light/dark technique
Dark mode :
Both implementations are called
The results are compared
The results from the old implementation are returned
Light mode :
Both implementations are called
The results are compared
The results from the new implementation are returned
How To ?
Once the abstraction has been properly put in place
Start enabling dark mode
Monitor any differences being logged between the two result sets
Track down and fix any potential bugs in the new implementation causing those discrepancies
Repeat this process until you’ve properly handled all discrepancies,
Enabling dark mode to broader groups of users
Once all users have been opted in to dark mode
Continue logging any differences in the result sets
Continue to opt broader groups of users into light mode, until everyone is successfully processing results from the new implementation.
Disable execution of both code paths
Remove the old logic altogether : only the new implementation
should remain
Cons : If the code you are refactoring is performance-sensitive, and you’re operating in an environment that does not enable true multi-threading (PHP, Python, or Node), then running two versions of the same logic side by side might not be a great option
Cleaning Up Artifacts
No refactor is complete unless all remaining transitional artifacts are properly cleaned up.
Examples : Feature Flags, Abstractions, Dead Code, Comments (TODOs), Unit Tests (duplicative ones)
Referencing Metrics in your Plan
To support the initiative, not only does your problem statement need to be convincing with clear success criteria
Your proposal also needs to include definitive progress metrics
Showing that you have a strong direction should ease any concerns they might have about giving the go-ahead on a lengthy refactor.
Estimating
Simple technique :
Go through each of the milestones and assign a number from 1 to 10
where 1 denotes a relatively short task
10 denotes a lengthy task
The larger the software project, the greater the chance something won’t go quite to plan
Sharing Your Plan with Other Teams
Large refactoring projects typically affect a large number of engineering groups
Brainstorm with your team (or a small group of trusted colleagues) to make sure you’ve covered a variety of disciplines and departments
2 primary reasons :
Provide transparency
Gather perspective on your plan to strengthen it
Why Your Manager Is Not Onboard ?
Managers Aren’t Coding
Managers Are Evaluated Differently
Managers See the Risk
Managers Need to Coordinate
Strategies for Making a Compelling Argument (With our Manager)
First, have an initial investigatory conversation :
It helps you understand which factors are weighing most heavily on your manager
It gives you a sense of whether your manager might be more readily convinced by an emotional or logical argument
This conversation will give you the preliminary context you need to choose the most effective strategies to convince your manager
HOW TO ?
1 to 1 conversation
"I’ve been thinking about how X is affecting our ability to do Y and I wanted to know whether you had any thoughts about it.”
AFTER THAT
Using Conversational Devices
Compliment their thought process
Very few of us are immune to flattery, your manager included
Present the counter-argument :
A few benefits to presenting
Demonstrating your thoughtfulness and thoroughness around the effort
You’re reaffirming your manager’s concerns
Confirming that their apprehension is legitimate
Your manager will be more open to hearing about your ideas if they feel that their own ideas are well understood
Building an Alignment Sandwich
Do it by securing the support of your teammates
Along with the support of upper management
sandwiching your manager between the two
Relying on Evidence
If your manager is partial to logical arguments :
Use the evidence you gathered in Chapter 3 to bolster your position
Playing Hardball
If you are exceedingly confident that your refactor is critical to the business and your manager is unwilling to budge
Stop doing unrewarded maintenance work
Giving an ultimatum
If all else fails, you can suggest to your manager that if they continue to oppose the
refactor, you’ll either transfer to another team or outright quit the company.
Rewarding Refactoring
No engineer should be told that refactoring is equivalent to career suicide; instead, work with engineering promotion committees and human resources to include (and encourage) code maintenance. (requiring managers to include maintenance efforts in their quarterly planning)
To execute on a large refactoring effort successfully, we need our own Ocean’s 11
Assembling a team just the right size with just the right skills
they cut down on their execution time and increased their chances of success
We can start by rereading our plan
Try to visualize the code we’ll need to interact with
Can we conjure it up easily?
Can we confidently identify the changes we need to make and reason through the potential impact or downstream effects of those changes?
Do we understand the pitfalls we might run into in the given area of the codebase?
Do we understand the potential product implications of the changes we want to make?
Are we deeply familiar with the technologies we’ll either be directly or indirectly interfacing with?
If so, great! We’re probably in a good position to make those changes ourselves.
If not, then we’ll need someone else’s help.
2 ways to enlist someone :
An active contributor :
heavily involved with the project (ideally from day one)
Actively contributing to the effort by writing code alongside you
Should be consulted for input on the execution plan early and through each of its
revisions
Subject matter experts, or SMEs
Not active contributors to your effort
Agreed to be available to talk through solutions with you
Answer questions
Maybe do some code review
Match each expertise with one or more people :
Start from the beginning of the list
For each items : write the first few names of either individuals or teams that come to mind
Part 3 : Execution
Policy of no laptops and minimal phone usage during meetings.
Within Your Team
Stand-Ups : a great habit for keeping everyone on the team aligned at regular intervals.
Weekly Syncs : 1h
1st part : accomplishments
2nd part : discuss any important topics (new edge case for example)
Retrospectives : opportunity to reflect on the latest iteration cycle
highlight opportunities for improvement
identify any actions you can take moving forward
When Kicking Off the Project
Choosing a single source of truth
Choose a platform your team enjoys using to collect all documentation related to the project
Setting expectations
Take some time to draft a rough communication plan including :
Where stakeholders can find information about the current stage of the refactor
Where stakeholders can find a high-level project timeline
Where engineers can expect to find technical information about the refactor
Where stakeholders can ask questions
When affected teams should expect to hear from you
During Project Execution
Progress announcements
Not only important to let everyone know that you’ve completed another milestone (and unlocked any number of benefits as a result),
Crucial in continuing to make your team feel productive and boost their morale
Execution plan
Make a copy of the original execution plan
It will serve as a living version of the original document and should be progressively updated as the project develops
All of us seek advice from peers and experienced colleagues when solving difficult problems
Team Building
Pair programming
Refactoring in pairs can be particularly effective because while one person is typing, the other is freer to think about the bigger picture
Encourage pairing, but do not make it mandatory
Pair engineers with similar levels of experience
Timebox the session
Keeping Everyone Motivated
Motivating individuals : recognizing individual teammates for their distinct contributions is a great way to keep them motivated
Motivating teams : remember to celebrate your team’s achievements
Keeping a Tally
important to check on your progress frequently and maintain a running tally of important findings
Intermediate Metric Measurements
Unearthed Bugs : two options when confronting a bug within the context of a refactor :
Fix the bug vs to reimplement it
Clean-Up Items
Keep track of everything that’ll need tidying
Just as a cook would recommend cleaning pots and pans as you use them when preparing a meal
I recommend continually cleaning up as a refactor progresses
Out-of-Scope Items
Consider keeping a list of the opportunities you encounter to expand on the project
Programming Productively
Prototyping
Prototyping early and often helps your team ultimately move faster if you abide by two important principles:
Know that your solution will not be perfect
Focus on crafting a solution that works well overall, being mindful about not spending too much time perfecting the details.
Be willing to throw code away
If we spend a week or two writing a solution that simply doesn’t deliver
take the pieces that work, throw the rest away, and start again
Keep Things Small
Commit small, incremental changes makes it much easier to author great code
Can get relevant feedback early and often from your tooling
Reverting is much easier
Concise commit = focused
Maintain original version history : use git mv
Test, Test, Test
Frequently rerunning
Unit tests, integration tests, or walking through manual tests
We can either confirm
that everything has remained unaffected
or pinpoint the precise moment at which the behavior diverged
Asking the “Stupid” Question
Prioritize clarity over maintaining an illusion of omniscience
You are modeling important behavior for your team
Affirming that no question is, in fact, a stupid question
Fostering Adoption
Education
Active : planning and leading workshops
Passive : step-by-step tutorials, online courses, ...
Integrating Improvement into the Culture :
One of the best ways to maintain a healthy codebase is simply to
Continue deliberately refactoring small, well-contained portions of code as you encounter the opportunity
Focus on incrementally improving areas of the codebase owned and maintained by our own team
Encourage and facilitate design conversations on our team frequently
Hold inclusive design reviews early in the feature development process
Inviting engineers from all backgrounds to evaluate your designs and ask questions
