Suppose we have a large to-do task manager app with many features. Say we have an entity, which is the task, and it has certain fields like: title, description, deadline, sub-tasks, dependencies, etc. This entity is used in many parts of our codebase.
Suppose we decided to modify this entity, either by modifying, removing, or adding a field. We may have to change most if not all of the code that deals with this entity. How can we do this in a way that protects us from errors and makes maintenance easy?
Bear in mind, this is just an example. The entity may be something more low-key, such as a logged user event in analytics, or a backend API endpoint being used in the frontend, etc.
Potential Solutions
Searching
One way people do this already is by just searching the entity across the codebase. This is not scalable, and not always accurate. You may get a lot of false positives, and some parts of the code may use the entity without using it by name directly.
Importing
Defining the entity in one central place, and importing it everywhere it is used. This will create an error if a deleted field remains in use, but it will not help us when, say, adding a new field and making sure it is used properly everywhere the entity is being used
so what can be done to solve this? plus points if the approach is compatible with Functional Programming
Automated Tests and CICD
Tests can discover these types of issues with high accuracy and precision. The downside is… Well tests have to be written. This requires developers to be proactive, and writing and maintaining tests is non-trivial and needs expensive developer time. It is also quite easy and common to write bad tests that give false positives.
Wouldn’t static type checking solve most of these issues?
Typescript, but that’s not the issue. You probably have to leverage types in a specific way to get all the protections I am talking about. For example, I want it such that if a new field is added to a type, every user of the type must explicitly either use it or explicitly declare that it won’t. From my experience with type systems, you typically aren’t required to explicitly declare that you won’t use a field in a dictionary / record type.
If you update your tests to reflect proper usage of the new field then you can catch potential errors.
Automates tests definitely work, but the downside is it requires the developer to be proactive, and the effort put in writing tests is non-trivial (and its easy and common for developers to write bad tests that give false positives).
Hmm I think you’re looking for a technical solution to a non-technical problem.
Depends on what you consider technical. I don’t see this as much different than how type systems prevent type errors.
But no matter what you do, you’re asking for something that will need to be manually done. Your tests should be done, and they should be reviewed. It will solve the problem you have and many more.
Just like type systems prevent you from type errors that you may otherwise write unit tests for, I don’t see it unviable to have something that protects from the errors I mention.
In fact I think my solution might be in particular use of the type system, which I am experimenting with right now.
Having unit and automated integration tests backed by both requirements and high code coverage. As a lead I can verify that not only you made the change to support the requirements though these unit tests but also a really quick verification that other functionality may not have changed based on your large scale change. Helps a lot for significant refactoring too
An adequate test coverage should help you with these kinds of errors. Your tests should at least somehow fail if you make something incompatible. Also using the tools of your IDE will help you with refactoring.
Testing definitely works, but the downside is it requires the developer to be proactive, and the effort put in writing tests is non-trivial (and it’s easy and common for developers to write bad tests that give false positives).
There is a whole field, that looks a bit like religion to me, about how to test right.
I can tell you from experience that testing is a tool that can give confidence. There are a few new tools that can help. Mutation testing is one I know that can find bad tests.
Integration tests can help find the most egregious errors that make your application crash.
Not every getter needs a test but using unit tests while developing a feature can even save time because you don’t have to start the app and get to the point where the change happens and test by hand.
A review can find some errors but human brains are not compilers it is hard to miss errors and the more you add to a review the easier it can get lost. The reviews can mostly help make sure that the code is more in line with the times style and that more than one person knows about the changes.
You can’t find all mistakes all the time. That’s why it is very important to have a strategy to avert the worse and revert errors. If you develop a web app: backups, rolling deployments, revert procedures. And make sure everyone know how and try it at least once. These procedures can fail. Refine them trough failure.
That is my experience from working in the field for a while. No tests is bad. Too many tests is a hassle. There will always be errors. Be prepared.
That’s why test coverage exists and needs to be a mandated item.
I have absolutely no patience for developers unwilling to make good code. I don’t give a shit if it takes a while, bad code means vulnerabilities means another fucking data breach. If you as a developer don’t want to do what it takes to make good code, then quit and find a new fucking career.
Test coverage alone is meaningless, you need to think about input-coversge as well, and that’s where you can spend almost an infinite amount of time. At some point you also have to ship stuff.
“What’s a technique so woodworkers can make sure their furniture fits together on the first try?”
“Measuring and marking out the plan before making cuts.”
“Hmm. No, that sounds tedious and difficult, and requires the woodworker to be proactive. No thank you.”
Interesting analogy, but it’s probably better to address my point directly instead of arguing about woodworking
Simple answer, unit tests.
I addressed this in some of my other replies, but I do not believe unit tests are a good solution here. It’s way too common for developers to write tests that give false positives, and its very common for organizations to have low or insufficent coverage due to the higher cost associated with testing.
Tests are good to have as backup though.
A factory pattern helps. By making a dedicated class that handles the creation and distribution of Task entities, that’s at least one point of failure that’s than centralised.
