Exploring Generative AI

TDD with GitHub Copilot

by Paul Sobocinski

Will the arrival of AI coding assistants resembling GitHub Copilot imply that we received’t want exams? Will TDD develop into out of date? To reply this, let’s look at two methods TDD helps software program growth: offering good suggestions, and a method to “divide and conquer” when fixing issues.

TDD for good suggestions

Good suggestions is quick and correct. In each regards, nothing beats beginning with a well-written unit check. Not handbook testing, not documentation, not code assessment, and sure, not even Generative AI. Actually, LLMs present irrelevant data and even hallucinate. TDD is particularly wanted when utilizing AI coding assistants. For a similar causes we’d like quick and correct suggestions on the code we write, we’d like quick and correct suggestions on the code our AI coding assistant writes.

TDD to divide-and-conquer issues

Drawback-solving by way of divide-and-conquer signifies that smaller issues could be solved prior to bigger ones. This allows Steady Integration, Trunk-Based mostly Improvement, and finally Steady Supply. However do we actually want all this if AI assistants do the coding for us?

Sure. LLMs hardly ever present the precise performance we’d like after a single immediate. So iterative growth just isn’t going away but. Additionally, LLMs seem to “elicit reasoning” (see linked research) after they clear up issues incrementally by way of chain-of-thought prompting. LLM-based AI coding assistants carry out greatest after they divide-and-conquer issues, and TDD is how we do this for software program growth.

TDD ideas for GitHub Copilot

At Thoughtworks, we now have been utilizing GitHub Copilot with TDD because the begin of the yr. Our objective has been to experiment with, consider, and evolve a collection of efficient practices round use of the device.

0. Getting began

Beginning with a clean check file doesn’t imply beginning with a clean context. We regularly begin from a person story with some tough notes. We additionally speak by way of a place to begin with our pairing accomplice.

That is all context that Copilot doesn’t “see” till we put it in an open file (e.g. the highest of our check file). Copilot can work with typos, point-form, poor grammar — you title it. However it may possibly’t work with a clean file.

Some examples of beginning context which have labored for us:

  • ASCII artwork mockup
  • Acceptance Standards
  • Guiding Assumptions resembling:
    • “No GUI wanted”
    • “Use Object Oriented Programming” (vs. Purposeful Programming)

Copilot makes use of open recordsdata for context, so conserving each the check and the implementation file open (e.g. side-by-side) tremendously improves Copilot’s code completion potential.

1. Pink

TDD represented as a three-part wheel with the 'Red' portion highlighted on the top left third

We start by writing a descriptive check instance title. The extra descriptive the title, the higher the efficiency of Copilot’s code completion.

We discover {that a} Given-When-Then construction helps in 3 ways. First, it reminds us to supply enterprise context. Second, it permits for Copilot to supply wealthy and expressive naming suggestions for check examples. Third, it reveals Copilot’s “understanding” of the issue from the top-of-file context (described within the prior part).

For instance, if we’re engaged on backend code, and Copilot is code-completing our check instance title to be, “given the person… clicks the purchase button”, this tells us that we should always replace the top-of-file context to specify, “assume no GUI” or, “this check suite interfaces with the API endpoints of a Python Flask app”.

Extra “gotchas” to be careful for:

  • Copilot might code-complete a number of exams at a time. These exams are sometimes ineffective (we delete them).
  • As we add extra exams, Copilot will code-complete a number of traces as a substitute of 1 line at-a-time. It can usually infer the proper “prepare” and “act” steps from the check names.
    • Right here’s the gotcha: it infers the proper “assert” step much less usually, so we’re particularly cautious right here that the brand new check is appropriately failing earlier than shifting onto the “inexperienced” step.

2. Inexperienced

TDD represented as a three-part wheel with the 'Green' portion highlighted on the top right third

Now we’re prepared for Copilot to assist with the implementation. An already present, expressive and readable check suite maximizes Copilot’s potential at this step.

Having mentioned that, Copilot usually fails to take “child steps”. For instance, when including a brand new technique, the “child step” means returning a hard-coded worth that passes the check. Up to now, we haven’t been capable of coax Copilot to take this strategy.

Backfilling exams

As an alternative of taking “child steps”, Copilot jumps forward and offers performance that, whereas usually related, just isn’t but examined. As a workaround, we “backfill” the lacking exams. Whereas this diverges from the usual TDD move, we now have but to see any severe points with our workaround.

Delete and regenerate

For implementation code that wants updating, the best approach to contain Copilot is to delete the implementation and have it regenerate the code from scratch. If this fails, deleting the strategy contents and writing out the step-by-step strategy utilizing code feedback might assist. Failing that, one of the best ways ahead could also be to easily flip off Copilot momentarily and code out the answer manually.

3. Refactor

TDD represented as a three-part wheel with the 'Refactor' portion highlighted on the bottom third

Refactoring in TDD means making incremental modifications that enhance the maintainability and extensibility of the codebase, all carried out whereas preserving conduct (and a working codebase).

For this, we’ve discovered Copilot’s potential restricted. Take into account two situations:

  1. “I do know the refactor transfer I wish to attempt”: IDE refactor shortcuts and options resembling multi-cursor choose get us the place we wish to go sooner than Copilot.
  2. “I don’t know which refactor transfer to take”: Copilot code completion can’t information us by way of a refactor. Nevertheless, Copilot Chat could make code enchancment recommendations proper within the IDE. We’ve got began exploring that characteristic, and see the promise for making helpful recommendations in a small, localized scope. However we now have not had a lot success but for larger-scale refactoring recommendations (i.e. past a single technique/operate).

Generally we all know the refactor transfer however we don’t know the syntax wanted to hold it out. For instance, making a check mock that will enable us to inject a dependency. For these conditions, Copilot might help present an in-line reply when prompted by way of a code remark. This protects us from context-switching to documentation or internet search.


The frequent saying, “rubbish in, rubbish out” applies to each Information Engineering in addition to Generative AI and LLMs. Said otherwise: larger high quality inputs enable for the potential of LLMs to be higher leveraged. In our case, TDD maintains a excessive stage of code high quality. This top quality enter results in higher Copilot efficiency than is in any other case attainable.

We subsequently suggest utilizing Copilot with TDD, and we hope that you simply discover the above ideas useful for doing so.

Because of the “Ensembling with Copilot” workforce began at Thoughtworks Canada; they’re the first supply of the findings coated on this memo: Om, Vivian, Nenad, Rishi, Zack, Eren, Janice, Yada, Geet, and Matthew.

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