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Start with TDD when the behavior is understood well enough to express as a focused example. Start with a spike when an important unknown makes a meaningful test or design decision impossible. TDD gives you a repeatable Red-Green-Refactor loop and a growing regression net. A spike is a deliberately limited experiment that reduces uncertainty about technology, integration, user flow, acceptance criteria, or effort. The most useful workflow is often sequential: investigate the narrow unknown, record the decision, then use TDD to build the selected design. A spike is not production implementation, and TDD is not a complete quality-assurance strategy; each solves a different problem.
What is test-driven development?
Test-driven development (TDD) is a programming practice in which coding, unit-test writing, and design through refactoring are tightly interwoven. The test is written before the production behavior, so each small implementation decision has executable feedback.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsAgile Alliance describes the loop as writing one unit test, running it so it fails, writing just enough code to pass, refactoring, and repeating. UK Home Office guidance uses the familiar name Red, Green, Refactor and recommends testing early and often.
- Red: Write one focused test that expresses a behavior or rule, then run it and verify that it fails for the expected reason.
- Green: Implement the smallest production change that makes that test pass. Avoid adding behavior that no test requires.
- Refactor: Improve names, structure, duplication, or design while the passing test protects the behavior.
- Repeat: Add the next focused example and continue the cycle.
The result is more than a collection of checks. Well-chosen tests are executable examples of the behavior the team agreed to build, while frequent refactoring lets the design evolve without silently changing those examples.
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What TDD produces
- Passing, focused unit tests that describe selected behavior.
- Production code shaped by small, reversible design decisions.
- Fast regression feedback when later changes affect an established rule.
- A clearer boundary between the code under test and external dependencies.
TDD is strongest when the acceptance condition can be stated precisely. A tax calculation, parser rule, authorization decision, or known domain algorithm can usually be expressed as inputs, outputs, and edge cases before implementation begins.
What is spike-driven development?
A spike is a timeboxed research activity or experiment used to reduce uncertainty before the team commits to a design, estimate, or acceptance condition. The Agile Practice Guide lists learning a critical technical or functional element, understanding a user-action flow, defining acceptance criteria, and improving estimates as typical reasons to spike.
Instead of trying to make production code immediately complete, the team asks a narrow question and gathers enough evidence to make a decision. The experiment may be a disposable prototype, a small integration, a protocol probe, a user-flow walkthrough, or a comparison of implementation options.
What a spike should contain
- A question: for example, whether a proposed library can handle the required authentication flow.
- A boundary: the specific behavior, integration, or assumption being investigated.
- A timebox: a fixed limit that prevents open-ended exploration.
- An exit condition: the evidence needed to choose a direction, reject an option, clarify a requirement, or revise an estimate.
- A record: notes, findings, a decision, and assumptions that the implementation team can use later.
Spike code can be intentionally rough. Unless the team explicitly decides to retain and harden it, its value is the knowledge it provides, not its suitability for release. Treating an exploratory script as finished product is one of the easiest ways to carry insecure, untested, or poorly structured code into production.
Should I write tests first or build a spike?
Choose based on the source of uncertainty. If the behavior is known and can be demonstrated with a focused example, begin with TDD. If a material unknown blocks the test or a sound design choice, run a narrow spike first.
| Situation | Start with | Why | Exit artifact |
|---|---|---|---|
| Business behavior is clear and testable | TDD | Tests can serve as executable examples and regression protection | Passing tests plus refactored production code |
| Core algorithm or domain rule is known | TDD | Unit-level feedback is fast and isolates design decisions | Focused unit-test suite |
| New library, protocol, platform, or integration is uncertain | Spike | The team needs evidence before committing to an interface or architecture | Decision, notes, prototype findings, or discarded option |
| User flow or acceptance criteria are unclear | Spike with stakeholders | The team must learn what “correct” means before encoding it | Clarified criteria or flow model |
| Estimate is unreliable because of unknown work | Timeboxed spike | Learning improves planning without pretending to know the answer | Revised estimate and assumptions |
| Spike has selected a direction | TDD | Convert the learned behavior into durable, automated feedback | Production code driven by tests |
The decision is not a contest between two development religions. TDD optimizes confidence in behavior that is already understood; a spike optimizes learning where the team cannot yet state that behavior or select an implementation responsibly.
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What is the difference between TDD and a spike?
The practices differ in purpose, output, duration, and stopping rule. Confusing those dimensions leads either to testing an assumption that has not been validated or to leaving an experiment running long after it has answered its question.
| Comparison axis | TDD | Spike |
|---|---|---|
| Purpose | Build confidence in known behavior while shaping the design | Reduce uncertainty before committing to behavior or design |
| Primary output | Automated tests and production code | Learning, a decision, clarified criteria, or an updated estimate |
| Duration | An ongoing loop repeated throughout implementation | A bounded experiment with an agreed timebox |
| Stopping rule | The focused test passes, then the code is refactored and the next behavior is chosen | The question is answered well enough to choose a path or explicitly record that it remains unresolved |
| Code standard | Production-quality code protected by tests | Exploratory code may be disposable unless deliberately adopted and hardened |
When is TDD better than a spike?
TDD is the better first move when the team can describe the required behavior without discovering a new technology or negotiating what success means.
Known domain rules
Rules such as “a rejected request must not change the account balance” or “a date range includes its start but excludes its end” can be represented as focused examples. Starting with tests exposes ambiguities in the rule immediately and gives later refactoring a safety net.
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Stable interfaces and familiar tools
If the language, framework, storage boundary, and external contracts are already understood, a spike adds little information. Unit-level tests can isolate the business logic, while mocks or fixtures stand in for external dependencies.
Small, reversible design decisions
TDD works particularly well when the next design step can be made in a few lines and evaluated quickly. A failing test tells the team exactly which behavior is missing; a passing test permits structural improvement without changing the contract.
Regression-sensitive code
For code that will be changed often, executable examples pay for themselves by revealing unintended behavior changes. Run the focused suite automatically in continuous integration so every proposed change receives the same feedback.
When should a spike come first?
A spike belongs before TDD when a wrong assumption would invalidate the tests, the architecture, or the estimate.
Unfamiliar technology or protocol
A new library may not support the required authentication, streaming, error handling, or throughput model. A small experiment can verify the actual interface and failure behavior before the team builds an abstraction around guesses.
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When two services, devices, or platforms must cooperate, the risk may be in credentials, network constraints, message ordering, or undocumented limits rather than in the local business rule. The spike should exercise the narrowest path that could disprove the integration plan.
Unclear user flow or acceptance criteria
If stakeholders disagree about what a user should see, what counts as completion, or how an exception should be handled, tests would merely freeze an argument. Walk through the flow with stakeholders, capture examples and edge cases, and only then encode the agreed behavior.
Unreliable estimates
When an estimate depends on an unknown technical step, a timeboxed experiment can expose the work and its assumptions. The output is not a promise that the whole feature is easy; it is a better-supported estimate with known risks.
Architecture choices with expensive consequences
If selecting the wrong storage model, messaging pattern, or platform would force a costly rewrite, investigate the decisive constraint first. Keep the experiment narrow enough that it can compare the alternatives rather than becoming an unplanned implementation project.
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Do I need a spike before TDD?
No. A spike is justified by a consequential unknown, not by the fact that a feature is new. Many new features contain well-understood rules that can go directly into a Red-Green-Refactor loop.
- Ask what must be known before a useful test can be written.
- Separate unknown behavior from unknown implementation. Unclear business behavior calls for examples and stakeholder clarification; unknown technology calls for an experiment.
- Estimate the cost of being wrong. If the assumption is cheap to reverse, begin with TDD and learn through small tests. If it could invalidate the design or integration, spike the assumption first.
- Define the smallest question whose answer changes the decision.
- After the answer is sufficient, stop the spike and write the first production test against the selected behavior.
A spike should not become a ceremonial gate before every story. Requiring one for routine work adds delay without reducing meaningful risk.
How long should a technical spike last?
There is no universal hour or day limit in the guidance; the essential constraint is that the experiment is timeboxed and has a defined exit condition. Set the shortest boundary that can answer the decision-making question, then stop when the evidence is sufficient or the boundary is reached.
Set the timebox around a decision
Write the decision in advance: “Can option A complete the required handshake?” or “Which flow satisfies the agreed acceptance examples?” The timebox ends when the answer is clear enough to choose, not when every related curiosity has been explored.
Use checkpoints instead of open-ended exploration
At the midpoint or another agreed checkpoint, ask whether new information is changing the decision. If not, stop and document the result. If the question cannot be answered within the boundary, record what remains unknown and create a separate decision rather than silently extending the spike.
Keep the experiment narrow
Do not build screens, error taxonomies, deployment automation, or performance tuning that the question does not require. A small representative path is more useful than a broad but inconclusive prototype.
What do you do after a spike?
Turn learning into an explicit decision, then replace exploratory code with production work driven by tests unless the team has consciously chosen to retain and harden the prototype.
- Record the finding: state what was tried, under which assumptions, and what happened.
- Choose a direction: select an option, reject one, clarify the requirement, or identify the remaining risk.
- Update the plan: revise the estimate, architecture note, acceptance criteria, or integration contract.
- Discard or quarantine experiment code: do not let a disposable prototype become an accidental dependency.
- Write the first focused test: capture the selected behavior as an executable example.
- Implement and refactor: proceed through Red, Green, Refactor with production-quality standards.
- Add broader checks where needed: integration, usability, security, scalability, and other system qualities require their own evidence.
The handoff from spike to TDD is complete when the team can explain both what the system should do and why the chosen design is credible.
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Most nontrivial work benefits from a narrow learning step followed by a disciplined implementation loop. The two practices can alternate when a new unknown appears, but each cycle should have a distinct purpose.
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Example: an unfamiliar payment provider
First, spike the smallest sandbox transaction that exercises credentials, success, decline, timeout, and webhook behavior. Record the provider constraints and choose an adapter boundary. Then use TDD for local rules such as amount validation, idempotency, retry decisions, and mapping provider responses to the application’s domain errors. Integration tests can verify the real contract separately.
Example: a known pricing rule
If the pricing formula and rounding policy are agreed, skip the spike. Write examples for ordinary values, boundaries, and invalid inputs; implement the smallest rule that passes; and refactor the calculation into a maintainable design.
Example: an uncertain onboarding flow
Run a stakeholder spike using a flow sketch or clickable experiment to resolve which information is required, what happens when verification fails, and where the user can resume. Once those acceptance examples are agreed, use TDD for the state transitions and validation rules.
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Tests should express purposeful behavior rather than the incidental structure of the prototype. UK Home Office guidance emphasizes clarity, repeatability, appropriate automation, and attention to edge cases.
- Name the behavior and the condition that distinguishes it from neighboring cases.
- Use realistic fixtures, but keep each test focused on one observable rule.
- Test boundaries and failure paths, not only the successful example discovered during the spike.
- Mock or fixture external dependencies at the unit boundary, as AWS guidance recommends, so a unit test remains fast and deterministic.
- Run the suite automatically in continuous integration and investigate failures rather than weakening the assertion to make the build green.
- Refactor production code and test code together when duplication or misleading structure appears.
An illustrative coverage target sometimes appears in guidance, but an example such as 80% is not a universal TDD success benchmark. Coverage can show which lines lack execution; it cannot prove that the examples represent the right behavior.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What can TDD and spikes not prove?
TDD provides confidence in the behaviors its tests cover; it does not replace broader quality assurance. PMI states that TDD, whether acceptance-test-driven or unit-test-driven, does not replace traditional testing.
- Usability: users may struggle with a flow that is functionally correct.
- Scalability and performance: fast unit tests do not establish behavior under production load.
- Security: authorization, data exposure, dependency risk, and abuse cases need dedicated assessment.
- Integration compatibility: mocks cannot reveal every real service, network, deployment, or version interaction.
- Operational resilience: observability, recovery, backup, and failure handling require system-level exercises.
- Accessibility and other quality attributes: these need appropriate specialist checks and representative environments.
A spike can reveal that an option is risky, but a successful prototype is not evidence that the finished system is secure, maintainable, or ready to release.
What are common failure modes?
Writing tests for an unverified assumption
If the test encodes an interface or user expectation that the spike would have disproved, the team gains false confidence. Pause, identify the assumption, and run the smallest experiment that can validate it.
Letting a spike grow into production
Exploratory code often omits validation, error handling, security controls, and maintainable structure. Mark it clearly, preserve the findings, and reimplement the selected behavior under production standards.
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Using TDD only occasionally
Partial adoption can leave the suite inconsistent and make the feedback loop less trustworthy. Agree which layers and behaviors will be test-driven, then keep the practice coherent rather than presenting a few isolated tests as a complete safety net.
Allowing the test suite to become slow
A slow suite discourages frequent execution and weakens the Red-Green-Refactor rhythm. Keep unit tests isolated from real external systems, move appropriate checks to separate integration stages, and run them automatically in continuous integration.
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Abandoning tests after the feature ships
Tests that no longer run or no longer describe current behavior become misleading documentation. Remove obsolete examples, update changed contracts deliberately, and treat a failing test as information about the design rather than an obstacle to suppress.
Spiking without a decision owner
Exploration can continue indefinitely when nobody is responsible for choosing a path. Name the decision maker, define the exit artifact, and document unresolved risk if the evidence remains inconclusive.
Is TDD always proven to improve design?
No universal design improvement claim has been confirmed by empirical research, although practitioners often report better design quality from the feedback and refactoring discipline. Treat TDD as a method for obtaining fast, executable feedback and evolving code safely, not as a guarantee of a particular architecture.
Design quality still depends on domain understanding, review, system boundaries, operational constraints, and the skill with which tests are chosen. A large number of brittle tests can constrain change just as a lack of tests can make change dangerous.
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Describe the choice in terms of the risk being managed. Say that TDD is being used because the expected behavior is clear and needs repeatable regression feedback, or that a spike is being used because a specific unknown makes an estimate, acceptance criterion, or architecture unreliable.
Share the exit artifact with the people who depend on the decision: passing tests and refactored code for TDD; a finding, decision, clarified flow, or revised estimate for a spike. This keeps the work legible and prevents a timeboxed experiment from being mistaken for finished delivery.
Frequently Asked Questions
Can a spike include automated tests?
Yes. A spike may use a test or script to probe an unfamiliar interface, but those checks answer the experiment’s question and may not be suitable as the product’s regression suite. After the direction is chosen, rewrite the durable behavior as maintainable production tests.
Who should participate in a spike about acceptance criteria?
Include the people who can define the user outcome and the people who must implement or support it. A short joint session with stakeholders, design, engineering, and relevant operations staff can expose conflicting assumptions before they are encoded in tests.
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How do you know a spike has failed?
A spike has failed to answer its question when the timebox ends without evidence strong enough to choose a path. Record the unresolved assumptions, explain why the result is inconclusive, and decide whether to narrow the question, obtain missing access, or reject the option.
Should spike findings be kept with the code?
Keep the decision and assumptions where the team will find them with the relevant architecture or planning record. If the experiment code is retained, label its status and limitations so nobody mistakes it for supported production code.
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