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Write tests first when the behavior is clear enough to describe in a focused, executable example. Start with a timeboxed spike when a meaningful decision depends on something the team does not yet know—such as whether a technology or integration will work, or what a user flow should be. Often the best sequence is both: spike to resolve the narrow uncertainty, choose a direction, then use test-driven development (TDD) to build it reliably.
What is the difference between TDD and a spike?
TDD is a way to develop behavior the team can already specify: write a failing test, make it pass with the smallest implementation, refactor, and repeat. A spike is a bounded investigation or experiment intended to reduce uncertainty before the team commits to an implementation or plan.
The distinction is about the work’s immediate purpose, not whether the team cares about quality. A TDD cycle produces executable examples and production code. A spike produces learning—perhaps a decision, clarified acceptance criteria, notes, or prototype findings. The Agile Alliance describes TDD as tightly interwoven coding, unit testing, and design through refactoring; the Agile Practice Guide describes spikes as “timeboxed research or experiments.”
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minute| Dimension | TDD | Spike |
|---|---|---|
| Primary purpose | Develop known behavior with repeatable feedback | Reduce uncertainty that blocks a sound decision |
| Main output | Passing tests and refactored production code | Learning, a decision, clarified criteria, or prototype findings |
| Duration | An ongoing loop repeated as behavior is developed | A bounded experiment with an agreed time limit |
| Stopping rule | The test passes, the code is refactored, and the next behavior can be addressed | The question is answered well enough to choose a path—or the timebox ends |
“Spike-driven development” is often used to describe work that begins with one or more spikes, but the Agile Practice Guide defines spikes as a technique rather than prescribing a complete development framework. Treat the spike as a way to learn, not as a replacement for testing and production implementation.
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Should I write tests first or build a spike?
Ask whether you can state the behavior and what would count as correct before choosing an implementation. If you can, start with TDD. If an important unknown prevents you from writing a meaningful test or choosing a design, spike that unknown first.
| Situation | Start with | Why | Useful exit artifact |
|---|---|---|---|
| Business behavior is clear and testable | TDD | A test can express an executable example and provide regression feedback | Passing tests and refactored production code |
| A core algorithm or domain rule is known | TDD | Unit-level feedback can be fast and isolate design decisions | A focused unit-test suite |
| A new library, protocol, platform, or integration is uncertain | Spike | The team needs evidence before committing to an interface or architecture | A decision, notes, prototype findings, or a discarded option |
| The user flow or acceptance criteria are unclear | Spike with stakeholders | The team needs to understand what “correct” means before encoding it | Clarified criteria or a flow model |
| An estimate is unreliable because the work is uncertain | Timeboxed spike | Investigation can reveal planning assumptions without pretending the answer is already known | A revised estimate and its assumptions |
| A spike has established a direction | TDD | Tests can turn the learned behavior into durable automated feedback | Production behavior developed against tests |
Uncertainty should be specific enough to investigate. “We are not sure whether this integration can meet our requirement” is a useful spike question. “We are not sure how to build the feature” is too broad: identify the decision that is blocked, then investigate only what would help make it.
A quick decision checklist
Before opening an editor, ask four questions. First, can the team name an observable input and expected result? Second, do stakeholders agree that the result is correct? Third, can the behavior be isolated from uncertain external systems with a mock or fixture? Fourth, would a failed test provide useful information rather than merely confirming an untested assumption? A “yes” to these questions points toward TDD. A “no” identifies a candidate for a spike, but make the unknown precise before spending time on it.
Also ask what decision could change the design. If discovering a protocol limitation would force a different interface, investigate that limitation first. If the protocol is settled and only the domain rule remains, test the rule directly and keep the protocol behind a boundary. This prevents a broad technical investigation from delaying behavior that is already understood.
When is TDD better than a spike?
TDD is the better starting point when the team can express an observable behavior and its acceptance conditions without first conducting an experiment. Its tight feedback loop helps catch regressions as code changes, and the test can serve as an executable example of the intended behavior.
Use TDD for defined rules and outcomes
A domain rule, calculation, or clearly specified business behavior can usually be expressed as a focused unit test. Start with one example, run it to confirm it fails for the expected reason, implement just enough behavior to make it pass, and refactor while keeping the test green. Repeat with the next behavior or edge case.
This is the Red-Green-Refactor sequence described by the UK Home Office: write a failing unit test, implement the solution to pass it, then refactor. The Home Office guidance also emphasizes testing early and often, and says tests should be clear, repeatable, purposeful, and attentive to edge cases. A test that is difficult to understand or that does not check a meaningful behavior is not useful merely because it was written first.
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TDD works best when the relevant tests can run reliably and quickly enough to guide development. For external dependencies, AWS recommends unit-level TDD with mocks or fixtures, and automatic test execution in continuous integration (CI). That keeps a unit test focused on the behavior under development rather than requiring a live external service for every feedback cycle.
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Mocks and fixtures are not a substitute for checking that the real integration works. They isolate dependencies during a unit-level loop; broader integration testing is still needed when the system must be verified against actual components.
How TDD changes the design conversation
A focused test forces the team to name the interface it needs, the result it expects, and the failure or edge condition that matters. Refactoring then gives the team a deliberate point to improve the design without changing the tested behavior. The test is not a complete design document, but it preserves a concrete example that future maintainers can execute.
Keep the first test narrow. Writing a large suite before implementing anything is not the TDD loop; it delays feedback and can encode details that the next example would change. One behavior at a time lets the team discover a cleaner interface while the test remains a stable statement of the requirement.
When should I run a spike before TDD?
Use a spike when a consequential unknown makes a test or design decision premature. A test written before the team understands the expected behavior may simply freeze an assumption in code; a quick investigation can show whether the assumption is valid or what the requirement should be.
Technology or integration is unproven
If a new library, protocol, platform, or integration could change the architecture, investigate that uncertainty before encoding a design around it. The spike should answer a focused question—for example, whether the candidate approach supports a required capability—not attempt to build the feature end to end.
Keep the experiment representative enough to answer the question. If the concern is an interface capability, exercise that capability and record constraints. Do not spend the timebox polishing screens, handling every error path, or building unrelated domain logic. Those are delivery tasks after the direction is selected.
People do not agree on the intended flow
When stakeholders have not settled how a user action should work or what acceptance criteria mean, a spike can help surface the ambiguity. Use a lightweight flow model or experiment to learn what the intended outcome is. Once the team agrees on the behavior, write tests that express it.
Include the people who can clarify the decision. A developer working alone can demonstrate that a flow is technically possible, but cannot decide which outcome represents business value. The exit artifact should make the agreed behavior visible to anyone who will review or test the implementation.
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Planning depends on unknown work
If an estimate would be mostly guesswork because a critical technical or functional element is unknown, a timeboxed spike can produce evidence for a revised estimate. Record assumptions alongside the estimate: an estimate based on a limited experiment should not be presented as certainty about every implementation detail.
How long should a technical spike last?
There is no universal number of hours or days established for every spike. Set the limit according to the decision being made, the cost of delaying it, and the amount of evidence needed to choose a direction. A small interface question may need only a short experiment; a cross-system uncertainty may need a larger but still explicit boundary.
Write the timebox into the work item and define what happens when it expires. The team can choose a direction with the evidence available, narrow the question, or schedule another bounded investigation. Extending a spike indefinitely turns it into an untracked development effort and makes its planning value disappear.
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Timeboxing also protects the quality of the conclusion. A spike is not required to answer every related question. It only needs to answer the question that blocks the next decision well enough for the team to proceed consciously.
How do I run a spike that leads to useful work?
Make the spike an investigation with a question, a boundary, and a decision at the end. The Agile Practice Guide identifies estimation, acceptance-criteria definition, understanding a user’s action flow, and learning a critical technical or functional element as reasons to use spikes.
- State the unknown. Write down the decision the team cannot yet make and why it matters. Phrase the investigation as a question that evidence could answer.
- Define what evidence is enough. Decide what observation or result would support a direction, rule it out, or leave the question unresolved. If stakeholders are needed to clarify the flow or criteria, include them in the investigation.
- Set a timebox and scope. Keep the experiment limited to the unknown. A spike is timeboxed, but the sources do not establish a universal number of hours or days; choose a limit that fits the decision and the team’s planning needs.
- Investigate and record what happened. Capture the relevant findings, assumptions, constraints, and options considered. If the experiment does not answer the question, record what remains unknown rather than implying a decision was validated.
- Make the decision explicit. Choose a direction, identify what must be clarified next, or decide that another option should be explored. The useful output is the learning and decision, not merely code written during the timebox.
- Move into delivery work. Once the behavior and direction are clear, write focused tests and implement production code using TDD where appropriate. Keep any prototype findings distinct from production implementation.
What to record in the spike result
A concise result should name the question, the experiment performed, the observed behavior, relevant constraints, alternatives considered, and the decision. Include assumptions that affect the estimate or acceptance criteria. If an option was discarded, say why. This record prevents the team from repeating the same investigation and gives future developers the context behind the chosen interface.
A spike can include code, but prototype code should not be treated as production-ready just because it ran once. The investigation’s job is to reduce uncertainty; the implementation still needs the team’s normal design, testing, and quality work.
What do you do after a spike?
Use the result to make the next work item more precise. If the spike establishes a viable direction and the behavior is understood, turn the relevant behavior into tests and build the production implementation. If it clarifies a user flow, update the acceptance criteria or flow model before testing against it.
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If the experiment rejects an option, record that result so the team does not quietly repeat the same investigation or base a design on the discarded approach. If it ends without resolving the question, name the remaining uncertainty and decide whether another bounded investigation is worth doing. A spike that produces no decision is not automatically wasted, but its unresolved result should not be mistaken for confidence.
Do not try to preserve every line of exploratory code. Carry forward useful findings, assumptions, and verified constraints; then decide deliberately whether any prototype code is suitable for reuse. When the team moves to production work, the TDD loop gives that work a fresh, repeatable feedback cycle.
What can TDD and spikes not prove?
TDD provides feedback about the behaviors its tests cover; it does not establish that the whole system is usable, scalable, secure, or fit for every operating condition. PMI states that “TDD, whether ATDD or UTDD, does not replace traditional testing.” Plan broader quality assurance for qualities that unit tests do not establish.
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Nor should teams treat TDD as a guaranteed route to better design. Agile Alliance notes that empirical research has not confirmed all of the design benefits claimed for TDD, even though practitioners report improvements in design quality. The practical case for TDD is its disciplined, repeatable loop and the feedback it gives for the behaviors being tested—not a promise that the technique will solve every design problem.
Watch for team-level failure modes
- Partial adoption: applying the loop inconsistently can weaken the expected feedback and make it harder for the team to rely on tests as a normal part of development.
- Slow suites: if test runs become too slow, the feedback loop becomes less useful. Keep unit-level checks focused and use CI to run tests automatically.
- Abandoned suites: tests that are no longer maintained stop providing dependable regression feedback. Keep them purposeful and repeatable as the code changes.
- Prototype promotion: moving spike code directly into production can carry over shortcuts, missing edge cases, and unexamined assumptions. Rebuild or review it deliberately under the normal quality process.
- Unbounded investigation: continuing to explore after the decision is good enough consumes delivery capacity without producing a clearer outcome. Revisit the question and set a new boundary if more learning is genuinely needed.
Coverage percentage alone is not a reliable TDD success measure. An 80% coverage figure found in guidance is an illustrative example, not a universal benchmark for TDD quality or effectiveness.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How can a team combine spikes and TDD?
Use the spike to answer only the question that blocks a meaningful test or implementation decision, then use TDD for the behavior the team has selected. This avoids two opposite mistakes: testing an unverified assumption as if it were a requirement, and letting an exploratory prototype stand in for a tested production feature.
- Identify the unknown that could change the design, acceptance criteria, or estimate.
- Timebox a narrow experiment and decide what evidence will resolve the question.
- Record the result and select a direction—or state what remains uncertain.
- Translate the now-understood behavior into focused tests and develop the production implementation in Red-Green-Refactor cycles.
- Use broader testing for system qualities and integrations that the unit-level TDD loop does not establish.
The Agile Alliance description of TDD and the Home Office’s Red-Green-Refactor guidance both center on a repeated test-and-code loop. The Agile Practice Guide’s definition of spikes complements that loop: experimentation comes first when learning is necessary to choose what behavior or design to test.
Practical examples of the sequence
Suppose a calculation rule is already agreed: given defined inputs, the service must return a specified result, including an edge case. There is no meaningful uncertainty blocking the team, so write the failing unit test, implement the smallest rule, and refactor. Add examples as the acceptance conditions become more complete.
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Now suppose the team does not know whether a proposed library supports a required protocol capability. A test against the unfinished production design would test an assumption about the library. A narrow spike can exercise that capability, capture constraints, and compare the viable direction with the discarded option. After the decision, TDD can drive the adapter and the domain behavior separately.
For an unclear user flow, the first deliverable may be a clarified flow model and acceptance criteria rather than code. Once stakeholders agree on what the user action should accomplish, those criteria become the examples that guide implementation. In each case, the handoff from spike to TDD is a change from learning what should be built to proving, repeatedly, that the chosen behavior still works.
Frequently Asked Questions
Is a spike the same as a proof of concept?
They can overlap, but the terms do not guarantee the same scope or deliverable. A spike is defined by its timeboxed learning goal; a proof of concept is usually described by what it demonstrates, so clarify the question and exit decision rather than relying on the label.
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Yes. The experiment may expose that the original question was too broad or that evidence is still missing. Record what was learned and what remains unresolved so the team can decide whether another investigation is worthwhile.
Does writing tests first mean writing all tests before implementation?
No. TDD is an iterative cycle around one focused behavior at a time, not a requirement to complete a full test suite before writing production code. The failing test guides the smallest implementation, after which the code is refactored.
Should I keep code written during a spike?
Keep it only if the team deliberately determines that it is suitable for production use after review and the necessary implementation and quality work. A successful experiment by itself does not establish that exploratory code is production-ready.
Do I need a spike before every TDD feature?
No. Use a spike only when a material technical, functional, flow, acceptance, or estimation unknown blocks a meaningful design or test. If the behavior is understood, starting with a focused TDD example is usually more direct.
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Record the evidence and the reason for rejection, then select another viable direction or define the next bounded question. Do not quietly carry the rejected assumption into production code.
How do spikes affect estimates?
A timeboxed spike can replace guesswork with evidence and support a revised estimate. Keep the assumptions visible, because the result reduces particular uncertainties rather than guaranteeing the exact effort for every implementation detail.
Are unit tests enough after a spike?
No. Unit-level TDD protects the behaviors covered by those tests. Integration, usability, scalability, security, and other system qualities still require appropriate broader testing.
The Bottom Line
Choose TDD when the behavior is clear enough to test. Choose a timeboxed spike when an important unknown blocks that clarity or a sound design decision. Capture the learning, decide, and then use TDD to turn the selected behavior into maintainable, repeatable production code.
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