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A team can ship steadily and still lack evidence that it is solving a consequential problem. Software engineering therefore needs more than output targets: it needs discovery to identify the right problem and delivery to build, test, and operate a solution. The goal is not to stop shipping, but to connect shipped work to an intended outcome and learn whether it made a difference.
Why output alone is an incomplete measure
Output describes work completed: features released, stories closed, or changes deployed. These measures can help teams understand their delivery process, but they do not establish whether the work addressed a user need or improved an organizational outcome.
An outcome is the change the work is intended to produce—for example, helping users complete a task more easily or reducing a specific operational problem. The distinction matters because a feature request is a proposed solution, not proof that the underlying problem has been understood. A team that optimizes only for volume can meet its delivery target while leaving the original need unresolved.
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What discovery asks a software team to learn
Discovery is the work of understanding a problem, its users, and the conditions around it before committing to a solution—and continuing to learn as the solution takes shape. The Australian Government Digital Transformation Agency describes discovery as an initial exploration to determine what work may be needed to address a problem and plan for it. Its guidance is practical process advice, not a controlled study proving a particular outcome.
Understand the problem and its context
Talk with relevant stakeholders and users, examine how the problem appears in practice, and distinguish visible symptoms from possible causes. Review existing solutions, constraints, and dependencies. The purpose is not to delay implementation indefinitely; it is to avoid treating the first proposed feature as if it were already a validated answer.
Define the outcome before choosing the solution
State what should change for users or the organization, and how the team could observe that change. Set a measure before solution development where possible. A useful success measure is tied to the problem, rather than simply counting how much work was shipped.
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Record what is known and what remains uncertain
Keep a concise discovery record: the problem, intended outcome, evidence gathered, important constraints, key uncertainties, and the next test or learning step. This makes assumptions visible and gives the team a basis for revisiting its choices as evidence changes.
How discovery and delivery work together
Discovery does not replace engineering or create a separate phase that must be completed perfectly before coding begins. It informs what to build, while delivery produces working software and new opportunities to learn. DORA’s team experimentation guidance puts stories in the context of a business outcome or problem, then asks teams to decide what work is needed and test whether it achieves that outcome.
In practice, teams can prototype an idea, test it with users, deliver a small increment, and observe what happens. Feedback may confirm the approach, reveal a new constraint, or show that the original requirement needs revision. This cycle helps teams adapt rather than treating an initial specification as permanently correct.
Give teams autonomy with useful context
Teams need enough information about organizational outcomes to make informed choices, along with room to pursue ideas and revise stories or specifications as they learn. DORA identifies the ability to experiment and change work without outside permission as part of team experimentation. That is not a claim that every change should be unconstrained: teams must still respect governance, safety, security, and operational responsibilities.
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Learning is not a reason to leave requirements vague. NASA’s Software Engineering Handbook says requirements analysis is continuous, including when requirements change. It recommends examining requirements for qualities such as clarity, correctness, consistency, completeness, feasibility, testability, traceability, and maintainability.
For each important requirement, teams should be able to explain what it means, how it relates to the intended outcome, and how they will verify it. Analyze requirements individually and as a set; a change that makes sense on its own can conflict with another requirement or affect safety, reliability, or traceability. This discipline is particularly important in safety-critical work, where exploratory change must remain controlled and auditable.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose measures that connect work to results
Use delivery measures to understand the flow and quality of engineering work, and pair them with evidence about the outcome the work was meant to influence. There is no single universally best metric established by the cited guidance. The appropriate evidence depends on the problem, the users, and the risks involved.
| Question | Output-focused view | Discovery-informed view |
|---|---|---|
| What counts as progress? | Work delivered, such as features or completed stories. | Work delivered plus evidence about the intended user or organizational change. |
| What informs priorities? | Requests and estimates. | Requests and estimates considered alongside user and stakeholder needs, observed feedback, and constraints. |
| Can the plan change? | Specifications may be treated as fixed once work begins. | Stories or specifications can be revised when learning justifies it, with appropriate controls. |
| When are success measures set? | They may focus on delivery completion. | Measures for the intended outcome are defined before solution development where possible. |
| How are requirements handled? | Completion may be emphasized. | Requirements remain clear, testable, traceable, and subject to analysis as they change. |
DORA’s Core Model connects capabilities, metrics, and outcomes and is presented as conservative practitioner guidance based on ongoing research. DORA and Google’s 2024 report record describes participation by more than 39,000 professionals across organizations of different sizes and industries globally; that figure describes the report’s reach, not a causal estimate about discovery. The 2025 report record describes nearly 5,000 technology professionals worldwide and more than 100 hours of qualitative data, and characterizes AI as an amplifier of organizational strengths and dysfunctions. These figures describe study scope; they do not prove that discovery alone improves performance or that output optimization causes poor outcomes.
A practical review for each piece of work
Before and during delivery, use these questions to keep the work connected to what the team is trying to learn and achieve:
- Problem: What user or organizational problem are we addressing?
- Outcome: What observable change would indicate progress?
- Evidence: What do we know from users, stakeholders, or existing data, and what are we assuming?
- Uncertainty: What is the most important unanswered question?
- Next learning step: What test, prototype, or observation could reduce that uncertainty?
- Engineering measures: What delivery, quality, reliability, or safety measures must be maintained?
- Requirements: Are the relevant requirements clear, verifiable, and traceable—and how will changes be analyzed?
Revisit the answers as work progresses. If evidence changes the team’s understanding, update the plan and requirements deliberately. That keeps discovery and delivery connected: discovery helps select and adapt the work, while engineering turns it into software that can be evaluated in use.
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