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What “NASA-style” means for a prototype
NASA’s Software Engineering and Assurance Handbook provides practical guidance for implementing NASA software requirements and standards; its current portal associates the handbook with NPR 7150.2D and NASA-STD-8739.8B. That guidance is for work governed by those requirements, not a shortcut for declaring an independent prototype compliant. See the NASA Software Engineering and Assurance Handbook.
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For a small prototype, borrow the underlying habits: state what the software must do, make the design and changes reviewable, connect requirements to tests, and preserve evidence of what was checked. NASA’s assurance overview describes software assurance and software safety across the lifecycle, with effort informed by software classification and risk. The appropriate rigor therefore depends on context; follow the actual project’s applicable directives, plans, contract, and authority for mission work. See NASA’s overview of software assurance and software safety.
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1. Bound the prototype before asking AI to code
Write a short scope statement before opening a coding assistant. Identify the user or system need, what the prototype is meant to demonstrate, what it will not do, its assumptions, and the consequences of a misleading or failed result. The boundary matters because a convincing demo can conceal missing behavior or untested conditions.
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- Purpose: What decision, idea, or workflow should the prototype help someone evaluate?
- Non-goals: Which production capabilities, integrations, operating conditions, or safety functions are explicitly out of scope?
- Assumptions: What data, inputs, environment, or user actions does the demonstration depend on?
- Risks: What could go wrong if the software fails, displays an incorrect result, or gives someone unwarranted confidence?
For example, a classroom prototype might demonstrate how a user imports a sample file and sees a chart. Its scope statement could exclude live sensor connections, operational decisions, and claims that the displayed values are suitable for real-world use. That boundary should shape both the requirements and the tests.
2. Turn the scope into observable requirements
Give each requirement a unique identifier and phrase it so a reviewer can decide whether it passed. “The interface should be easy to use” is not directly testable; “Given a valid sample file, the user can import it and see a chart with a labeled time axis” is more observable. Keep uncertain behavior visible as an assumption rather than quietly letting the coding assistant decide it.
Pair each requirement with acceptance criteria: the input or conditions, the expected behavior, and what evidence will demonstrate the result. NASA requirements guidance connects requirements, acceptance, verification, and evidence; NPR 7150.2C is an earlier revision than the current handbook’s 7150.2D association, so its language should not be treated as the governing revision for every project. See NPR 7150.2C.
| ID | Requirement | Acceptance evidence |
|---|---|---|
| R-01 | The prototype accepts a valid sample file in the agreed format. | A test file imports successfully and the application shows the expected record count. |
| R-02 | The chart displays the imported values with labeled axes. | A reviewer checks the chart against the known sample values and confirms both axis labels. |
| R-03 | The prototype reports an understandable error for an invalid file. | A malformed file produces the expected message and does not display data as though the import succeeded. |
These are illustrative requirements, not NASA requirements. Use the same principle for your own subject: specify observable behavior and define the evidence that would convince a reviewer.
3. Keep a lightweight trace from need to test
Traceability makes gaps visible. A spreadsheet, project issue links, or a small table in the repository is enough for a prototype if it lets a reviewer follow each requirement through its implementation and verification. Update links when the implementation or acceptance criteria change.
| Requirement | Design or component | Code change | Verification | Result |
|---|---|---|---|---|
| R-01 | File import component | Commit or change request implementing import | Valid-file test and record-count check | Pass, fail, or not run; include a link to evidence |
| R-02 | Chart view | Commit or change request implementing the chart | Known-value comparison and label check | Pass, fail, or not run; include a link to evidence |
| R-03 | Import error handling | Commit or change request implementing validation | Malformed-file test | Pass, fail, or not run; include a link to evidence |
Do not mark a requirement complete merely because the feature exists in the code. It is complete for the prototype when the agreed behavior has been checked and the result recorded.
4. Set the design and boundaries for AI-generated changes
Before implementation, sketch the main components and interfaces, record important data assumptions, and decide which files or functions the coding assistant is allowed to change. Keep work under version control and pin development dependency versions where practical so you can identify what changed between runs.
NASA’s SWE-146 guidance says generated source code should be verified and validated using the same software standards and processes as hand-generated code. It also emphasizes control of the generation approach, tools, inputs, outputs, permitted scope, and manual changes. See NASA SWEHB Topic 7.25: AI and Software Engineering. For a prototype, apply the practical implication: know which assistant and inputs produced a change, inspect what it changed, and do not treat generated output as exempt from ordinary review.
5. Prompt for small, reviewable work
Give the assistant relevant project context, the requirement being addressed, constraints, and a narrow task. Ask it to identify assumptions and suggest tests, but independently check both its code and its explanation. A useful prompt structure is:
Implement requirement R-03 in the import component only. Use the existing project conventions and do not change dependencies or unrelated files. For malformed input, show the specified error and do not report a successful import. State any assumptions, propose tests for the normal and failure cases, and summarize the files changed.
Ask for one behavior at a time where possible. Smaller changes make it easier to compare the diff with the requirement, identify unintended edits, and connect tests to the work. GitHub documents Copilot as usable across planning, building, review, testing, and shipping, but those documented capabilities are not a guarantee that its output is correct. See GitHub’s guide to where Copilot can be used.
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6. Review generated changes before accepting them
Read the complete diff rather than relying on a summary from the assistant. Check whether the change stays within the requested scope, meets the requirement, and introduces assumptions or side effects that were not agreed. Pay particular attention to changes involving dependencies, input validation, error handling, data exposure, and boundary conditions.
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- Compare changed files and behavior with the requirement and design boundary.
- Check that the proposed tests actually exercise the acceptance criteria, including relevant negative cases.
- Run the project’s existing checks; a clean result is useful evidence, not a substitute for reviewing behavior.
- Have a human reviewer assess requirement coverage and any consequential assumptions.
NASA’s AI and software assurance guidance emphasizes evaluation, human oversight, security, uncertainty management, and change management. Its Version D guidance recommends limiting AI use to non-safety-critical applications unless an appropriate authority approves a documented AI safety case and risk controls. See NASA SWEHB Topic 8.25: AI and Software Assurance. For an independent prototype, do not use AI-generated code to perform safety-critical functions on the assumption that testing alone makes it acceptable.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.7. Test behavior at useful levels
Choose tests from the requirements, not just from the code the assistant happened to generate. NASA describes testing as checking functionality against requirements and design, finding defects to correct and track, and validating operation in the intended environment. The testing language cited here appears in NPR 7150.2C, an earlier revision; it explains the purpose of testing without establishing that this revision governs every project. See NPR 7150.2C.
Focused tests for individual behavior
Check one behavior at a time, such as whether valid input is parsed correctly or malformed input produces the expected error. Include representative boundary and negative cases, not only the easiest successful example.
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Check that connected parts work together: for example, that imported records reach the chart in the expected format. These tests can reveal mismatches that a test of each component alone will miss.
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System or demonstration tests in the intended environment
Run the end-to-end workflow in an environment that resembles the one in which the prototype is meant to be demonstrated. Check each acceptance criterion with representative inputs and observe the actual behavior. A test run in a different environment may not establish that the intended demonstration will work there.
Keep the evidence specific enough for another person to understand what was tested. Record the code version, environment, test inputs, expected and actual results, failures, and their disposition. A passing suite supports the cases it contains; it does not establish that all behavior is correct, that requirements are complete, or that software is safe for use beyond its tested scope.
8. Close the loop when a test fails
- Record the failure: Link it to the requirement and capture the input, environment, expected behavior, actual behavior, and code version.
- Decide what it means: Determine whether the cause is a defect, an unclear requirement, a mistaken assumption, or an environment mismatch. Do not silently rewrite acceptance criteria to turn a failure into a pass.
- Make a bounded correction: If an AI assistant helps, give it the failure evidence and request a focused change rather than a broad rewrite.
- Review and rerun: Inspect the new diff, rerun the failed test and relevant regression tests, and update the trace record with the outcome.
- State what remains unknown: Record limitations and the additional validation needed before using the prototype for a more consequential purpose.
What this workflow does—and does not—establish
This workflow helps make a small software prototype more inspectable: its intended behavior is explicit, AI changes are bounded and reviewed, and test results are connected to acceptance criteria. It does not certify safety, completeness, NASA approval, or compliance with a NASA project’s controls. For real NASA or mission work, confirm the currently applicable requirements and obtain decisions from the project’s responsible authority. NASA’s software engineering requirements and standards resource portal is a starting point for official resources, not a replacement for project-specific direction.
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