Do not add a fixed number of hours for each AI flag. First define the agreed TypeScript deliverable, then classify every flag as work already in scope, a necessary dependency, or a genuine scope addition. Estimate the baseline and any accepted additions separately, using evidence, comparable completed work, and an explicit account of uncertainty.
“Stretch IDs” is not established as a standard TypeScript estimation term, and the available sources do not identify a specific tool or workflow that uses it. Here, it means AI-flagged items that may expand or clarify the requested work; verify what the term means in your team before applying this method.
What an AI flag should—and should not—change
Treat a flag as a prompt to inspect scope, not as a validated measure of effort. A flag may expose a requirement the team already agreed to meet, reveal a dependency needed for the deliverable, or propose additional behavior that was not agreed. Only the last category necessarily changes the scope; none can be translated reliably into hours from the flag alone.
There is no established universal hours-per-flag rule or TypeScript-specific estimation formula in the available evidence. A 2020 mapping study selected 120 primary studies from 3,746 candidates; more than 70% of the selected studies used multiple estimation approaches, and more than 90% of participants were students rather than professionals. Those findings caution against treating published results as a direct numeric rule for professional TypeScript work. Carbonera et al., “Software development effort estimation” (2020).
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Define the deliverable before estimating
Describe the requested result as observable outcomes, not a vague task label such as “fix the types” or “handle the AI findings.” Scope-estimation research treats scope as tangible outcomes and discusses functionality, dependencies, and newness as relevant attributes. “Scope Attributes and Systemic Effect in Estimation Practices for Software Projects” (2023).
- Outcome: What should a user, caller, or downstream system be able to do when the work is complete?
- Acceptance checks: What observable behavior, type safety, test results, or integration outcome proves completion?
- Boundaries: What behavior, refactoring, compatibility work, or related cleanup is explicitly excluded?
- Dependencies and novelty: Which APIs, packages, data contracts, or unfamiliar areas could affect the work?
For example, “resolve flagged issues” is not a bounded deliverable. “Make the existing search response conform to the documented response type, preserve current error behavior, and pass the related tests” gives an estimator a result to decompose and verify.
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Classify each flagged item
Make a short ledger before changing the estimate. For each flag, record the concrete change it implies, the evidence for it, its dependencies, and its relationship to the agreed outcome. Evidence could be an acceptance requirement, reproducible failing test, compiler error, or traceable dependency. A flag without evidence may still be worth investigating, but investigation is not the same as approved implementation scope.
| Classification | How to recognize it | Estimate treatment |
|---|---|---|
| Already in scope | The change is necessary to meet an agreed outcome or acceptance check. | Include it in the baseline estimate; the flag has clarified work rather than added scope. |
| Necessary dependency | The deliverable cannot work or pass its acceptance checks without a related change, such as aligning a consumed type with an existing API contract. | Include the dependency in the baseline, state the dependency assumption, and expose uncertainty if its extent is not yet known. |
| Genuine addition | The flag proposes new behavior, broader compatibility, extra refactoring, or another outcome not required by the agreed checks. | Keep it outside the baseline until someone accepts the scope change; estimate it separately if accepted. |
| Unverified or unclear | The flag does not identify a reproducible problem or a link to a requirement, test, or dependency. | Do not silently convert it into implementation hours. Clarify the issue or estimate a bounded investigation separately. |
When the classification is disputed, ask: “Which acceptance check fails if we do not do this?” If there is no answer, the item may be useful additional work, but it has not yet been shown to belong in the agreed deliverable.
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Build the estimate from reviewable work
Break the baseline into units that can be inspected and estimated independently. For TypeScript changes, useful categories include behavior or UI changes, types and interfaces, data or API dependencies, error cases, tests, integration, and code review. These are practical decomposition prompts, not a universal required checklist; omit categories that do not apply and add project-specific ones where needed.
- Write the baseline tasks. Include only work needed for the accepted deliverable and its necessary dependencies.
- List accepted additions separately. Show which newly flagged behaviors or cleanup requests change the agreed outcome.
- Record assumptions and unknowns. For example, state whether an API contract is stable or whether a package change may be needed.
- Estimate task units, then sum. Account for implementation, verification, integration, and review where they apply; do not assume a code change is the whole task.
Keep estimates for baseline and additions distinct in the plan or ticket. This lets a reviewer see whether a larger total comes from a change in scope, a dependency assumption, or uncertainty about the original work.
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Cross-check the number and communicate uncertainty
Use the team’s own completed tasks as the most relevant calibration when that history is available. Compare work with similar functionality, dependencies, and novelty—not merely tasks with a similar title or number of flagged items. Record what made past work take more or less time so the comparison has useful context.
Then make an independent top-down estimate of the overall deliverable and compare it with the bottom-up sum of task estimates. If they differ, inspect the assumptions behind the gap: a task may be missing, an integration cost may be understated, or the top-down view may be assuming a simpler path. A review of expert software-effort estimation practices supports using documented prior-task data, independent top-down and bottom-up estimates, justification and critique, uncertainty assessment, and feedback on accuracy. Jørgensen, “A review of studies on expert estimation of software development effort” (2004).
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Do not present a precise point estimate as certainty when important facts remain unknown. State a range or confidence level appropriate to the unknowns and say what would narrow it. In a study of 43 internal projects executed in 2002 in the IT division of a large government organization in Israel, higher uncertainty was generally associated with higher effort-estimation errors. The sample is context-specific; it does not provide an AI-flag multiplier. “Factors affecting duration and effort estimation errors in software development projects” (2007).
Check what context the AI had
A flag’s usefulness depends partly on what the assistant could see, but context is not proof that a finding is correct or an effort estimate is accurate. A 2025 preprint qualitatively analyzed 401 open-source repositories with assistant directives, grouping context into conventions, guidelines, project information, LLM directives, and examples. It supports asking what project context informed a suggestion; it does not show that directives improve estimation accuracy. Jiang and Nam, “An Empirical Study of Developer-Provided Context for AI Coding Assistants in Open-Source Projects” (2025 preprint).
- Check whether the assistant had access to the relevant files, tests, conventions, and API contracts.
- Separate a technically plausible suggestion from a requirement the team has actually accepted.
- Ask for a reproducible example or a specific dependency path when the flag is unclear.
- Keep a human review step for deciding whether the finding changes the estimate or scope.
A 2026 JetBrains Research study reports a survey of 56 professional developers and seven design sessions, with its abstract noting interest in controls such as minimum confidence thresholds and visibility into suggestion quality. That is relevant to oversight of assistant output, not evidence that confidence settings convert flags into labor estimates. Koshchenko, Stankovic, Zakharov, and Sergeyuk, “Configurable AI Coding Assistants” (2026).
Close the loop after delivery
When the work is complete, compare estimated effort with actual effort and note what drove the difference: overlooked dependencies, new scope, a wrong assumption, unexpectedly difficult integration, or an overestimated task. Feed that information into future estimates and keep it with the task history. Estimation guidance emphasizes evaluating accuracy and using feedback to improve the process; a flag count alone does neither. Jørgensen (2004).
Quick Recap
A quick decision rule
- If a flag is required by an existing acceptance check, include the work in the baseline.
- If it reveals a necessary dependency, include it while making the dependency and its uncertainty visible.
- If it adds a new outcome, show it as a separate estimate and get scope agreement before folding it into the commitment.
- If its evidence or relevance is unclear, resolve that question before treating it as implementation scope.
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