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A feature can have zero known bugs and still be a poor thing to keep: its ongoing maintenance, support, testing, and complexity may outweigh the value it delivers. But low usage alone is not enough to justify removal. Before sunsetting it, find out who depends on it, what systems it touches, and how users will move to an alternative.
Why remove a feature that works?
“Zero bugs” means a team has not found defects under the conditions it has observed. It does not mean the feature costs nothing to maintain, has no dependencies, or continues to provide enough value to justify its place in the product.
Ownership includes more than fixing defects. A feature can require ongoing support and testing, complicate data handling, add to the product’s cognitive load, or divert engineering time from work that serves customers better. GitHub software engineer Dalia Abuadas captured the distinction in a July 17, 2026 GitHub Blog article: “The cost of writing code dropped; the cost of owning it didn’t.” This is a framing of the trade-off, not a measured statistic.
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There is also evidence that removing functionality is an established engineering choice, though it should not be mistaken for a sunset rate. A software-engineering study, The Design of Bug Fixes, reported that about 76% of surveyed developer respondents had removed features from software to fix bugs in the past. The finding concerns respondents’ bug-fixing practices; it does not show that 76% of features should be retired or that removal is the right response to low usage.
Should you remove a feature nobody uses?
Usage is evidence, not a verdict. An all-time count can hide recent changes, a small but important user group, or users who rely on the feature only occasionally. A feature may be rarely used yet essential to a strategically important customer or a contractual commitment.
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Salesforce describes considering usage alongside customer feedback and direct conversations with customers. Apply the same discipline before deciding that low adoption means low value:
- Identify which customer groups use the feature, how recently, and for what job.
- Review support feedback and talk directly with affected users, rather than relying only on an aggregate count.
- Check whether users have a workable alternative and what it would cost them to switch.
- Consider the feature’s strategic or contractual importance, including value to a small number of users.
Weigh those findings against the continuing burden: maintenance, support, testing, complexity, data handling, and the opportunity cost of keeping the feature. The sources do not establish a universal formula or usage threshold for making that call.
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How do you decide whether to sunset a feature?
- Define the job and users. Write down what the feature does, which customer groups rely on it, and the evidence for current use. Combine recent usage with support feedback and direct conversations.
- Compare value with ownership cost. Consider customer value and strategic fit alongside maintenance, support, testing surface, data handling, and the complexity the feature adds. Treat a low-use feature with critical users differently from one with no meaningful use or viable role.
- Map dependencies and indirect use. Find out which products, services, data, or other components rely on the feature before touching it. Static code analysis may miss dynamic references, so combine dependency information with runtime and application signals where possible.
- Choose a transition. Decide whether users need a replacement, migration path, advance notice, or a support window. An accelerated retirement may be justified when service integrity or a third-party dependency requires it, but it is an exception to plan for, not a default shortcut.
- Retire in a controlled sequence. Remove or disable entry points in stages, watch for unexpected use, and delete underlying code or data only when usage evidence and dependency checks support it. Signals should be able to stop an automated deletion; ambiguous cases need engineering judgment.
- Close the loop with users. Explain what is changing, why and when it will happen, what users should do, and where they can get help.
There is no evidence-backed cutoff such as “retire below X users.” The decision depends on the affected users, product strategy, ownership burden, dependencies, and the transition available.
How can you tell whether code is really unused?
A static scan can show references visible in code, but it cannot prove that nothing invokes a feature dynamically or through another product. Meta describes this problem in its SCARF deprecation system: shared assets and dynamic references can make an apparently local deletion unsafe.
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Meta combines static dependency information with runtime and application analysis, along with additional textual checks. Its process maps dependencies and sequences removals; usage signals can block automated action, while engineers assess judgment calls. Meta also acknowledges that its methods do not cover every form of usage. That is a description of Meta’s internal system, not a guarantee that the same checks will be complete in another company’s environment.
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The scale of SCARF’s work shows what this can mean inside one large organization. Meta reported that, in the prior year, it removed petabytes of unused data across 12.8 million data types stored in 21 data systems. It also reported deleting over 100 million lines of code over five years. These are Meta’s own organizational figures, not benchmarks or targets for other teams. Meta Engineering’s account of automating product deprecation explains its approach and the dependency challenges behind it.
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How do you deprecate a feature without breaking users?
Deprecation is a lifecycle, not just a code change. Salesforce says its general retirement approach includes advance written notice, recommended alternatives where available, and continued support until the retirement date. Its policy also describes exceptions, so the general approach should not be read as a guarantee of the same timeline in every case.
For a planned retirement, give users information they can act on: the affected feature, the retirement date, any replacement, migration steps, and a way to get support. Sequence technical changes so that entry points, dependencies, code, and data are handled only when the evidence supports each step. Observe for unexpected usage before making removal irreversible.
Meta’s example also demonstrates why deleting code is not automatically safe just because the feature appears unused. Production use and cross-product dependencies can turn a seemingly local deletion into a wider failure. Map what depends on the feature and account for dynamic use before completing the retirement.
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Meta and Salesforce offer examples of how large organizations describe feature retirement. Their policies and tooling can inform a team’s process, but Meta’s SCARF figures describe its internal systems, and Salesforce’s philosophy is Salesforce policy that may change. Neither establishes a universal cost model, notice period, or usage threshold.
The 76% survey finding is narrower still: it describes surveyed developers who had removed features to fix bugs. It is not a measure of how often companies retire features, how often retirement succeeds, or whether any particular feature should go.
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