🏴 LaunchDarkly☕ Java

Cleaning Up LaunchDarkly Flag Debt in Java

Enterprise Java codebases accumulate flags in annotations, Spring configurations, and deeply nested service layers. FlagShark scans your entire Java project — annotation processors, Spring beans, and raw SDK calls — to surface flags ready for retirement.

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What LaunchDarkly Flag Cleanup Looks Like in Java

FlagShark generates a PR that removes the dead branch and keeps only the winning code path

Before: Stale flag in production

@RestController
public class FeatureController {
    @Autowired private LDClient ldClient;

    @GetMapping("/api/recommendations")
    public ResponseEntity<?> getRecommendations(@AuthUser User user) {
        LDContext ctx = LDContext.builder(user.getId()).build();
        boolean useML = ldClient.boolVariation("ml-recommendations", ctx, false);

        if (useML) {
            return ResponseEntity.ok(mlService.recommend(user));
        }
        return ResponseEntity.ok(legacyService.recommend(user));
    }
}

After: Clean, readable code

@RestController
public class FeatureController {
    @GetMapping("/api/recommendations")
    public ResponseEntity<?> getRecommendations(@AuthUser User user) {
        return ResponseEntity.ok(mlService.recommend(user));
    }
}

Dead code removed, logic preserved

How FlagShark Detects LaunchDarkly Flags in Java

Scan for LaunchDarkly SDK calls

FlagShark uses Annotation scanning and Java AST for SDK method detection to find every call to variation(), variationDetail(), boolVariation(), stringVariation()across your Spring Boot, Micronaut codebase.

Cross-reference with your dashboard

Each detected flag key is matched against your LaunchDarkly project to check rollout status, last evaluation date, and whether the flag is archived or still active.

Generate safe removal PRs

For each stale flag, FlagShark creates a pull request that removes the SDK call, eliminates the dead code branch, and preserves the winning path. Every PR includes a before/after diff you can review.

Java + LaunchDarkly: Watch Out For This

Spring's dependency injection makes it hard to trace flag usage across the object graph — a flag evaluated in a `@Service` bean might control behavior three layers deep in a `@Repository`, and removing it requires understanding the full dependency chain.

Why Automate LaunchDarkly Cleanup?

Find every flag

Grep misses flags behind abstractions. AST-level scanning catches SDK calls that regex can't.

PR-based removal

Every cleanup goes through code review. No surprises, no broken builds.

Safe by default

Only flags at 100% rollout for your configured threshold are suggested for removal.

Continuous

New stale flags are detected automatically. Your codebase stays clean without manual audits.

LaunchDarkly SDK Methods We Detect in Java

variation()variationDetail()boolVariation()stringVariation()

Plus custom patterns you define in .flagshark.yaml

LaunchDarkly + Java Cleanup FAQ

How does FlagShark detect LaunchDarkly flags in a Spring Boot app?

FlagShark scans your Java source tree for LaunchDarkly SDK imports, then traces every `boolVariation`, `stringVariation`, and `jsonValueVariation` call. It also detects custom annotations that wrap flag evaluation and follows Spring bean wiring to map flag propagation.

Can I remove flags from a Micronaut application safely?

Yes. FlagShark recognizes Micronaut's compile-time DI model and correctly resolves injected `LDClient` instances. The generated diffs account for Micronaut's annotation-based routing and conditional bean loading.

What about flags used in Java annotation processors?

FlagShark detects flags referenced in custom annotations and their processors. It warns you when a flag removal would affect compile-time code generation, so you can update processors before deleting the flag.

Start Cleaning Up LaunchDarkly Flags in Java

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@RestController public class FeatureController { @Autowired private LDClient ldClient; @GetMapping("/api/recommendations") public ResponseEntity<?> getRecommendations(@AuthUser User user) { LDContext ctx = LDContext.builder(user.getId()).build(); boolean useML = ldClient.boolVariation("ml-recommendations", ctx, false); if (useML) { return ResponseEntity.ok(mlService.recommend(user)); } return ResponseEntity.ok(legacyService.recommend(user)); } }

@RestController public class FeatureController { @GetMapping("/api/recommendations") public ResponseEntity<?> getRecommendations(@AuthUser User user) { return ResponseEntity.ok(mlService.recommend(user)); } }

LaunchDarkly + Java Cleanup FAQ