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Technology Digital MediaTop 10 Best Code Scanner Software of 2026
Discover the top 10 best code scanner software to streamline your development workflow. Compare features and find tools for your needs.
How we ranked these tools
Core product claims cross-referenced against official documentation, changelogs, and independent technical reviews.
Analyzed video reviews and hundreds of written evaluations to capture real-world user experiences with each tool.
AI persona simulations modeled how different user types would experience each tool across common use cases and workflows.
Final rankings reviewed and approved by our editorial team with authority to override AI-generated scores based on domain expertise.
Score: Features 40% · Ease 30% · Value 30%
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Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
GitHub Advanced Security
Code scanning security alerts with PR annotations and commit-level traceability
Built for teams managing secure pull-request workflows with GitHub-native triage.
SonarQube
Quality Gates that block merges or releases when defined code quality metrics regress
Built for engineering teams needing centralized code quality and security scanning visibility.
Snyk
Snyk Code scanning with issue-level remediation guidance tied to findings
Built for engineering teams securing CI pipelines for code and dependency risk visibility.
Related reading
Comparison Table
This comparison table benchmarks leading code scanner tools used to detect security flaws, code quality issues, and dependency risks across modern development pipelines. It covers GitHub Advanced Security, CodeQL, SonarQube, Snyk, Semgrep, and other popular scanners so teams can evaluate coverage, supported languages, and integration points side by side.
| # | Tool | Category | Overall | Features | Ease of Use | Value |
|---|---|---|---|---|---|---|
| 1 | GitHub Advanced Security Provides code scanning with CodeQL to detect vulnerabilities and security issues in pull requests and default branches. | enterprise | 8.7/10 | 8.9/10 | 8.6/10 | 8.4/10 |
| 2 | SonarQube Performs static analysis for code quality and security findings with rules for multiple languages and a web UI for triage. | static analysis | 8.2/10 | 8.6/10 | 7.7/10 | 8.1/10 |
| 3 | Snyk Runs automated code and dependency security scanning and produces remediation guidance through centralized dashboards. | SaaS security | 8.2/10 | 8.7/10 | 7.9/10 | 7.8/10 |
| 4 | Semgrep Checks code against custom and community rules and flags security, quality, and compliance issues with fast PR feedback. | rule-based | 8.2/10 | 8.7/10 | 7.8/10 | 7.9/10 |
| 5 | CodeQL Uses a query language to model code and surface findings for vulnerability classes by scanning repositories and pull requests. | code query engine | 8.2/10 | 8.8/10 | 7.6/10 | 7.9/10 |
| 6 | Checkmarx Performs static application security testing to identify application-layer vulnerabilities with configurable scanning workflows. | SAST | 8.1/10 | 8.7/10 | 7.6/10 | 7.9/10 |
| 7 | Trivy Scans source code and dependencies for known vulnerabilities and misconfigurations using lightweight, CI-friendly tooling. | open-source | 7.8/10 | 8.1/10 | 8.4/10 | 6.9/10 |
| 8 | Codecov Code Scanning Aggregates analysis from tools into a unified interface and highlights findings tied to commits and pull requests. | findings management | 7.6/10 | 8.2/10 | 7.6/10 | 6.9/10 |
| 9 | Code Climate Analyzes repositories for maintainability and security signals and reports issues with actionable ownership insights. | code quality | 8.0/10 | 8.2/10 | 7.6/10 | 8.0/10 |
| 10 | Coverity Performs static analysis that detects defects and security-relevant issues by modeling data flow and control flow paths. | static analysis | 7.3/10 | 7.7/10 | 6.8/10 | 7.2/10 |
Provides code scanning with CodeQL to detect vulnerabilities and security issues in pull requests and default branches.
Performs static analysis for code quality and security findings with rules for multiple languages and a web UI for triage.
Runs automated code and dependency security scanning and produces remediation guidance through centralized dashboards.
Checks code against custom and community rules and flags security, quality, and compliance issues with fast PR feedback.
Uses a query language to model code and surface findings for vulnerability classes by scanning repositories and pull requests.
Performs static application security testing to identify application-layer vulnerabilities with configurable scanning workflows.
Scans source code and dependencies for known vulnerabilities and misconfigurations using lightweight, CI-friendly tooling.
Aggregates analysis from tools into a unified interface and highlights findings tied to commits and pull requests.
Analyzes repositories for maintainability and security signals and reports issues with actionable ownership insights.
Performs static analysis that detects defects and security-relevant issues by modeling data flow and control flow paths.
GitHub Advanced Security
enterpriseProvides code scanning with CodeQL to detect vulnerabilities and security issues in pull requests and default branches.
Code scanning security alerts with PR annotations and commit-level traceability
GitHub Advanced Security stands out by bringing code scanning into the GitHub workflow with security findings tied to commits, pull requests, and code navigation. It supports code scanning using multiple analysis engines and produces actionable alerts with severity, file locations, and remediation paths. It also adds organization-wide visibility for findings management through security alerts and dependency context so teams can track risk over time. The tight GitHub integration makes triage and fix loops faster than standalone scanners.
Pros
- Findings link directly to commits and pull requests for fast triage
- Multiple code scanning engines surface security issues across common languages
- Alert organization and assignment support consistent workflow at scale
Cons
- Setup and tuning take effort to reduce noise in large repositories
- Some remediation context depends on analyzer accuracy for each issue type
- Advanced governance workflows can feel GitHub-centric
Best For
Teams managing secure pull-request workflows with GitHub-native triage
More related reading
SonarQube
static analysisPerforms static analysis for code quality and security findings with rules for multiple languages and a web UI for triage.
Quality Gates that block merges or releases when defined code quality metrics regress
SonarQube stands out for its deep, rule-based static analysis across multiple languages with a central quality dashboard. It finds code smells, bugs, and security issues using configurable rule sets and quality profiles. The platform ties analysis results to measures like code coverage and technical debt so teams can prioritize fixes across releases and branches. SonarQube also supports CI integration via scanner tooling for repeatable scans in automated pipelines.
Pros
- Strong multi-language static analysis with extensive, configurable rules
- Actionable quality dashboards link issues to code ownership and trends
- CI-friendly scanners support consistent automated analysis across branches
Cons
- Rule tuning and quality profile management can become complex at scale
- Initial setup and server operations require ongoing configuration effort
- High issue volumes can slow triage without careful gating and policies
Best For
Engineering teams needing centralized code quality and security scanning visibility
Snyk
SaaS securityRuns automated code and dependency security scanning and produces remediation guidance through centralized dashboards.
Snyk Code scanning with issue-level remediation guidance tied to findings
Snyk stands out for combining code-first security scanning with continuous monitoring of dependencies and source changes. It runs automated SAST for supported languages, highlights insecure patterns in code, and provides issue details tied to remediation guidance. It also emphasizes software composition analysis by detecting vulnerable dependencies and mapping fixes to tracked components.
Pros
- Actionable vulnerability findings with remediation steps and code context
- Strong dependency scanning with clear ownership of vulnerable components
- Integrates into CI workflows for repeatable checks on each change
Cons
- Configuring accurate scan scope for large repos can be time-consuming
- Coverage varies by language and framework, affecting consistency across stacks
- False positives require triage work to maintain trust in results
Best For
Engineering teams securing CI pipelines for code and dependency risk visibility
More related reading
Semgrep
rule-basedChecks code against custom and community rules and flags security, quality, and compliance issues with fast PR feedback.
Semgrep rule authoring with semantic patterns for precise vulnerability detection
Semgrep stands out with a rule-driven code scanning engine that lets teams author and share custom detection logic. It supports scanning across many languages using semantic patterns, not just keyword matching. Results connect to remediation guidance by pointing to exact code locations and offering configurable severities.
Pros
- Semantic pattern rules catch vulnerability classes beyond simple string matching
- Custom rules enable organization-specific security and compliance checks
- Actionable findings include file and line locations for fast remediation
- Supports scanning in CI workflows and local developer runs
Cons
- Rule tuning is required to reduce noise in large codebases
- Complex custom patterns can be harder to author and maintain
- Coverage depends on rule selection and language support quality
- Large scans can generate a high volume of findings to triage
Best For
Teams needing precise, customizable static security checks in CI pipelines
CodeQL
code query engineUses a query language to model code and surface findings for vulnerability classes by scanning repositories and pull requests.
CodeQL query packs with custom query authoring for semantic security and quality rules
CodeQL stands out by treating source code as a queryable dataset so teams can run semantic code scanning with customizable logic. It ships with security and quality query packs that flag vulnerable patterns and suspicious data flows across many languages. Integrations with GitHub code scanning workflows enable automated results on pull requests and repository history. A powerful feature set exists for building, testing, and managing custom CodeQL queries for specific internal standards.
Pros
- Semantic analysis detects more than simple syntax patterns in many languages
- Custom CodeQL query creation supports tailored secure coding rules
- Pull request annotations connect findings directly to code review workflows
- Query packs cover security and code quality with low setup effort
Cons
- First-time setup requires learning the CodeQL build and query workflow
- Large repos can create heavy scan times and resource demands
- Some findings require tuning to reduce noise for specific codebases
Best For
Engineering teams needing semantic security scanning with reusable and custom queries
Checkmarx
SASTPerforms static application security testing to identify application-layer vulnerabilities with configurable scanning workflows.
Checkmarx SAST with persistent findings management and workflow-based remediation tracking
Checkmarx stands out with enterprise-grade application security testing across the software lifecycle, covering static, software composition, and security analytics workflows. The solution’s core value comes from deep static analysis that identifies vulnerabilities in source code and integrates with CI pipelines and developer tooling. Checkmarx also emphasizes security governance through findings management, remediation workflows, and reporting that supports audit-ready traceability.
Pros
- Strong static code analysis depth for common and complex vulnerability patterns
- CI integration supports automated scans and consistent security gates
- Centralized findings management improves tracking across projects and teams
- Security governance reporting supports audit trails and remediation visibility
- Broad security coverage beyond SAST enables unified application security workflows
Cons
- Initial configuration and tuning can take time to reduce noise
- Large codebases may slow scans without careful scheduling and scope control
- Workflow setup for teams can require process and role design effort
Best For
Enterprises standardizing secure SDLC with SAST, governance, and CI-driven enforcement
More related reading
Trivy
open-sourceScans source code and dependencies for known vulnerabilities and misconfigurations using lightweight, CI-friendly tooling.
Trivy’s misconfiguration and vulnerability scanning across images, filesystems, and repositories
Trivy stands out for fast, container-first vulnerability and misconfiguration scanning driven by a single scanner workflow. It covers vulnerability detection for container images, filesystem directories, and Git repositories using multiple vulnerability sources. It also flags IaC issues and secrets by scanning configuration files and embedded artifacts, which makes it useful across build and CI steps. Results integrate into common CI pipelines with machine-readable output for gates and reporting.
Pros
- Unified scanning workflow for images, filesystems, and Git repos
- Strong vulnerability coverage with packaged checks and misconfiguration rules
- Readable CI output formats for automated gating and reporting
- Includes IaC and secret detection alongside vulnerability scanning
- Quick scan execution supports frequent pipeline runs
Cons
- Dependency relationship accuracy can lag behind complex build setups
- High signal issues still require tuning to reduce noise
- Large repos and multi-image pipelines can slow down without scoping
Best For
Dev teams adding CI security checks for containers, IaC, and source code
Codecov Code Scanning
findings managementAggregates analysis from tools into a unified interface and highlights findings tied to commits and pull requests.
Pull request code annotations that connect scan findings to specific diff lines
Codecov Code Scanning distinguishes itself with code intelligence built around pull request feedback, not just post-build reports. It integrates with repository workflows to detect issues from supported analyzers and annotate results in the code review context. The platform emphasizes actionable findings, filtering by severity and ownership, and tracking scan outcomes across changesets.
Pros
- Pull request annotations surface issues directly in code review diffs
- Change-based reporting helps teams focus on new or modified findings
- Integration with Codecov workflows centralizes scanning and issue tracking
Cons
- Setup and analyzer configuration can be complex for heterogeneous codebases
- Advanced tuning often requires deeper familiarity with scanning rules
- Usability varies when results volume is high and ownership mapping is incomplete
Best For
Teams that want PR-focused code scanning with change-based issue tracking
More related reading
Code Climate
code qualityAnalyzes repositories for maintainability and security signals and reports issues with actionable ownership insights.
Code review annotations for pull requests with code health issue tracking
Code Climate stands out for translating static analysis results into developer-focused quality signals and workflow-ready issue reporting. It provides code scanning across popular languages with findings for maintainability, code health, and security patterns, then ties those findings to pull requests and code diffs. The platform emphasizes actionable prioritization through issue tracking, ownership, and trend visibility so teams can monitor quality over time rather than only view raw alerts.
Pros
- Pull request annotations turn findings into concrete review actions.
- Quality insights prioritize maintainability and code health signals.
- Trend tracking highlights whether fixes reduce recurring issues.
Cons
- Setup and configuration can be time-consuming for multi-repo teams.
- Some findings require manual tuning to match team coding standards.
- Security coverage is strong but may not replace dedicated SAST tooling.
Best For
Teams needing pull-request code scanning with quality trends and issue prioritization
Coverity
static analysisPerforms static analysis that detects defects and security-relevant issues by modeling data flow and control flow paths.
Defect triage with merged, rule-based findings and evidence traces
Coverity stands out with deep static analysis that focuses on finding defects in compiled languages, then prioritizes results through defect grouping and rules. It includes data flow analysis for security and reliability issues, plus code property extraction to support scalable scanning in CI workflows. The platform also supports remediation guidance via issue traces that map discovered defects back to source locations.
Pros
- Strong static analysis with precise defect traces to source paths
- Advanced data flow rules for security, reliability, and quality issues
- Defect triage features that group related findings for faster review
- Integrates into CI and automated workflows for repeatable scans
- Scales better than lightweight scanners for large codebases
Cons
- Onboarding requires build and configuration effort for best results
- Result tuning and rule management takes ongoing administrator attention
- User experience can feel heavy compared to simpler code scanning tools
Best For
Large engineering teams needing static defect detection and triage workflows
Conclusion
After evaluating 10 technology digital media, GitHub Advanced Security stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
How to Choose the Right Code Scanner Software
This buyer’s guide helps teams choose code scanner software that fits their workflow, language mix, and enforcement needs. It covers GitHub Advanced Security, SonarQube, Snyk, Semgrep, CodeQL, Checkmarx, Trivy, Codecov Code Scanning, Code Climate, and Coverity. The guidance focuses on how each tool produces findings, where those findings appear in development, and which operational tradeoffs show up during setup and tuning.
What Is Code Scanner Software?
Code scanner software analyzes source code and related artifacts to detect security vulnerabilities, quality defects, and compliance or configuration issues. It converts analysis results into actionable findings that teams can triage in pull requests, code review diffs, or centralized dashboards. Teams typically use these tools to reduce risk during CI runs and release gates, and to connect alerts to the exact commit or code location needing change. GitHub Advanced Security and SonarQube show how scanners commonly tie findings into workflows through commit or pull request context and quality reporting.
Key Features to Look For
The most reliable tool choices map directly to how findings are generated, how quickly they reach developers, and how effectively teams can reduce noise.
Pull-request and commit traceability for fast triage
For teams that triage in code review, GitHub Advanced Security links code scanning security alerts to pull requests and commits for traceable resolution. Codecov Code Scanning and Code Climate also emphasize pull request code annotations tied to diff lines so developers can act on issues inside review.
Semantic detection that goes beyond keyword matching
For deeper vulnerability reasoning across languages, CodeQL uses a queryable code model and ships security and quality query packs to find suspicious data flows. Semgrep adds semantic pattern rules that detect vulnerability classes beyond string matching and produces findings with file and line locations.
Quality gating tied to release or merge decisions
For teams that need enforcement, SonarQube provides Quality Gates that block merges or releases when code quality metrics regress. This governance model helps teams move from reporting to automated decision-making across branches.
Custom rule creation and configurable rule tuning
For organizations with specific secure coding standards, Semgrep enables custom rule authoring and severity configuration using semantic patterns. CodeQL supports custom query authoring so internal standards can be enforced using reusable query packs.
Unified scanning across code, dependencies, misconfiguration, and secrets
For CI security coverage across multiple artifact types, Trivy runs a lightweight unified workflow for container images, filesystem directories, Git repositories, and flags IaC and secrets in configuration files. Snyk also unifies code-first SAST and dependency scanning by identifying vulnerable dependencies and mapping fixes to tracked components.
Centralized findings management with governance and triage workflows
For enterprise teams that must manage findings at scale, Checkmarx provides centralized findings management and workflow-based remediation tracking plus security governance reporting for audit traceability. Coverity supports defect triage with merged, rule-based findings and evidence traces that map grouped defects back to source locations.
How to Choose the Right Code Scanner Software
The decision starts by matching where developers want to see findings and what enforcement behavior the organization needs in CI and release workflows.
Choose the workflow surface where developers will act on findings
If developers triage inside pull requests and need commit-level context, GitHub Advanced Security places code scanning alerts directly into the PR workflow with commit traceability. If the goal is diff-line annotations in a unified platform view, Codecov Code Scanning and Code Climate focus on pull request code annotations that connect findings to specific review locations.
Decide which analysis style matches the risk model
For semantic code reasoning and reusable security packs, CodeQL uses a query language and security and quality query packs to model code and surface findings. For fast CI feedback with organization-specific logic, Semgrep supports custom semantic pattern rules that produce precise findings tied to file and line locations.
Map enforcement requirements to quality gates and remediation workflows
For teams that must block merges or releases when quality metrics regress, SonarQube quality gates provide an enforcement mechanism for automated decision-making. For enterprise SDLC governance and audit traceability, Checkmarx combines CI-driven scanning with centralized findings management and workflow-based remediation tracking.
Cover the artifact types that actually exist in the delivery pipeline
If the pipeline builds containers, scans IaC, and ships secrets embedded in configuration, Trivy runs a unified workflow that covers images, filesystems, Git repos, IaC issues, and secrets. If the pipeline risk includes vulnerable dependencies alongside insecure code patterns, Snyk runs SAST for supported languages and dependency scanning with issue-level remediation guidance tied to findings.
Plan for tuning time and scan-time behavior before scaling
Large repositories often require tuning to reduce noise in tools like GitHub Advanced Security, Semgrep, CodeQL, SonarQube, and Checkmarx. Coverity and Checkmarx can scale better than lightweight scanners but require build and configuration effort, so planning for onboarding time prevents delays in enforcement rollout.
Who Needs Code Scanner Software?
Code scanner software targets teams that want automated security and quality detection tied to code changes, and teams that need governance-level enforcement across CI and releases.
Teams that manage secure pull-request workflows inside GitHub
GitHub Advanced Security fits teams that want security alerts annotated in pull requests with commit-level traceability for faster triage. CodeQL also supports pull request annotations and repository history scanning when semantic query packs are the priority.
Engineering organizations needing centralized code quality visibility across branches
SonarQube suits teams that want deep multi-language static analysis with centralized quality dashboards and configurable rule sets. SonarQube also fits teams that require Quality Gates to block merges or releases when metrics regress.
CI-focused teams that need code and dependency risk coverage with remediation guidance
Snyk fits teams that secure CI pipelines by combining code-first scanning with continuous dependency monitoring. Trivy fits teams that focus on container-first workflows and also want IaC and secrets detection alongside vulnerability scanning.
Enterprises standardizing secure SDLC with governance, audit trails, and remediation tracking
Checkmarx fits enterprises that need enterprise-grade SAST with CI integration, centralized findings management, and workflow-based remediation tracking. Coverity fits large engineering teams that need defect triage with merged findings and evidence traces that map back to source paths.
Common Mistakes to Avoid
Common failures cluster around scanning signal quality, operational onboarding, and mismatches between finding presentation and developer triage behavior.
Picking a scanner without a clear triage entry point
If developers triage in pull requests, scanners that do not prioritize PR context add extra workflow friction for GitHub Advanced Security, Codecov Code Scanning, and Code Climate users. GitHub Advanced Security emphasizes PR annotations and commit traceability, while Codecov and Code Climate emphasize diff-line annotations to keep fixes inside review.
Assuming rules can run at full scale without tuning
Noise reduction needs effort in tools like SonarQube, Semgrep, CodeQL, and GitHub Advanced Security when repositories generate high issue volumes. Semgrep requires rule tuning to reduce noise in large codebases, and SonarQube requires quality profile management as the organization’s rule sets expand.
Overlooking scan-time and onboarding requirements for deep analysis
First-time setup and heavy scan times can impact teams adopting CodeQL and also large repos using CodeQL or Code scanning engines in GitHub Advanced Security. Coverity and Checkmarx require build and configuration effort for best results, so skipping onboarding planning delays usable governance controls.
Ignoring non-code artifacts such as containers, IaC, and secrets
Teams that only scan source code often miss misconfigurations and secrets that live in configuration files and deployment artifacts. Trivy covers IaC and secrets alongside images, filesystems, and Git repositories, and Snyk adds dependency vulnerability coverage that many code-only scanners do not address.
How We Selected and Ranked These Tools
We evaluated every tool on three sub-dimensions that match real implementation outcomes for engineering teams: features with a weight of 0.4, ease of use with a weight of 0.3, and value with a weight of 0.3. The overall rating is computed as overall = 0.40 × features + 0.30 × ease of use + 0.30 × value. GitHub Advanced Security separated itself by combining strong features with workflow speed because it delivers code scanning security alerts tied to pull requests and commits for commit-level traceability that improves triage loops. That blend supported both practical implementation impact and day-to-day usability in PR-centric development, which is why GitHub Advanced Security reached an 8.7 overall rating while remaining 8.9 on features and 8.6 on ease of use.
Frequently Asked Questions About Code Scanner Software
Which code scanner is best for security findings tied directly to pull requests and commit context?
GitHub Advanced Security is the most direct match because it annotates findings on pull requests and preserves commit-level traceability inside the GitHub workflow. Codecov Code Scanning also focuses on pull request feedback by connecting issues to diff lines, but it depends on supported analyzers for the detection logic.
What tool works best as a central hub for rule-based static analysis across multiple languages?
SonarQube fits teams that need centralized quality and security visibility because it uses configurable rule sets and quality profiles with a quality dashboard. Semgrep overlaps on security scanning, but it is more about authoring and sharing custom semantic detection rules than enforcing broad quality gates.
Which platform should be selected for continuous dependency risk monitoring alongside code scanning?
Snyk is built for code-first security scanning plus continuous monitoring of dependencies and source changes. Trivy can detect vulnerabilities in container images and repositories, but it does not provide the same dependency-centric remediation workflow that Snyk connects to tracked components.
Which code scanning option enables custom detection logic with reusable rules?
Semgrep enables custom rule authoring using semantic patterns, which makes detections more precise than keyword-based checks. CodeQL can also be extended through custom query development and reusable query packs, but its model is query-as-dataset rather than rule authoring with semantic patterns.
Which scanner is the strongest choice for semantic security analysis that treats code as queryable data?
CodeQL is designed for semantic code scanning by turning source code into a queryable dataset for security and quality query packs. Code Scanners like SonarQube and Semgrep excel at rule-driven findings, but CodeQL’s strength is dataflow and pattern detection expressed as queries across languages.
Which tool is most suitable for enforcing secure SDLC governance and audit-ready traceability?
Checkmarx targets governance by combining SAST, software composition workflows, and security analytics with persistent findings management. Coverity also supports defect triage and evidence traces that map issues back to source locations, which helps teams document why specific defects were raised.
Which scanner is best for adding container, IaC, and secrets checks directly into CI pipelines?
Trivy is the best fit for CI checks because it scans container images, filesystem directories, and Git repositories in a single workflow. It also flags IaC issues and secrets by scanning configuration files and embedded artifacts, and it outputs machine-readable results for gating.
How do teams choose between Codecov Code Scanning and Code Climate for PR annotations and issue tracking?
Codecov Code Scanning centers PR code intelligence by annotating findings on specific diff lines and tracking outcomes across changesets with severity and ownership filtering. Code Climate focuses on developer-facing quality signals by tying findings to pull requests and diffs with maintainability, code health, and security trends.
Which tool is best for large-scale defect triage in compiled languages with dataflow analysis?
Coverity is designed for deep static analysis that prioritizes defects using defect grouping and rules, then supports data flow analysis for security and reliability. Its defect traces map discoveries back to source locations, which supports scalable triage when teams process high volumes of findings.
Tools reviewed
Referenced in the comparison table and product reviews above.
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