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Apex

Apex static code analysis

Unique rules to find Bugs, Vulnerabilities, Security Hotspots, and Code Smells in your APEX code

  • All rules 97
  • Vulnerability2
  • Bug26
  • Security Hotspot3
  • Code Smell66
Filtered: 22 rules found
salesforce
    Impact
      Clean code attribute
        1. SOQL queries should not contain hardcoded date literals

           Code Smell
        2. Apex classes should contain at most one "@InvocableMethod" annotation

           Bug
        3. SOQL queries should not be executed inside loops

           Code Smell
        4. SOSL queries in test methods should use "Test.setFixedSearchResults"

           Code Smell
        5. DML operations in catch blocks can mask original exceptions

           Bug
        6. Test classes and methods should not use "seeAllData=true"

           Code Smell
        7. HTTP requests should have explicit timeout configuration

           Code Smell
        8. Methods with "@AuraEnabled" should be public or global

           Bug
        9. Classes implementing "Database.Batchable" should be declared as "global"

           Bug
        10. "getRoleAndSubordinates()" should be replaced with "getRoleAndSubordinatesInternal()"

           Code Smell
        11. Future methods making HTTP callouts should include "callout=true" parameter

           Bug
        12. SOQL queries should use SystemModStamp instead of LastModifiedDate for better performance

           Code Smell
        13. Future methods should not call other future methods

           Bug
        14. Test classes should be annotated with "@isTest"

           Code Smell
        15. SOQL queries should include LIMIT clauses to prevent hitting governor limits

           Bug
        16. Private members made public for testing should use "@TestVisible" annotation

           Code Smell
        17. Batch Apex scope parameters should not exceed 2000 records

           Code Smell
        18. Custom event names should not be prefixed with "on"

           Code Smell
        19. Apex REST services should implement comprehensive exception handling

           Vulnerability
        20. SOQL queries should be assigned to Lists to avoid QueryException

           Bug
        21. InvocableMethod annotations should include descriptive label and description

           Code Smell
        22. URLs should not be hardcoded in Apex code

           Bug

        Batch Apex scope parameters should not exceed 2000 records

        intentionality - efficient
        reliability
        Code Smell
        • salesforce
        • performance
        • batch

        This rule raises an issue when Database.executeBatch() is called with a scope parameter greater than 2000.

        Why is this an issue?

        How can I fix it?

        More Info

        When executing batch jobs in Salesforce, the scope parameter determines how many records are processed in each batch execution. While Salesforce allows scope values up to 2000 and will automatically chunk larger values into smaller batches, specifying excessively large scope parameters can lead to several problems.

        First, larger batch sizes increase the risk of hitting governor limits within a single batch execution. Each batch execution has its own set of governor limits for DML operations, SOQL queries, and CPU time. Processing too many records at once makes it more likely to exceed these limits, causing the batch job to fail.

        Second, performance can degrade with larger batch sizes. The Salesforce platform is optimized for processing smaller chunks of data efficiently. Larger batches can lead to longer execution times and increased memory usage, potentially affecting overall system performance.

        Finally, using appropriate batch sizes improves error handling and recovery. If a batch fails, smaller scope sizes mean fewer records need to be reprocessed, making debugging and recovery more manageable.

        What is the potential impact?

        Using excessively large batch scope parameters can cause batch jobs to fail due to governor limit violations, degrade system performance, and make error recovery more difficult. This can lead to incomplete data processing and unreliable batch operations.

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