Common signs of SQL injection vulnerability include the presence of unsanitized user input directly concatenated into SQL queries, error messages that reveal database structure or query information, and the absence of input validation mechanisms. These indicators suggest that the system does not adequately sanitize and validate user input, making it vulnerable to SQL injection attacks.

In a blind SQL injection attack, attackers may exploit error messages or use comments to gather information. However, a common technique is using database metadata to understand the database structure.

Using mock data for stress testing allows testers to simulate scenarios of high load and assess how the database performs under pressure. Stress testing helps identify potential bottlenecks, weaknesses, or points of failure in the database system, enabling teams to optimize performance and ensure reliability in real-world usage scenarios.

Scalability testing examines how a system handles increased workload. Horizontal scaling involves adding more resources, such as servers, to distribute the load across multiple machines. Therefore, as the term "horizontal" implies expansion across a wider base, it directly relates to the system's performance with increasing loads.

Structural testing, also known as schema testing, ensures that the database schema follows the defined structure and constraints. It involves validating the database objects such as tables, columns, indexes, and relationships to ensure they conform to the specifications outlined in the schema design. This testing helps identify any discrepancies or errors in the database structure.

SQL Server Management Studio (SSMS) is a popular tool for managing SQL Server databases, providing features for performance monitoring, query optimization, and administration tasks. DBeaver is a versatile database management tool that supports various database platforms and offers performance profiling capabilities to monitor database performance and troubleshoot issues.

SQL query testing ensures that the SQL queries return the correct results according to the intended logic and requirements. It involves checking the accuracy, completeness, and efficiency of SQL queries. This helps in identifying any discrepancies or errors in the data retrieval process.

White box testing involves examining the internal structure of the SQL query to identify any logic errors or anomalies that may cause incorrect results. By analyzing the query's code and execution paths, you can pinpoint and rectify issues that affect data retrieval accuracy.

Automation frameworks facilitate consistent test execution, which is crucial for reliable and accurate results. Consistency ensures that tests are performed uniformly, reducing the likelihood of errors and inconsistencies.

Test automation frameworks offer a systematic methodology for designing and executing database tests. These tests encompass various aspects such as validating data integrity, testing database performance, and ensuring compliance with business rules.