SQL Injection Attack (SQLIA) is one of the most severe attack that can be used against web database-driven applications. Attackers use SQLIA to obtain unauthorized access and perform unauthorized data modifications due to initial improper input validation by the web application developer. Various studies have shown that, on average, 64% of web applications worldwide are vulnerable to SQLIA due to improper input. To mitigate the devastating problem of SQLIA, this research proposes an automatic black box testing for SQL Injection Vulnerability (SQLIV). This acts to automate an SQLIV assessment in SQLIA. This research also focuses on improving the effectiveness of SQLIVS by proposing an object-oriented approach in its development. To test and validate the accuracy of research work, three vulnerable web applications were developed. The result of the experimental analysis shows significant improvement by achieving high accuracy compared to existing studies. Similarly, the analytical evaluations showed that the proposed scanner is capable of analyzing attacked page response using four different techniques.