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During Android application development, ensuring adequate security is a crucial and intricate aspect. However, many applications are released without adequate security measures due to the lack of vulnerability identification and code verification at the initial development stages. To address this issue, machine learning models can be employed to automate the process of detecting vulnerabilities in the code. However, such models are inadequate for real-time Android code vulnerability mitigation. In this research, an open-source AI-powered plugin named Android Code Vulnerabilities Early Detection (ACVED) was developed using the LVDAndro dataset. Utilising Android source code vulnerabilities, the dataset is categorised based on Common Weakness Enumeration (CWE). The ACVED plugin, featuring an ensemble learning model, is implemented in the backend to accurately and efficiently detect both source …