Modern software often struggles with bloat, leading to increased memory consumption and security vulnerabilities from unused code. In response, various program debloating techniques have been developed, typically utilizing test cases that represents functionalities users want to retain. These methods range from aggressive approaches, which prioritize maximal code reduction but may overfit to test cases and potentially reintroduce past security issues, to conservative strategies that aim to preserve all influenced code, often at the expense of less effective bloat reduction and security improvement. In this research, we present RLDebloatDU, an innovative debloating technique that employs 1-DU chain minimality within abstract syntax trees. Our approach maintains essential program data dependencies, striking a balance between aggressive code reduction and the preservation of program semantics. We evaluated RLDebloatDU on ten Linux kernel programs, comparing its performance with two leading debloating techniques: Chisel, known for its aggressive debloating approach, and Razor, recognized for its conservative strategy. Our findings reveal that while RLDebloatDU modestly sacrifices reduction in average binary size compared to Chisel, it notably surpasses Razor. In terms of reducing gadgets, which are indicative of attackable space, RLDebloatDU demonstrates parity with Chisel and superiority over Razor. Most importantly, RLDebloatDU shows enhanced performance in improving security by significantly reducing occurrences of Common Vulnerabilities and Exposures (CVEs) compared to both Chisel and Razor. These findings highlight RLDebloatDU’s ability to effectively reduce attackable space akin to aggressive debloating methods, while simultaneously avoiding the reintroduction of previously resolved security issues.