Automated Driving Systems (ADSs) coordinate multiple modules (e.g., perception, prediction, planning, and control) and expose a large configuration surface that engineers must tune for different platforms and operational conditions. In industrial practice, misconfigured parameters are a common source of unsafe or law-violating behaviors, yet fixing such configuration bugs remains largely overlooked. Debugging is difficult because parameter effects propagate through long, nonlinear pipelines (often involving learning-based components), and manual trial-and-error provides little guidance in high-dimensional spaces while risking regressions in previously passing scenarios. We propose ConFixer, an automated approach for repairing ADS configuration bugs against formal correctness specifications, including traffic laws encoded in signal temporal logic (STL). ConFixer uses robustness semantics to derive gradient-like signals that localize bug-relevant parameters and guide fine-tuning toward compliance. To support adoption in scenario-based validation workflows, ConFixer evaluates candidate fixes on large scenario suites and prioritizes regression-safe solutions. Our evaluation on Baidu Apollo with the LGSVL simulator shows that ConFixer fixes 173 configuration bugs without introducing regressions.