File carvers are forensic software tools used to recover data from storage devices in order to find evidence. Every legal case requires different trade-offs between precision and runtime performance. The resulting required changes to the software tools are performed manually and under the strictest deadlines. In this paper we present a model-driven approach to file carver development that enables these trade-offs to be automated. By transforming high-level file format specifications into approximations that are more permissive, forensic investigators can trade precision for performance, without having to change source. Our study shows that performance gains up to a factor of three can be achieved, at the expense of up to 8% in precision and 5% in recall.