Software performance modeling plays a crucial role in developing and maintaining software systems. A performance model analytically describes the relationship between the performance of a system and its runtime activities. This process typically examines various aspects of a system’s runtime behavior, such as the execution frequency of functions or methods, to forecast performance metrics like program execution time. By using performance models, developers can predict expected performance and thereby effectively identify and address unexpected performance regressions when actual performance deviates from the model’s predictions. One common and precise method for capturing performance behavior is software tracing, which involves instrumenting the execution of a program, either at the kernel level (e.g., system calls) or application level (e.g., function calls). However, due to the nature of tracing, it can be highly resource-intensive, making it impractical for production environments where resources are limited. In this work, we propose statistical approaches to reduce tracing overhead by identifying and excluding performance-insensitive code regions, particularly application-level functions, from tracing while still building accurate performance models that can capture execution time degradations. We develop both dynamic methods that analyze runtime behavior patterns and static methods that examine code structure to identify performance-sensitive functions. Our methodology specifically targets execution time as the primary performance metric, building models that capture the relationship between function call frequencies and overall program latency. By selecting an optimal set of functions to be traced, we can construct optimized performance models that achieve an R 2 score of up to 99% and, in some cases, outperform full-tracing models (i.e., models using non-optimized tracing data), while significantly reducing the tracing overhead by more than 80% in most cases. Our optimized performance models can also effectively detect performance regressions in our studied programs, demonstrating their usefulness in distinguishing between normal workload variations and actual performance degradations. Finally, our approach is fully automated, making it ready to be used in production environments with minimal human effort.