Execution Path Tracing as the Basis for Platform-Agnostic Performance Tests

Abstract

Over the past several decades, the global reliance on electronics has surged due to ongoing advancements in computational hardware. This pursuit for enhanced performance, driven by its economic impact on productivity, has led to shorter computer lifespans and a rise in electronic waste. It's imperative for software engineers to understand performance characteristics by optimizing software for real-world use, we can potentially prolong computer lifespans and combat the escalating electronic waste issue. This research introduces Paptools, a novel software performance evaluation tool suite for predicting computational complexity and effectively mapping execution pathways. We designed and developed an instrumenting compiler, an execution path tracing library, an execution path model fitting utility using symbolic regression, and a platform-agnostic performance assertion library. Paptools accurately predicted performance models for 93% of identified execution paths for a single measurement environment We proved the platform-agnosticism of performance assertions based on execution pathways with 96% success rate across eight different measurement environments. Furthermore, Paptools provided more comprehensive insight into the intricacies of run time performance than Google Benchmark, a proven performance analysis utility, underscoring its potential in the field.