An actionable performance profiler for optimizing the order of evaluations

The efficiency of programs often can be improved by applying rel- atively simple changes. To find such optimization opportunities, developers either rely on manual performance tuning, which is time-consuming and requires expert knowledge, or on traditional profilers, which show where resources are spent but not how to optimize the program. This paper presents a profiler that provides actionable advice, by not only finding optimization opportunities but by also suggesting code transformations that exploit them. Specifically, we focus on optimization opportunities related to the order of evaluating subexpressions that are part of a decision made by the program. To help developers find such reordering opportuni- ties, we present DecisionProf, a dynamic analysis that automatically identifies the optimal order, for a given input, of checks in logical expressions and in switch statements. The key idea is to assess the computational costs of all possible orders, to find the optimal order, and to suggest a code transformation to the developer only if reordering yields a statistically significant performance improve- ment. Applying DecisionProf to 43 real-world JavaScript projects reveals 52 beneficial reordering opportunities. Optimizing the code as proposed by DecisionProf reduces the execution time of indi- vidual functions between 2.5% and 59%, and leads to statistically significant application-level performance improvements that range between 2.5% and 6.5%.