Performance Localisation

TL;DR

This paper explores using mutation analysis to identify key locations in code that, when optimized, could significantly improve overall program performance, complementing traditional profiling methods.

Contribution

It introduces mutation analysis as a novel approach to locate impactful yet infrequently executed code segments for performance optimization.

Findings

Mutation analysis highlights impactful code locations.

It identifies performance improvement opportunities not evident from profiling.

The approach aims to facilitate automated performance tuning.

Abstract

Performance becomes an issue particularly when execution cost hinders the functionality of a program. Typically a profiler can be used to find program code execution which represents a large portion of the overall execution cost of a program. Pinpointing where a performance issue exists provides a starting point for tracing cause back through a program. While profiling shows where a performance issue manifests, we use mutation analysis to show where a performance improvement is likely to exist. We find that mutation analysis can indicate locations within a program which are highly impactful to the overall execution cost of a program yet are executed relatively infrequently. By better locating potential performance improvements in programs we hope to make performance improvement more amenable to automation.