Source Code Optimization using Equivalent Mutants

TL;DR

This paper introduces a novel method that leverages equivalent mutants to optimize source code while preserving functionality, demonstrating potential advantages over traditional compiler optimizations through experimental validation.

Contribution

The paper proposes a new approach to source code optimization using equivalent mutants, which is a novel application beyond mutation testing.

Findings

The approach can outperform traditional compiler optimizations.

Experimental results with Java and C programs validate the method.

Equivalent mutants can be effectively used for source code enhancement.

Abstract

A mutant is a program obtained by syntactically modifying a program's source code; an equivalent mutant is a mutant, which is functionally equivalent to the original program. Mutants are primarily used in \emph{mutation testing}, and when deriving a test suite, obtaining an equivalent mutant is considered to be highly negative, although these equivalent mutants could be used for other purposes. We present an approach that considers equivalent mutants valuable, and utilizes them for source code optimization. Source code optimization enhances a program's source code preserving its behavior. We showcase a procedure to achieve source code optimization based on equivalent mutants and discuss proper mutation operators. Experimental evaluation with Java and C programs demonstrates the applicability of the proposed approach. An algorithmic approach for source code optimization using equivalent mutants is proposed. It is showcased that whenever applicable, the approach can outperform traditional compiler optimizations.