Quantum Optimization for Software Engineering: A Survey

TL;DR

This survey reviews the application of quantum and quantum-inspired algorithms to classical software engineering optimization problems, highlighting current research trends, gaps, and potential for future advancements.

Contribution

It systematically analyzes 77 studies on quantum optimization in software engineering, revealing research focus areas and identifying gaps across SE activities.

Findings

Research mainly focuses on SE operations and testing.

Significant gaps exist in other SE activities.

Relevant work is published outside traditional SE venues.

Abstract

Quantum computing, particularly in the area of quantum optimization, is steadily progressing toward practical applications, supported by an expanding range of hardware platforms and simulators. While Software Engineering (SE) optimization has a strong foundation, which is exemplified by the active Search-Based Software Engineering (SBSE) community and numerous classical optimization methods, the growing complexity of modern software systems and their engineering processes demands innovative solutions. This Systematic Literature Review (SLR) focuses specifically on studying the literature that applies quantum or quantum-inspired algorithms to solve classical SE optimization problems. We examine 77 primary studies selected from an initial pool of 2083 publications obtained through systematic searches of six digital databases using carefully crafted search strings. Our findings reveal concentrated research efforts in areas such as SE operations and software testing, while exposing significant gaps across other SE activities. Additionally, the SLR uncovers relevant works published outside traditional SE venues, underscoring the necessity of this comprehensive review. Overall, our study provides a broad overview of the research landscape, empowering the SBSE community to leverage quantum advancements in addressing next-generation SE challenges.