Quantum Optimization for Software Engineering: A Survey

📅 2025-06-20
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🤖 AI Summary
The growing complexity of software engineering (SE) optimization problems necessitates rigorous investigation into emerging paradigms such as quantum computing. Method: We conducted the first systematic literature review (SLR) specifically focused on quantum optimization in SE, screening 2,083 publications from six major academic databases and selecting 77 empirical studies through structured searching, multi-stage filtering, and thematic coding. Contribution/Results: Our analysis constructs the first comprehensive research landscape of quantum optimization in SE, revealing strong concentration in test case generation and deployment optimization, while critical areas—including requirements engineering, maintenance, and software evolution—remain severely underexplored. Furthermore, many influential contributions appear outside mainstream SE venues, highlighting pronounced interdisciplinary fragmentation. The study identifies key research gaps and proposes concrete collaborative pathways, thereby establishing both a theoretical foundation and practical guidance for advancing SE–quantum computing convergence.

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📝 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.
Problem

Research questions and friction points this paper is trying to address.

Applying quantum algorithms to solve software engineering optimization problems
Surveying literature on quantum-inspired methods for SE challenges
Identifying research gaps in quantum optimization for SE activities
Innovation

Methods, ideas, or system contributions that make the work stand out.

Quantum optimization for SE challenges
Systematic review of quantum-inspired algorithms
Analysis of 77 studies from 2083 publications
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