A Quantum Annealing Approach for Solving Optimal Feature Selection and Next Release Problems

TL;DR

This paper introduces quantum annealing algorithms for solving large-scale feature selection and next release problems in software engineering, demonstrating improved computational efficiency over traditional methods.

Contribution

The paper presents novel QA-based algorithms tailored for different problem scales in SBSE, integrating quantum computing with classical optimization techniques.

Findings

QA methods reduce execution time significantly

QA approaches find more non-dominated solutions than NSGA-II

QA algorithms outperform ILP in scalability and efficiency

Abstract

Search-based software engineering (SBSE) addresses critical optimization challenges in software engineering, including the next release problem (NRP) and feature selection problem (FSP). While traditional heuristic approaches and integer linear programming (ILP) methods have demonstrated efficacy for small to medium-scale problems, their scalability to large-scale instances remains unknown. Here, we introduce quantum annealing (QA) as a subroutine to tackling multi-objective SBSE problems, leveraging the computational potential of quantum systems. We propose two QA-based algorithms tailored to different problem scales. For small-scale problems, we reformulate multi-objective optimization (MOO) as single-objective optimization (SOO) using penalty-based mappings for quantum processing. For large-scale problems, we employ a decomposition strategy guided by maximum energy impact (MEI), integrating QA with a steepest descent method to enhance local search efficiency. Applied to NRP and FSP, our approaches are benchmarked against the heuristic NSGA-II and the ILP-based $\epsilon$-constraint method. Experimental results reveal that while our methods produce fewer non-dominated solutions than $\epsilon$-constraint, they achieve significant reductions in execution time. Moreover, compared to NSGA-II, our methods deliver more non-dominated solutions with superior computational efficiency. These findings underscore the potential of QA in advancing scalable and efficient solutions for SBSE challenges.