Open Access

ARTICLE

QIMIG: A Quantum-Inspired Evolutionary Framework for Software Library Migration

Yun Liu¹, Jinghua Zhao¹, Liang Ma¹, Zijie Huang^2,3,*, Lizhi Cai^2,3, Jianxin Ge^2,3

1 School of Management, University of Shanghai for Science and Technology, Shanghai, China
2 Shanghai Key Laboratory of Computer Software Testing & Evaluating, Shanghai, China
3 Shanghai Development Center of Computer Software Technology, Shanghai, China

* Corresponding Author: Zijie Huang. Email:

Computers, Materials & Continua 2026, 88(3), 32 https://doi.org/10.32604/cmc.2026.084179

Received 17 April 2026; Accepted 21 May 2026; Issue published 23 July 2026

Abstract

Automated library migration reduces refactoring costs but challenges traditional evolutionary algorithms, which often suffer from premature convergence and poor recall in sparse, complex API mapping spaces. To address this, we propose QIMIG, a multi-objective optimization framework integrating quantum-inspired encoding with quality-aware and greedy heuristic filtering. QIMIG utilizes a probabilistic Q-bit representation to maintain population diversity and avoid local optima. Simultaneously, its heuristic components leverage historical usage context to filter semantic noise and guide the search toward valid mappings. Evaluated on 9 real-world migration rules derived from 57,447 open-source projects, QIMIG statistically significantly outperforms state-of-the-art baselines such as UNSGA-III. The framework achieves a global mean F1-score of 0.92, exceeding the best-performing baseline by an absolute margin of 0.05, and demonstrates strong stability in resolving complex mapping structures.

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Keywords

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