In recent years, quantum, quantum-inspired, and hybrid algorithms are increasingly showing promise for solving software engineering optimization problems. However, best-intended practices for conducting empirical studies have not yet well established. In this paper, based on the primary studies identified from the latest systematic literature review on quantum optimization for software engineering problems, we conducted a systematic analysis on these studies from various aspects including experimental designs, hyperparameter settings, case studies, baselines, tooling, and metrics. We identify key gaps in the current practices such as limited reporting of the number of repetitions, number of shots, and inadequate consideration of noise handling, as well as a lack of standardized evaluation protocols such as the adoption of quality metrics, especially quantum-specific metrics. Based on our analysis, we provide insights for designing empirical studies and highlight the need for more real-world and open case studies to assess cost-effectiveness and practical utility of the three types of approaches: quantum-inspired, quantum, and hybrid. This study is intended to offer an overview of current practices and serve as an initial reference for designing and conducting empirical studies on evaluating and comparing quantum, quantum-inspired, and hybrid algorithms in solving optimization problems in software engineering.

翻译：近年来，量子算法、量子启发式算法及混合算法在解决软件工程优化问题方面展现出日益增长的应用潜力。然而，开展实证研究的最佳实践尚未得到充分确立。本文基于针对软件工程问题量子优化领域最新系统性文献综述所识别出的主要研究，从实验设计、超参数设置、案例研究、基准方法、工具支持及评估指标等多个维度对这些研究进行了系统性分析。我们指出了当前实践中的关键不足，例如重复实验次数与量子测量次数的报告有限、噪声处理考虑不充分，以及缺乏标准化评估协议（如质量指标的应用，尤其是量子专用指标）。基于分析结果，我们为实证研究的设计提供了见解，并强调需要更多真实场景与开放案例研究，以评估量子启发式、量子及混合三类方法的成本效益与实际效用。本研究旨在概述当前实践，并为设计及开展评估与比较量子、量子启发式及混合算法在解决软件工程优化问题中的实证研究提供初步参考。