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arXiv 2401.08151cs.SE
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敏捷遇上量子:一种用于预测量子软件开发项目成功率的新型遗传算法模型

Agile Meets Quantum: A Novel Genetic Algorithm Model for Predicting the Success of Quantum Software Development Project

Arif Ali Khan, Muhammad Azeem Akbar, Valtteri Lahtinen, Marko Paavola, Mahmood Niazi, Mohammed Naif Alatawi, Shoayee Dlaim Alotaibi

AI总结:

本研究针对传统敏捷方法适配量子软件项目的挑战,结合遗传算法与两类分类器构建成功率预测模型AQSSPM,实验验证其可大幅提升项目成功率、降低成本,为敏捷量子项目落地提供关键参考。

摘要:

研究背景:量子软件系统是软件工程领域的新方向,它利用量子比特(Qubits)和量子门(Qgates)来比经典软件更高效地解决复杂问题。敏捷软件开发方法被认为可应对量子软件开发中的诸多固有挑战,但二者的有效融合仍有待探索。研究目标:本研究探究了阻碍传统敏捷方法在量子软件项目中落地的关键挑战成因,并构建了敏捷量子软件项目成功率预测模型(Agile Quantum Software Project Success Prediction Model, AQSSPM)。研究方法:首先,我们从前期研究中梳理出19项可能影响敏捷量子项目成功率的挑战成因;其次,通过调研收集专家对这些成因的意见,结合朴素贝叶斯分类器(Naive Bayes Classifier, NBC)和逻辑回归(Logistic Regression, LR)应用遗传算法(Genetic Algorithm, GA)构建AQSSPM。研究结果:结合NBC的GA将项目成功率从53.17%提升至99.68%,成本占比从0.463%降至0.403%;与之类似,结合LR的GA在100次迭代后将成功率从55.52%提升至98.99%,成本占比从0.496%降至0.409%。两种方法的成因排序呈强正相关(rs=0.955),且二者无显著差异(t=1.195, p=0.240>0.05)。研究结论:AQSSPM明确了在考量特定项目成本因素时,高效成功落地敏捷量子项目需重点关注的核心领域。

英文摘要:

Context: Quantum software systems represent a new realm in software engineering, utilizing quantum bits (Qubits) and quantum gates (Qgates) to solve the complex problems more efficiently than classical counterparts . Agile software development approaches are considered to address many inherent challenges in quantum software development, but their effective integration remains unexplored Objective: This study investigates key causes of challenges that could hinders the adoption of traditional agile approaches in quantum software projects and develop an Agile Quantum Software Project Success Prediction Model (AQSSPM). Methodology: Firstly, w e identified 19 causes of challenging factors discussed in our previous study, which are potentially impacting agile quantum project success. Secondly, a survey was conducted to collect expert opinions on these causes and applied Genetic Algorithm (GA) with Na i ve Bayes Classifier (NBC) and Logistic Regression (LR) to develop the AQSSPM Results: Utilizing GA with NBC, project success probability improved from 53.17% to 99.68%, with cost reductions from 0.463% to 0.403%. Similarly, GA with LR increased success rates from 55.52% to 98.99%, and costs decreased from 0.496% to 0.409% after 100 iterati ons. Both methods result showed a strong positive correlation (rs=0.955) in causes ranking, with no significant difference between them (t=1.195, p=0.240>0.05). Conclusion: The AQSSPM highlights critical focus areas for efficiently and successfully implementing agile quantum projects considering the cost factor of a particular project

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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