University of Oulu

Akbar, M.A., Khan, A.A. & Rafi, S. A systematic decision-making framework for tackling quantum software engineering challenges. Autom Softw Eng 30, 22 (2023).

A systematic decision‑making framework for tackling quantum software engineering challenges

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Author: Akbar, Muhammad Azeem1; Khan, Arif Ali2; Rafi, Saima3
Organizations: 1Software Engineering Department, Lappeenranta-Lahti University of Technology, 53851, Lappeenranta, Finland
2M3S Empirical Software Engineering Research Unit, University of Oulu, 90014, Oulu, Finland
3School of Computing and Engineering and The Built Environment, Edinburgh Napier University, Edinburgh, UK
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 2.8 MB)
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Language: English
Published: Springer Nature, 2023
Publish Date: 2023-09-06


Quantum computing systems harness the power of quantum mechanics to execute computationally demanding tasks more effectively than their classical counterparts. This has led to the emergence of Quantum Software Engineering (QSE), which focuses on unlocking the full potential of quantum computing systems. As QSE gains prominence, it seeks to address the evolving challenges of quantum software development by offering comprehensive concepts, principles, and guidelines. This paper aims to identify, prioritize, and develop a systematic decision-making framework of the challenging factors associated with QSE process execution. We conducted a literature survey to identify the challenging factors associated with QSE process and mapped them into 7 core categories. Additionally, we used a questionnaire survey to collect insights from practitioners regarding these challenges. To examine the relationships between core categories of challenging factors, we applied Interpretive Structure Modeling (ISM). Lastly, we applied fuzzy TOPSIS to rank the identified challenging factors concerning to their criticality for QSE process. We have identified 22 challenging factors of QSE process and mapped them to 7 core categories. The ISM results indicate that the ‘resources’ category has the most decisive influence on the other six core categories of the identified challenging factors. Moreover, the fuzzy TOPSIS indicates that ‘complex programming’, ‘limited software libraries’, ‘maintenance complexity’, ‘lack of training and workshops’, and ‘data encoding issues’ are the highest priority challenging factor for QSE process execution. Organizations using QSE could consider the identified challenging factors and their prioritization to improve their QSE process.

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Series: Automated software engineering
ISSN: 0928-8910
ISSN-E: 1573-7535
ISSN-L: 0928-8910
Volume: 30
Issue: 2
Article number: 22
DOI: 10.1007/s10515-023-00389-7
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
213 Electronic, automation and communications engineering, electronics
Funding: Open Access funding provided by University of Oulu including Oulu University Hospital.
Dataset Reference: The codes and data are available under request from the authors.
Copyright information: © The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit