The Smelly Eight: An Empirical Study on the Prevalence of Code Smells in Quantum Computing
Quantum Computing (QC) is a fast-growing field that has enhanced the emergence of new programming languages and frameworks. Furthermore, the increased availability of computational resources has also contributed to an influx in the development of quantum programs. Given that classical and QC are significantly different due to the intrinsic nature of quantum programs, several aspects of QC (e.g., performance, bugs) have been investigated, and novel approaches have been proposed. However, from a purely quantum perspective, maintenance, one of the major steps in a software development life-cycle, has not been considered by researchers yet. In this paper, we fill this gap and investigate the prevalence of code smells in quantum programs as an indicator of maintenance issues. We defined eight quantum-specific smells and validated them through a survey with 35 quantum developers. Since no tool specifically aims to detect quantum smells, we developed a tool called QSmell that supports the proposed quantum-specific smells. Finally, we conducted an empirical investigation to analyze the prevalence of quantum-specific smells in 15 open-source quantum programs. Our results showed that 11 programs (73.33%) contain at least one smell and, on average, a program has three smells. Furthermore, the long circuit is the most prevalent smell present in 53.33% of the programs.
Wed 17 MayDisplayed time zone: Hobart change
13:45 - 15:15 | Code smells and clonesTechnical Track / Journal-First Papers / SEIP - Software Engineering in Practice at Level G - Plenary Room 1 Chair(s): Sigrid Eldh Ericsson AB, Mälardalen University, Carleton Unviersity | ||
13:45 15mTalk | Comparison and Evaluation of Clone Detection Techniques with Different Code Representations Technical Track Yuekun Wang University of Science and Technology of China, Yuhang Ye University of Science and Technology of China, Yueming Wu Nanyang Technological University, Weiwei Zhang University of Science and Technology of China, Yinxing Xue University of Science and Technology of China, Yang Liu Nanyang Technological University | ||
14:00 15mTalk | Learning Graph-based Code Representations for Source-level Functional Similarity Detection Technical Track Jiahao Liu National University of Singapore, Jun Zeng National University of Singapore, Xiang Wang University of Science and Technology of China, Zhenkai Liang National University of Singapore | ||
14:15 15mTalk | The Smelly Eight: An Empirical Study on the Prevalence of Code Smells in Quantum Computing Technical Track Qihong Chen University of California, Irvine, Rúben Câmara LASIGE and Department of Informatics are Faculdade Ciências Universidade de Lisboa,, José Campos University of Porto, Portugal, André Souto LaSiGE & FCUL, University of Lisbon, Iftekhar Ahmed University of California at Irvine Pre-print | ||
14:30 15mTalk | An Empirical Comparison on the Results of Different Clone Detection Setups for C-based Projects SEIP - Software Engineering in Practice Yan Zhou Huawei, Jinfu Chen Centre for Software Excellence, Huawei, Canada, Yong Shi Huawei Technologies, Boyuan Chen Centre for Software Excellence, Huawei Canada, Zhen Ming (Jack) Jiang York University | ||
14:45 7mTalk | Developers’ perception matters: machine learning to detect developer-sensitive smells Journal-First Papers Daniel Oliveira PUC-Rio, Wesley Assunção Johannes Kepler University Linz, Austria & Pontifical Catholic University of Rio de Janeiro, Brazil, Alessandro Garcia PUC-Rio, Baldoino Fonseca Federal University of Alagoas (UFAL), Márcio Ribeiro Federal University of Alagoas, Brazil | ||
14:52 7mTalk | Smells in system user interactive tests Journal-First Papers Renaud Rwemalika University of Luxembourg, Sarra Habchi Ubisoft, Mike Papadakis University of Luxembourg, Luxembourg, Yves Le Traon University of Luxembourg, Luxembourg, Marie-Claude Brasseur BGL BNP Paribas | ||
15:00 7mTalk | Bash in the Wild: Language Usage, Code Smells, and Bugs Journal-First Papers Yiwen Dong University of Waterloo, Zheyang Li University of Waterloo, Yongqiang Tian University of Waterloo, Chengnian Sun University of Waterloo, Michael W. Godfrey University of Waterloo, Canada, Mei Nagappan University of Waterloo | ||
15:07 7mTalk | 1-to-1 or 1-to-n? Investigating the effect of function inlining on binary similarity analysis Journal-First Papers Ang Jia Xi'an Jiaotong University, Ming Fan Xi'an Jiaotong University, Wuxia Jin Xi'an Jiaotong University, Xi Xu Xi'an Jiaotong University, Zhaohui Zhou Xi'an Jiaotong University, Qiyi Tang Tencent Security Keen Lab, Sen Nie Keen Security Lab, Tencent, Shi Wu Tencent Security Keen Lab, Ting Liu Xi'an Jiaotong University | ||