{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,9]],"date-time":"2025-06-09T20:40:06Z","timestamp":1749501606723,"version":"3.41.0"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T00:00:00Z","timestamp":1748304000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T00:00:00Z","timestamp":1748304000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Software Qual J"],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s11219-025-09724-5","type":"journal-article","created":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T03:06:47Z","timestamp":1748315207000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["QNet: exploring deep learning for quantum code smell detection"],"prefix":"10.1007","volume":"33","author":[{"given":"Ruchika","family":"Malhotra","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-7455-2854","authenticated-orcid":false,"given":"Bhawna","family":"Jain","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marouane","family":"Kessentini","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,5,27]]},"reference":[{"key":"9724_CR1","doi-asserted-by":"crossref","unstructured":"Ali, S., Arcaini, P., Wang, X., Yue, T. (2021). Assessing the effectiveness of input and output coverage criteria for testing quantum programs, In: 2021 14th IEEE Conference on Software Testing, Verification and Validation (ICST), IEEE, pp. 13\u201323.","DOI":"10.1109\/ICST49551.2021.00014"},{"key":"9724_CR2","doi-asserted-by":"crossref","unstructured":"Alkharabsheh, K., Alawadi, S., Kebande, V. R., Crespo, Y., Fern\u00e1ndez-Delgado, M., & Taboada, J. A. (2022). A comparison of machine learning algorithms on design smell detection using balanced and imbalanced dataset: A study of god class. Information and Software Technology,143, Article 106736.","DOI":"10.1016\/j.infsof.2021.106736"},{"key":"9724_CR3","doi-asserted-by":"publisher","unstructured":"Bafatakis, N., Boecker, N., Boon, W., Cabello\u00a0Salazar, M., Krinke, J., Oznacar, G., White, R. (2019). Python coding style compliance on stack overflow, In: 2019 IEEE\/ACM 16th International Conference on Mining Software Repositories (MSR), pp. 210\u2013214. https:\/\/doi.org\/10.1109\/MSR.2019.00042","DOI":"10.1109\/MSR.2019.00042"},{"key":"9724_CR4","doi-asserted-by":"crossref","unstructured":"Ball, H., Biercuk, M. J., Carvalho, A. R., Chen, J., Hush, M., De Castro, L. A., Li, L., Liebermann, P. J., Slatyer, H. J., Edmunds, C., et al. (2021). Software tools for quantum control: Improving quantum computer performance through noise and error suppression. Quantum Science and Technology,6(4), Article 044011.","DOI":"10.1088\/2058-9565\/abdca6"},{"key":"9724_CR5","doi-asserted-by":"crossref","unstructured":"Barbez, A., Khomh, F., Gu\u00e9h\u00e9neuc, Y.-G. (2019). Deep learning anti-patterns from code metrics history, In: 2019 IEEE International Conference on Software Maintenance and Evolution (ICSME), IEEE, pp. 114\u2013124.","DOI":"10.1109\/ICSME.2019.00021"},{"issue":"7497","key":"9724_CR6","doi-asserted-by":"publisher","first-page":"500","DOI":"10.1038\/nature13171","volume":"508","author":"R Barends","year":"2014","unstructured":"Barends, R., Kelly, J., Megrant, A., Veitia, A., Sank, D., Jeffrey, E., White, T. C., Mutus, J., Fowler, A. G., Campbell, B., et al. (2014). Superconducting quantum circuits at the surface code threshold for fault tolerance. Nature, 508(7497), 500\u2013503.","journal-title":"Nature"},{"key":"9724_CR7","doi-asserted-by":"crossref","unstructured":"Benedetti, M., Realpe-G\u00f3mez, J., Biswas, R., & Perdomo-Ortiz, A. (2016). Estimation of effective temperatures in quantum annealers for sampling applications: A case study with possible applications in deep learning. Physical Review A,94(2), 022308","DOI":"10.1103\/PhysRevA.94.022308"},{"key":"9724_CR8","unstructured":"Bennett, C.\u00a0H., Brassard, G. (2020). Quantum cryptography: Public key distribution and coin tossing, arXiv:2003.06557."},{"key":"9724_CR9","doi-asserted-by":"publisher","unstructured":"Bhave, A., Sinha, R. (2022). Deep multimodal architecture for detection of long parameter list and switch statements using distilbert, In: 2022 IEEE 22nd International Working Conference on Source Code Analysis and Manipulation (SCAM), pp. 116\u2013120. https:\/\/doi.org\/10.1109\/SCAM55253.2022.00018","DOI":"10.1109\/SCAM55253.2022.00018"},{"key":"9724_CR10","doi-asserted-by":"crossref","unstructured":"Chen, Q., C\u00e2mara, R., Campos, J., Souto, A., Ahmed, I. (2023). The smelly eight: An empirical study on the prevalence of code smells in quantum computing, In: 2023 IEEE\/ACM 45th International Conference on Software Engineering (ICSE), IEEE, pp. 358\u2013370.","DOI":"10.1109\/ICSE48619.2023.00041"},{"key":"9724_CR11","doi-asserted-by":"publisher","unstructured":"Chen, Z., Chen, L., Ma, W., Xu, B. (2016). Detecting code smells in python programs, In: 2016 International Conference on Software Analysis, Testing and Evolution (SATE), pp. 18\u201323. https:\/\/doi.org\/10.1109\/SATE.2016.10","DOI":"10.1109\/SATE.2016.10"},{"key":"9724_CR12","doi-asserted-by":"publisher","unstructured":"Das, A.\u00a0K., Yadav, S., Dhal, S. (2019) Detecting code smells using deep learning, in: TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON), pp. 2081\u20132086. https:\/\/doi.org\/10.1109\/TENCON.2019.8929628","DOI":"10.1109\/TENCON.2019.8929628"},{"key":"9724_CR13","doi-asserted-by":"publisher","unstructured":"Dewangan, S., Rao, R. S., Mishra, A., & Gupta, M. (2022). Code smell detection using ensemble machine learning algorithms. Applied Sciences,12(20), 10321. https:\/\/doi.org\/10.3390\/app122010321","DOI":"10.3390\/app122010321"},{"key":"9724_CR14","doi-asserted-by":"publisher","first-page":"162869","DOI":"10.1109\/ACCESS.2021.3133810","volume":"9","author":"S Dewangan","year":"2021","unstructured":"Dewangan, S., Rao, R. S., Mishra, A., & Gupta, M. (2021). A novel approach for code smell detection: An empirical study. IEEE Access, 9, 162869\u2013162883. https:\/\/doi.org\/10.1109\/ACCESS.2021.3133810","journal-title":"IEEE Access"},{"key":"9724_CR15","doi-asserted-by":"publisher","unstructured":"Di\u00a0Nucci, D., Palomba, F., Tamburri, D.\u00a0A., Serebrenik, A., De\u00a0Lucia, A. (2018). Detecting code smells using machine learning techniques: Are we there yet?, In: 2018 IEEE 25th International Conference on Software Analysis, Evolution and Reengineering (SANER), pp. 612\u2013621. https:\/\/doi.org\/10.1109\/SANER.2018.8330266","DOI":"10.1109\/SANER.2018.8330266"},{"issue":"6\/7","key":"9724_CR16","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1007\/BF02650179","volume":"21","author":"RP Feynman","year":"1982","unstructured":"Feynman, R. P. (1982). Simulating physics with computers. International Journal of Theoretical Physics, 21(6\/7), 467\u2013488.","journal-title":"International Journal of Theoretical Physics"},{"key":"9724_CR17","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1007\/3-540-45672-4_31","volume-title":"Extreme Programming and Agile Methods \u2013 XP\/Agile Universe 2002","author":"M Fowler","year":"2002","unstructured":"Fowler, M. (2002). Refactoring: Improving the design of existing code. In D. Wells & L. Williams (Eds.), Extreme Programming and Agile Methods \u2013 XP\/Agile Universe 2002 (pp. 256\u2013256). Berlin Heidelberg, Berlin, Heidelberg: Springer."},{"key":"9724_CR18","first-page":"6","volume-title":"11th European Conference","author":"M Fowler","year":"1997","unstructured":"Fowler, M., & Beck, K. (1997). Refactoring: Improving the design of existing code. 11th European Conference (p. 6). Jyv\u00e4skyl\u00e4: Finland."},{"key":"9724_CR19","unstructured":"Gesi, J., Liu, S., Li, J., Ahmed, I., Nagappan, N., Lo, D., de\u00a0Almeida, E.\u00a0S., Kochhar, P.\u00a0S., Bao, L. (2022). Code smells in machine learning systems. arXiv:2203.00803."},{"key":"9724_CR20","doi-asserted-by":"publisher","unstructured":"Guo, X., Shi, C., Jiang, H. (2019). Deep semantic-based feature envy identification, In: Proceedings of the 11th Asia-Pacific Symposium on Internetware, Internetware \u201919, Association for Computing Machinery, New York, NY, USA, p.\u00a06. https:\/\/doi.org\/10.1145\/3361242.3361257","DOI":"10.1145\/3361242.3361257"},{"key":"9724_CR21","doi-asserted-by":"publisher","unstructured":"Gupta, R., Kumar Singh, S. (2022). A novel metric based detection of temporary field code smell and its empirical analysis, Journal of King Saud University - Computer and Information Sciences34\u00a0(10, Part B) 9478\u20139500. https:\/\/doi.org\/10.1016\/j.jksuci.2021.11.005https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1319157821003050","DOI":"10.1016\/j.jksuci.2021.11.005"},{"key":"9724_CR22","doi-asserted-by":"publisher","unstructured":"Gupta, H., Kulkarni, T.\u00a0G., Kumar, L., Neti, L.\u00a0B.\u00a0M., Krishna, A. (2021). An empirical study on predictability of software code smell using deep learning models, In: Advanced Information Networking and Applications, Springer International Publishing, pp. 120\u2013132. https:\/\/doi.org\/10.1007\/978-3-030-75075-6_10.","DOI":"10.1007\/978-3-030-75075-6_10"},{"key":"9724_CR23","doi-asserted-by":"crossref","unstructured":"Huang, Y., Martonosi, M. (2019). Statistical assertions for validating patterns and finding bugs in quantum programs, In: Proceedings of the 46th International Symposium on Computer Architecture, pp. 541\u2013553.","DOI":"10.1145\/3307650.3322213"},{"key":"9724_CR24","first-page":"58","volume":"2017","author":"I. Ahmed, C. Brindescu, U. A. Mannan, C. Jensen, A. Sarma, An empirical examination of the relationship between code smells and merge conflicts, in","year":"2017","unstructured":"I. Ahmed, C. Brindescu, U. A. Mannan, C. Jensen, A. Sarma, An empirical examination of the relationship between code smells and merge conflicts, in. (2017). ACM\/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM). IEEE, 2017, 58\u201367.","journal-title":"IEEE"},{"key":"9724_CR25","doi-asserted-by":"crossref","unstructured":"Jebnoun, H., Ben\u00a0Braiek, H., Rahman, M.\u00a0M., Khomh, F. (2020). The scent of deep learning code: An empirical study, In: Proceedings of the 17th International Conference on Mining Software Repositories, pp. 420\u2013430.","DOI":"10.1145\/3379597.3387479"},{"key":"9724_CR26","doi-asserted-by":"publisher","unstructured":"Lewowski, T., Madeyski, L. (2022). Code Smells Detection Using Artificial Intelligence Techniques: A Business-Driven Systematic Review, Springer International Publishing, Cham, 2022, Ch. SSDC, 377, pp. 285\u2013319. https:\/\/doi.org\/10.1007\/978-3-030-77916-0_12","DOI":"10.1007\/978-3-030-77916-0_12"},{"key":"9724_CR27","doi-asserted-by":"crossref","unstructured":"Leymann, F., Barzen, J., Falkenthal, M., Vietz, D., Weder, B., Wild, K. (2020). Quantum in the cloud: application potentials and research opportunities, arXiv:2003.06256.","DOI":"10.5220\/0009819800090024"},{"key":"9724_CR28","doi-asserted-by":"crossref","unstructured":"Li, G., Zhou, L., Yu, N., Ding, Y., Ying, M., Xie, Y. (2020). Projection-based runtime assertions for testing and debugging quantum programs, Proceedings of the ACM on Programming Languages4\u00a0(OOPSLA) 1\u201329.","DOI":"10.1145\/3428218"},{"key":"9724_CR29","doi-asserted-by":"publisher","unstructured":"Liu, H., Xu, Z., Zou, Y. (2018). Deep learning based feature envy detection, In: 2018 33rd IEEE\/ACM International Conference on Automated Software Engineering (ASE), pp. 385\u2013396. https:\/\/doi.org\/10.1145\/3238147.3238166","DOI":"10.1145\/3238147.3238166"},{"key":"9724_CR30","doi-asserted-by":"publisher","unstructured":"Ma, W., Yu, Y., Ruan, X., Cai, B. (2023). Pre-trained model based feature envy detection, In: 2023 IEEE\/ACM 20th International Conference on Mining Software Repositories (MSR), pp. 430\u2013440. https:\/\/doi.org\/10.1109\/MSR59073.2023.00065","DOI":"10.1109\/MSR59073.2023.00065"},{"key":"9724_CR31","unstructured":"Muralidhar, N., Muthiah, S., Butler, P., Jain, M., Yu, Y., Burne, K., Li, W., Jones, D., Arunachalam, P., McCormick, H.\u00a0S., Ramakrishnan, N. (2021). Using antipatterns to avoid mlops mistakes. arXiv:2107.00079."},{"key":"9724_CR32","unstructured":"Nayak, P.\u00a0K., Kher, K.\u00a0V., Chandra, M.\u00a0B., Rao, M.\u00a0V.\u00a0P., Zhang, L. (2023). Q-pac: Automated detection of quantum bug-fix patterns. arXiv:2311.17705 ."},{"key":"9724_CR33","doi-asserted-by":"crossref","unstructured":"Omari, S., Martinez, G. (2020). Enabling empirical research: A corpus of large-scale python systems, In: K.\u00a0Arai, R.\u00a0Bhatia, S.\u00a0Kapoor (Eds.), Proceedings of the Future Technologies Conference (FTC) 2019, Springer International Publishing, Cham, pp. 661\u2013669.","DOI":"10.1007\/978-3-030-32523-7_49"},{"key":"9724_CR34","doi-asserted-by":"crossref","unstructured":"Openja, M., Morovati, M. M., An, L., Khomh, F., & Abidi, M. (2022). Technical debts and faults in open-source quantum software systems: An empirical study. Journal of Systems and Software,193, Article 111458.","DOI":"10.1016\/j.jss.2022.111458"},{"key":"9724_CR35","doi-asserted-by":"crossref","unstructured":"Pecorelli, F., Palomba, F., Di\u00a0Nucci, D., De\u00a0Lucia, A. (2019). Comparing heuristic and machine learning approaches for metric-based code smell detection, In: 2019 IEEE\/ACM 27th International Conference on Program Comprehension (ICPC), IEEE, pp. 93\u2013104.","DOI":"10.1109\/ICPC.2019.00023"},{"key":"9724_CR36","doi-asserted-by":"publisher","unstructured":"Saheb-Nassagh, R., Ashtiani, M., Minaei-Bidgoli, B. (2022). A probabilistic-based approach for automatic identification and refactoring of software code smells, Applied Soft Computing130 109658. https:\/\/doi.org\/10.1016\/j.asoc.2022.109658https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1568494622007074","DOI":"10.1016\/j.asoc.2022.109658"},{"key":"9724_CR37","doi-asserted-by":"crossref","unstructured":"Sandouka, R., & Aljamaan, H. (2023). Python code smells detection using conventional machine learning models. PeerJ Computer Science,9, Article e1370.","DOI":"10.7717\/peerj-cs.1370"},{"key":"9724_CR38","doi-asserted-by":"publisher","unstructured":"Sharma, T., Efstathiou, V., Louridas, P., Spinellis, D. (2021) Code smell detection by deep direct-learning and transfer-learning, Journal of Systems and Software176 110936. https:\/\/doi.org\/10.1016\/j.jss.2021.110936https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0164121221000339","DOI":"10.1016\/j.jss.2021.110936"},{"key":"9724_CR39","doi-asserted-by":"crossref","unstructured":"Shaydulin, R., Thomas, C., Rodeghero, P. (2020). Making quantum computing open: Lessons from open source projects, In: Proceedings of the IEEE\/ACM 42nd International Conference on Software Engineering Workshops, pp. 451\u2013455.","DOI":"10.1145\/3387940.3391471"},{"issue":"5","key":"9724_CR40","doi-asserted-by":"publisher","first-page":"1484","DOI":"10.1137\/s0097539795293172","volume":"26","author":"PW Shor","year":"1997","unstructured":"Shor, P. W. (1997). Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer. SIAM Journal on Computing, 26(5), 1484\u20131509. https:\/\/doi.org\/10.1137\/s0097539795293172","journal-title":"SIAM Journal on Computing"},{"key":"9724_CR41","doi-asserted-by":"publisher","unstructured":"Tarwani, S., Chug, A. (2022). Application of deep learning models for code smell prediction, In: 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), pp. 1\u20135. https:\/\/doi.org\/10.1109\/ICRITO56286.2022.9965048","DOI":"10.1109\/ICRITO56286.2022.9965048"},{"issue":"11","key":"9724_CR42","doi-asserted-by":"publisher","first-page":"1063","DOI":"10.1109\/TSE.2017.2653105","volume":"43","author":"M Tufano","year":"2017","unstructured":"Tufano, M., Palomba, F., Bavota, G., Oliveto, R., Di Penta, M., De Lucia, A., & Poshyvanyk, D. (2017). When and why your code starts to smell bad (and whether the smells go away). IEEE Transactions on Software Engineering, 43(11), 1063\u20131088.","journal-title":"IEEE Transactions on Software Engineering"},{"key":"9724_CR43","doi-asserted-by":"crossref","unstructured":"Van Oort, B., Cruz, L., Aniche, M., Van Deursen, A. (2021). The prevalence of code smells in machine learning projects, In,. IEEE\/ACM 1st Workshop on AI Engineering-Software Engineering for AI (WAIN). IEEE, 2021, 1\u20138.","DOI":"10.1109\/WAIN52551.2021.00011"},{"key":"9724_CR44","doi-asserted-by":"crossref","unstructured":"Yamashita, A., Moonen, L. (2013). Exploring the impact of inter-smell relations on software maintainability: An empirical study, In: 2013 35th International Conference on Software Engineering (ICSE), IEEE, pp. 682\u2013691.","DOI":"10.1109\/ICSE.2013.6606614"},{"key":"9724_CR45","doi-asserted-by":"publisher","unstructured":"Zhang, Y., Ge, C., Hong, S., Tian, R., Dong, C., Liu, J. (2022) Delesmell: Code smell detection based on deep learning and latent semantic analysis, Knowledge-Based Systems255 109737. https:\/\/doi.org\/10.1016\/j.knosys.2022.109737https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0950705122008796","DOI":"10.1016\/j.knosys.2022.109737"},{"key":"9724_CR46","unstructured":"Zhao, J. (2020). Quantum software engineering: Landscapes and horizons, arXiv:2007.07047."},{"key":"9724_CR47","doi-asserted-by":"crossref","unstructured":"Zhao, P., Wu, X., Li, Z., Zhao, J. (2023). Qchecker: Detecting bugs in quantum programs via static analysis. arXiv:2304.04387 .","DOI":"10.1109\/Q-SE59154.2023.00014"},{"key":"9724_CR48","doi-asserted-by":"crossref","unstructured":"Zhao, P., Zhao, J., Ma, L. (2021). Identifying bug patterns in quantum programs, In,. IEEE\/ACM 2nd International Workshop on Quantum Software Engineering (Q-SE). IEEE, 2021, 16\u201321.","DOI":"10.1109\/Q-SE52541.2021.00011"}],"container-title":["Software Quality Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11219-025-09724-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11219-025-09724-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11219-025-09724-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,9]],"date-time":"2025-06-09T20:02:39Z","timestamp":1749499359000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11219-025-09724-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,27]]},"references-count":48,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["9724"],"URL":"https:\/\/doi.org\/10.1007\/s11219-025-09724-5","relation":{},"ISSN":["0963-9314","1573-1367"],"issn-type":[{"type":"print","value":"0963-9314"},{"type":"electronic","value":"1573-1367"}],"subject":[],"published":{"date-parts":[[2025,5,27]]},"assertion":[{"value":"19 May 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 May 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}],"article-number":"25"}}