{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T15:04:03Z","timestamp":1786115043584,"version":"build-2736575974"},"reference-count":28,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Science of Computer Programming"],"published-print":{"date-parts":[[2027,1]]},"DOI":"10.1016\/j.scico.2026.103538","type":"journal-article","created":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T15:33:07Z","timestamp":1783524787000},"page":"103538","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Fault modeling analysis of SaaS application software modules based on big data tendency prediction"],"prefix":"10.1016","volume":"255","author":[{"given":"Li","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.scico.2026.103538_bib0001","first-page":"1","article-title":"Optimal versioning strategies for software firms in the competitive environment","volume":"59","author":"Sun","year":"2020","journal-title":"Int. J. Prod. Res."},{"issue":"2","key":"10.1016\/j.scico.2026.103538_bib0002","doi-asserted-by":"crossref","first-page":"2049","DOI":"10.1109\/TVT.2019.2957938","article-title":"Performance analysis of an edge computing SAAS system for mobile users","volume":"69","author":"Bonadio","year":"2020","journal-title":"IEEE Trans. Veh. Technol."},{"issue":"4\/6","key":"10.1016\/j.scico.2026.103538_bib0003","doi-asserted-by":"crossref","first-page":"447","DOI":"10.13052\/jwe1540-9589.18467","article-title":"Applying feature-oriented software development in SAAS systems: real experience, measurements, and findings","volume":"18","author":"Pedreira","year":"2019","journal-title":"J. Web Eng."},{"issue":"3","key":"10.1016\/j.scico.2026.103538_bib0004","first-page":"1","article-title":"Analysing the impact of scaling out SAAS software on response time","volume":"2021","author":"Dong","year":"2021","journal-title":"Sci. Program."},{"issue":"1","key":"10.1016\/j.scico.2026.103538_bib0005","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1016\/j.future.2020.09.025","article-title":"Thread-level resource consumption control of tenant custom code in a shared JVM for multi-tenant SAAS","volume":"115","author":"Makki","year":"2021","journal-title":"Future Gener. Comput. Syst."},{"issue":"3","key":"10.1016\/j.scico.2026.103538_bib0006","first-page":"1","article-title":"Analysing the impact of scaling out SAAS software on response time","volume":"2021","author":"Dong","year":"2021","journal-title":"Sci. Program."},{"issue":"99","key":"10.1016\/j.scico.2026.103538_bib0007","article-title":"A systematic mapping study on the customization solutions of software as a service applications","volume":"PP","author":"Ali","year":"2019","journal-title":"IEEE Access"},{"issue":"5","key":"10.1016\/j.scico.2026.103538_bib0008","article-title":"Multi-source fault identification based on combined deep learning","volume":"309","author":"Xing","year":"2020","journal-title":"MATEC Web Conf."},{"issue":"99","key":"10.1016\/j.scico.2026.103538_bib0009","first-page":"1","article-title":"Fault identification for a class of nonlinear systems of canonical form via deterministic learning","volume":"PP","author":"Chen","year":"2021","journal-title":"IEEE Trans. Cybern."},{"issue":"5","key":"10.1016\/j.scico.2026.103538_bib0010","doi-asserted-by":"crossref","first-page":"724","DOI":"10.1049\/iet-gtd.2018.6334","article-title":"Fault-cause identification method based on adaptive deep belief network and time-frequency characteristics of traveling wave","volume":"13","author":"Liang","year":"2019","journal-title":"IET Gener. Transm. Distrib."},{"issue":"5","key":"10.1016\/j.scico.2026.103538_bib0011","doi-asserted-by":"crossref","first-page":"7571","DOI":"10.1007\/s13369-023-08486-1","article-title":"Enhancing software reliability forecasting through a hybrid ARIMA-ANN model","volume":"49","author":"Samal","year":"2024","journal-title":"Arab. J. Sci. Eng."},{"key":"10.1016\/j.scico.2026.103538_bib0012","first-page":"1","article-title":"Empowering software reliability: leveraging efficient fault detection and removal efficiency","author":"Samal","year":"2024","journal-title":"Qual. Eng."},{"issue":"5","key":"10.1016\/j.scico.2026.103538_bib0013","doi-asserted-by":"crossref","first-page":"1757","DOI":"10.1007\/s13198-025-02743-2","article-title":"Improving software reliability: a hybrid ARIMA-LSTM approach for fault prediction","volume":"16","author":"Samal","year":"2025","journal-title":"Int. J. Syst. Assur. Eng. Manag."},{"issue":"5","key":"10.1016\/j.scico.2026.103538_bib0014","doi-asserted-by":"crossref","first-page":"2005","DOI":"10.1002\/qre.3759","article-title":"Hybrid approach to software fault prediction using particle swarm optimization and fuzzy time series","volume":"41","author":"Samal","year":"2025","journal-title":"Qual. Reliab. Eng. Int."},{"issue":"3","key":"10.1016\/j.scico.2026.103538_bib0015","doi-asserted-by":"crossref","DOI":"10.1142\/S0218539324500098","article-title":"A neural network approach for software reliability prediction","volume":"31","author":"Samal","year":"2024","journal-title":"Int. J. Reliab. Qual. Saf. Eng."},{"key":"10.1016\/j.scico.2026.103538_bib0016","unstructured":"Yang, C.X., Yan, J.H. & Guo, C.,J. (2019). Fault detection of fault-tolerant integrated navigation based on one-class SVM computer simulation, 36(5), 78\u201382. http:\/\/en.cnki.com.cn\/Article_en\/CJFDTotal-JSJZ201905016.htm."},{"issue":"Dec","key":"10.1016\/j.scico.2026.103538_bib0017","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1016\/j.future.2019.06.022","article-title":"A comparative analysis of machine learning models for quality pillar assessment of SAAS services by multi-class text classification of users' reviews","volume":"101","author":"Raza","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"issue":"4","key":"10.1016\/j.scico.2026.103538_bib0018","doi-asserted-by":"crossref","first-page":"6556","DOI":"10.1109\/JIOT.2019.2908019","article-title":"Machine learning-based link fault identification and localization in complex networks (IEEE internet of things journal)","volume":"6","author":"Srinivasan","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.scico.2026.103538_bib0019","first-page":"4","article-title":"Comparison of statistical and machine learning models for pipe failure modeling in water distribution networks","volume":"12","author":"Giraldo","year":"2020","journal-title":"Water"},{"key":"10.1016\/j.scico.2026.103538_bib0020","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.isatra.2020.01.014","article-title":"Intelligent fault identification for industrial automation system via multi-scale convolutional generative adversarial network with partially labeled samples","volume":"101","author":"Pan","year":"2020","journal-title":"ISA Trans."},{"issue":"12","key":"10.1016\/j.scico.2026.103538_bib0021","doi-asserted-by":"crossref","first-page":"2734","DOI":"10.3390\/s19122734","article-title":"Fault identification ability of a robust deeply integrated GNSS\/ins system assisted by convolutional neural networks","volume":"19","author":"Zou","year":"2019","journal-title":"Sensors"},{"issue":"9","key":"10.1016\/j.scico.2026.103538_bib0022","doi-asserted-by":"crossref","first-page":"2131","DOI":"10.3390\/s19092131","article-title":"Fault identification for a closed-loop control system based on an improved deep neural network","volume":"19","author":"Sun","year":"2019","journal-title":"Sensors"},{"issue":"99","key":"10.1016\/j.scico.2026.103538_bib0023","article-title":"Fault diagnosis of battery systems for electric vehicles based on voltage abnormality combining the long short-term memory neural network and the equivalent circuit model","volume":"PP","author":"Li","year":"2020","journal-title":"IEEE Trans. Power Electron."},{"issue":"6","key":"10.1016\/j.scico.2026.103538_bib0024","article-title":"New methods based on back propagation (bp) and radial basis function (RBF) artificial neural networks (ANNS) for predicting the occurrence of haloketones in tap water","volume":"772","author":"Deng","year":"2021","journal-title":"Sci. Total Environ."},{"issue":"2","key":"10.1016\/j.scico.2026.103538_bib0025","doi-asserted-by":"crossref","first-page":"1293","DOI":"10.1109\/TIE.2019.2898619","article-title":"Intelligent fault identification based on multisource domain generalization towards actual diagnosis scenario","volume":"67","author":"Zheng","year":"2020","journal-title":"IEEE Trans. Ind. Electron."},{"issue":"99","key":"10.1016\/j.scico.2026.103538_bib0026","first-page":"1","article-title":"Fault identification for a class of nonlinear systems of canonical form via deterministic learning","author":"Chen","year":"2021","journal-title":"IEEE Trans. Cybern."},{"issue":"5","key":"10.1016\/j.scico.2026.103538_bib0027","doi-asserted-by":"crossref","first-page":"7571","DOI":"10.1007\/s13369-023-08486-1","article-title":"Enhancing software reliability forecasting through a hybrid Arima-ANN model","volume":"49","author":"Samal","year":"2024","journal-title":"Arab. J. Sci. Eng."},{"key":"10.1016\/j.scico.2026.103538_bib0028","doi-asserted-by":"crossref","first-page":"2656","DOI":"10.3390\/s24082656","article-title":"Recent advances in intelligent algorithms for fault detection and diagnosis","volume":"24","author":"Mercorelli","year":"2024","journal-title":"Sensors"}],"container-title":["Science of Computer Programming"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167642326001048?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167642326001048?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T23:44:29Z","timestamp":1785973469000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0167642326001048"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2027,1]]},"references-count":28,"alternative-id":["S0167642326001048"],"URL":"https:\/\/doi.org\/10.1016\/j.scico.2026.103538","relation":{},"ISSN":["0167-6423"],"issn-type":[{"value":"0167-6423","type":"print"}],"subject":[],"published":{"date-parts":[[2027,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Fault modeling analysis of SaaS application software modules based on big data tendency prediction","name":"articletitle","label":"Article Title"},{"value":"Science of Computer Programming","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.scico.2026.103538","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"103538"}}