{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:52:02Z","timestamp":1753887122426,"version":"3.41.2"},"reference-count":35,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,10,23]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Hardware testing has always been the core of hardware development, and improving the performance and efficiency of hardware testing is very important for hardware development. Because hardware quality management is insufficient, many large hardware tools were developed using manual workshop technology in the past and could hardly be maintained. This can lead to the cancellation of the project, causing major personnel and property losses. Improving hardware quality and ensuring security are very complex problems. Hardware testing is usually conducted through manual and automatic testing, and the limitations of manual testing are increasingly obvious. So hardware automatic testing technology has attracted people\u2019s attention in recent years. It has become an important research direction in the field hardware testing and can overcome many problems of traditional testing methods. Strict test rules, based on standards and scores, provide a fully automated test process. With the continuous improvement of network technology, the functions and scope of hardware are constantly enriched and expanded. With the acceleration of hardware updates and development, this has brought a heavy burden to the previous hardware testing work. The purpose of this article was to study the application of machine learning technology in the field of hardware automatic testing and provide an appropriate theoretical basis for optimizing testing methods. This article introduced the research methods of hardware automatic testing technology, introduced three automatic testing framework models, and summarized the application of machine learning in hardware testing. It included hardware security and reliability analysis, hardware defect prediction, and source-based research. Then, this article studied the defect prediction model and machine learning algorithm and constructed a hardware defect prediction model based on machine learning based on the theory. First, the data were preprocessed, and then, the Stacking method was used to build a comprehensive prediction model, and four prediction results evaluation indicators were established. In the experiment part, the defect prediction results of the hardware automatic test model were studied. The results showed that the hardware defect prediction model based on machine learning had higher accuracy, recall rate, <jats:italic>F<\/jats:italic>_measure and area under curve. Compared with other models, the average accuracy of the hardware defect prediction model in this article was 0.092 higher, which was more suitable for automatic hardware testing and analysis.<\/jats:p>","DOI":"10.1515\/comp-2024-0006","type":"journal-article","created":{"date-parts":[[2024,10,23]],"date-time":"2024-10-23T14:59:13Z","timestamp":1729695553000},"source":"Crossref","is-referenced-by-count":0,"title":["Hardware automatic test scheme and intelligent analyze application based on machine learning model"],"prefix":"10.1515","volume":"14","author":[{"given":"Ru","family":"Jing","sequence":"first","affiliation":[{"name":"Department of Information Engineering, Hainan Vocational University of Science and Technology , Haikou 571126, Hainan , China"}]},{"given":"Yajuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, Hainan Vocational University of Science and Technology , Haikou 571126, Hainan , China"}]},{"given":"Shulong","family":"Zhuo","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, Hainan Vocational University of Science and Technology , Haikou 571126, Hainan , China"}]}],"member":"374","published-online":{"date-parts":[[2024,10,23]]},"reference":[{"key":"2024102315465062017_j_comp-2024-0006_ref_001","unstructured":"P. Zhang, \u201cMeasurement of hallux valgus related indicators using Mimics hardware based on foot weight bearing CT imaging,\u201d Chin. J. Anat. Clin. Sci., vol. 23, no. 1, pp. 7\u201313, 2018."},{"key":"2024102315465062017_j_comp-2024-0006_ref_002","unstructured":"W. Chen, C. Yu, and L. Xiao, \u201cResearch on the application of cloud based automatic testing for relay protection,\u201d Electr. Technol., vol. 19, pp. 7\u201310, 2021."},{"key":"2024102315465062017_j_comp-2024-0006_ref_003","doi-asserted-by":"crossref","unstructured":"S. Motahhir, \u201cOpen hardware\/hardware test bench for solar tracker with virtual instrumentation,\u201d Sustain. Energy Technol. Assess, vol. 31, no. 2, pp. 9\u201316, 2019.","DOI":"10.1016\/j.seta.2018.11.003"},{"key":"2024102315465062017_j_comp-2024-0006_ref_004","doi-asserted-by":"crossref","unstructured":"J. M. Balera and V. A. de Santiago J\u00fanior, \u201cA systematic mapping addressing hyper-heuristics within search-based hardware testing,\u201d Inf. Hardw. Technol., vol. 114, no. 10, pp. 176\u2013189, 2019.","DOI":"10.1016\/j.infsof.2019.06.012"},{"key":"2024102315465062017_j_comp-2024-0006_ref_005","unstructured":"A. M. Alghamdi and E. E. Fathy, \u201cHardware testing techniques for parallel systems: A survey,\u201d Int. J. Comput. Sci. Netw. Secur., vol. 19, no. 4, pp. 176\u2013186, 2019."},{"key":"2024102315465062017_j_comp-2024-0006_ref_006","doi-asserted-by":"crossref","unstructured":"B. Marculescu, \u201cTransferring interactive search-based hardware testing to industry,\u201d J. Syst. Hardw., vol. 142, no. 8, pp. 156\u2013170, 2018.","DOI":"10.1016\/j.jss.2018.04.061"},{"key":"2024102315465062017_j_comp-2024-0006_ref_007","doi-asserted-by":"crossref","unstructured":"S. M. Melo, \u201cEmpirical research on concurrent hardware testing: A systematic mapping study,\u201d Inf. Hardw. Technol., vol. 105, no. 1, pp. 226\u2013251, 2019.","DOI":"10.1016\/j.infsof.2018.08.017"},{"key":"2024102315465062017_j_comp-2024-0006_ref_008","doi-asserted-by":"crossref","unstructured":"O. S. G\u00f3mez, R. H. Rosero, and K. Cort\u00e9s-Verd\u00edn, \u201cCRUDyLeaf: a DSL for generating spring boot REST APIs from entity CRUD operations,\u201d Cybern. Inf. Technol., vol. 20, no. 3, pp. 3\u201314, 2020.","DOI":"10.2478\/cait-2020-0024"},{"key":"2024102315465062017_j_comp-2024-0006_ref_009","doi-asserted-by":"crossref","unstructured":"G. Quir\u00f3s, D. Cao, and A. Canedo, \u201cDispersed automation for industrial internet of things,\u201d IEEE Trans. Autom. Sci. Eng., vol. 17, no. 3, pp. 1176\u20131181, 2020.","DOI":"10.1109\/TASE.2020.2978527"},{"key":"2024102315465062017_j_comp-2024-0006_ref_010","doi-asserted-by":"crossref","unstructured":"H. M. Tran, \u201cAn analyze of hardware bug reports using machine learning techniques,\u201d SN Comput. Sci, vol. 1, no. 1, pp. 1\u201311, 2020.","DOI":"10.1007\/s42979-019-0004-1"},{"key":"2024102315465062017_j_comp-2024-0006_ref_011","doi-asserted-by":"crossref","unstructured":"G. Esteves, \u201cUnderstanding machine learning hardware defect predictions,\u201d Autom. Hardw. Eng., vol. 27, no. 3, pp. 369\u2013392, 2020.","DOI":"10.1007\/s10515-020-00277-4"},{"key":"2024102315465062017_j_comp-2024-0006_ref_012","doi-asserted-by":"crossref","unstructured":"A. Tucker, \u201cGenerating high-fidelity synthetic patient data for assessing machine learning healthcare hardware,\u201d NPJ Digital Med., vol. 3, no. 1, pp. 1\u201313, 2020.","DOI":"10.1038\/s41746-020-00353-9"},{"key":"2024102315465062017_j_comp-2024-0006_ref_013","doi-asserted-by":"crossref","unstructured":"S. Gerke, \u201cThe need for a system view to regulate artificial intelligence\/machine learning-based hardware as medical device,\u201d NPJ Digital Med., vol. 3, no. 1, pp. 1\u20134, 2020.","DOI":"10.1038\/s41746-020-0262-2"},{"key":"2024102315465062017_j_comp-2024-0006_ref_014","doi-asserted-by":"crossref","unstructured":"S. Goyal and P. K. Bhatia, \u201cComparison of machine learning techniques for hardware quality prediction,\u201d Int. J. Knowl. Syst. Sci. (IJKSS), vol. 11, no. 2, pp. 20\u201340, 2020.","DOI":"10.4018\/IJKSS.2020040102"},{"key":"2024102315465062017_j_comp-2024-0006_ref_015","doi-asserted-by":"crossref","unstructured":"A. Jaiswal and R. Malhotra, \u201cHardware reliability prediction using machine learning techniques,\u201d Int. J. Syst. Assur. Eng. Manag., vol. 9, no. 1, pp. 230\u2013244, 2018.","DOI":"10.1007\/s13198-016-0543-y"},{"key":"2024102315465062017_j_comp-2024-0006_ref_016","doi-asserted-by":"crossref","unstructured":"A. K. Sandhu and R. S. Batth, \u201cHardware reuse analytics using integrated random forest and gradient boosting machine learning algorithm,\u201d Hardw.: Pract. Exp., vol. 51, no. 4, pp. 735\u2013747, 2021.","DOI":"10.1002\/spe.2921"},{"key":"2024102315465062017_j_comp-2024-0006_ref_017","doi-asserted-by":"crossref","unstructured":"A. Dwarakanath, D. Era, A. Priyadarshi, N. Dubash, and S. Podder, \u201cAccelerating test automation through a domain specific language,\u201d 2017 IEEE International Conference on Hardware Testing, Verification and Validation (ICST), Tokyo, Japan, 2017, pp. 460\u2013467, 10.1109\/ICST.2017.52.","DOI":"10.1109\/ICST.2017.52"},{"key":"2024102315465062017_j_comp-2024-0006_ref_018","unstructured":"X. Long, \u201cA script language for automatic testing of embedded hardware,\u201d Control. Inf. Technol., vol. 3, pp. 48\u201351, 2019."},{"key":"2024102315465062017_j_comp-2024-0006_ref_019","doi-asserted-by":"crossref","unstructured":"A. Khalilian, A. Baraani-Dastjerdi, and B. Zamani, \u201cAPRSuite: A suite of components and use cases based on categorical decomposition of automatic program repair techniques and tools,\u201d J. Comput. Lang., vol. 57, p. 100927, 2020, 10.1016\/j.cola.2019.100927.","DOI":"10.1016\/j.cola.2019.100927"},{"key":"2024102315465062017_j_comp-2024-0006_ref_020","doi-asserted-by":"crossref","unstructured":"A. S. Dimovski, \u201cA binary decision diagram lifted domain for analyzing program families,\u201d J. Comput. Lang., vol. 63, p. 101032, 2021, 10.1016\/j.cola.2021.101032.","DOI":"10.1016\/j.cola.2021.101032"},{"key":"2024102315465062017_j_comp-2024-0006_ref_021","doi-asserted-by":"crossref","unstructured":"Y. Tsutano, S. Bachala, W. Srisa-an, G. Rothermel, and J. Dinh, \u201cJitana. A modern hybrid program analyze framework for android platforms,\u201d J. Comput. Lang., vol. 52, pp. 55\u201371, 2019, 10.1016\/j.cola.2018.12.004.","DOI":"10.1016\/j.cola.2018.12.004"},{"key":"2024102315465062017_j_comp-2024-0006_ref_022","unstructured":"Peiling, \u201cError prone analyze and solution strategies for \"program structure\" in VB design,\u201d Inf. Comput., vol. 8, pp. 236\u2013237, 2019."},{"key":"2024102315465062017_j_comp-2024-0006_ref_023","doi-asserted-by":"crossref","unstructured":"A. Balapour, H. R. Nikkhah, and R. Sabherwal, \u201cMobile application security: Role of perceived privacy as the predictor of security perceptions,\u201d Int. J. Inf. Manag., vol. 52, p. 102063, 2020.","DOI":"10.1016\/j.ijinfomgt.2019.102063"},{"key":"2024102315465062017_j_comp-2024-0006_ref_024","doi-asserted-by":"crossref","unstructured":"V. H. Durelli, R. S. Durelli, S. S. Borges, A. T. Endo, M. M. Eler, D. R. Dias, et al., \u201cMachine learning applied to hardware testing: A systematic map** study,\u201d IEEE Trans. Reliab., vol. 68, no. 3, pp. 1189\u20131212, 2019.","DOI":"10.1109\/TR.2019.2892517"},{"key":"2024102315465062017_j_comp-2024-0006_ref_025","doi-asserted-by":"crossref","unstructured":"K. Shi, Y. Lu, J. Chang, and Z. Wei, \u201cPathPair2Vec: An AST path pair-based code representation method for defect prediction,\u201d J. Comput. Lang., vol. 59, p. 100979, 2020, 10.1016\/j.cola.2020.100979.","DOI":"10.1016\/j.cola.2020.100979"},{"key":"2024102315465062017_j_comp-2024-0006_ref_026","doi-asserted-by":"crossref","unstructured":"J. Yang, \u201cEvaluating and securing text-based java code through static code analyze,\u201d J. Cybersecur. Educ. Res. Pract., vol. 2020, no. 1, p. 3, 2020.","DOI":"10.62915\/2472-2707.1063"},{"key":"2024102315465062017_j_comp-2024-0006_ref_027","doi-asserted-by":"crossref","unstructured":"L. Kumar, S. Tummalapalli, S. C. Rathi, L. B. Murthy, A. Krishna, and S. Misra, \u201cMachine learning with word embedding for detecting web-services anti-patterns,\u201d J. Comput. Lang., vol. 75, p. 101207, 2023, 10.1016\/j.cola.2023.101207.","DOI":"10.1016\/j.cola.2023.101207"},{"key":"2024102315465062017_j_comp-2024-0006_ref_028","doi-asserted-by":"crossref","unstructured":"Z. Liao, \u201cA prediction model of the project life-span in open source hardware ecosystem,\u201d Mob. Netw. Appl., vol. 24, pp. 1382\u20131391, 2019.","DOI":"10.1007\/s11036-018-0993-3"},{"key":"2024102315465062017_j_comp-2024-0006_ref_029","doi-asserted-by":"crossref","unstructured":"A. F. da Silva, E. Borin, F. M. Pereira, N. L. Junior, and O. O. Napoli, \u201cProgram representations for predictive compilation: State of affairs in the early 20\u2019s,\u201d J. Comput. Lang., vol. 73, p. 101171, 2022, 10.1016\/j.cola.2022.101171.","DOI":"10.1016\/j.cola.2022.101171"},{"key":"2024102315465062017_j_comp-2024-0006_ref_030","doi-asserted-by":"crossref","unstructured":"S. Gonz\u00e1lez, \u201cA practical tutorial on bagging and boosting based ensembles for machine learning: Algorithms, software tools, performance study, practical perspectives and opportunities,\u201d Inf. Fusion., vol. 64, pp. 205\u2013237, 2020.","DOI":"10.1016\/j.inffus.2020.07.007"},{"key":"2024102315465062017_j_comp-2024-0006_ref_031","doi-asserted-by":"crossref","unstructured":"A. Plaia, \u201cComparing boosting and bagging for decision trees of rankings,\u201d J. Classifi., vol. 39, no. 1, pp. 78\u201399, 2022.","DOI":"10.1007\/s00357-021-09397-2"},{"key":"2024102315465062017_j_comp-2024-0006_ref_032","doi-asserted-by":"crossref","unstructured":"C. F. Kurz, W. Maier, and C. Rink, \u201cA greedy stacking algorithm for model ensembling and domain weighting,\u201d BMC Res. Notes, vol. 13, pp. 1\u20136, 2020.","DOI":"10.1186\/s13104-020-4931-7"},{"key":"2024102315465062017_j_comp-2024-0006_ref_033","doi-asserted-by":"crossref","unstructured":"N. L. Tsakiridis, \u201cA genetic algorithm-based stacking algorithm for predicting soil organic matter from vis-NIR spectral data,\u201d Eur. J. Soil. Sci., vol. 70, no. 3, pp. 578\u2013590, 2019.","DOI":"10.1111\/ejss.12760"},{"key":"2024102315465062017_j_comp-2024-0006_ref_034","doi-asserted-by":"crossref","unstructured":"T. Shippey, D. Bowes, and T. Hall, \u201cAutomatically identifying code features for software defect prediction: Using AST N-grams,\u201d Inf. Softw. Technol., vol. 106, pp. 142\u2013160, 2019.","DOI":"10.1016\/j.infsof.2018.10.001"},{"key":"2024102315465062017_j_comp-2024-0006_ref_035","doi-asserted-by":"crossref","unstructured":"A. Majd, M. Vahidi-Asl, A. Khalilian, P. Poorsarvi-Tehrani, and H. Haghighi, \u201cSLDeep: Statement-level software defect prediction using deep-learning model on static code features,\u201d Expert. Syst. 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