{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T07:29:20Z","timestamp":1780558160453,"version":"3.54.1"},"reference-count":24,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,10,20]],"date-time":"2020-10-20T00:00:00Z","timestamp":1603152000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["20K11799"],"award-info":[{"award-number":["20K11799"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>Various big data sets are recorded on the server side of computer system. The big data are well defined as a volume, variety, and velocity (3V) model. The 3V model has been proposed by Gartner, Inc. as a first press release. 3V model means the volume, variety, and velocity in terms of data. The big data have 3V in well balance. Then, there are various categories in terms of the big data, e.g., sensor data, log data, customer data, financial data, weather data, picture data, movie data, and so on. In particular, the fault big data are well-known as the characteristic log data in software engineering. In this paper, we analyze the fault big data considering the unique features that arise from big data under the operation of open source software. In addition, we analyze actual data to show numerical examples of reliability assessment based on the results of multiple regression analysis well-known as the quantification method of the first type.<\/jats:p>","DOI":"10.3390\/make2040024","type":"journal-article","created":{"date-parts":[[2020,10,20]],"date-time":"2020-10-20T09:28:23Z","timestamp":1603186103000},"page":"436-452","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Large Scale Fault Data Analysis and OSS Reliability Assessment Based on Quantification Method of the First Type"],"prefix":"10.3390","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7665-5765","authenticated-orcid":false,"given":"Yoshinobu","family":"Tamura","sequence":"first","affiliation":[{"name":"Department of Intelligent Systems, Faculty of Information Technology, Tokyo City University, Tokyo 158-8557, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shigeru","family":"Yamada","sequence":"additional","affiliation":[{"name":"Graduate School of Engineering, Tottori University, Tottori 680-8552, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yamada, S. (2014). Software Reliability Modeling: Fundamentals and Applications, Springer.","DOI":"10.1007\/978-4-431-54565-1"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Kapur, P.K., Pham, H., Gupta, A., and Jha, P.C. (2011). Software Reliability Assessment with OR Applications, Springer.","DOI":"10.1007\/978-0-85729-204-9"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Yamada, S., and Tamura, Y. (2016). OSS Reliability Measurement and Assessment, Springer International Publishing.","DOI":"10.1007\/978-3-319-31818-9"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhou, Y., and Davis, J. (2005, January 17). Open source software reliability model: An empirical approach. Proceedings of the Fifth Workshop on Open Source Software Engineering (5-WOSSE), St Louis, MO, USA.","DOI":"10.1145\/1083258.1083273"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1109\/MS.2004.1259211","article-title":"Mission-critical development with open source software","volume":"21","author":"Norris","year":"2004","journal-title":"IEEE Softw. Mag."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1016\/j.infsof.2014.06.015","article-title":"Investigating software testing and maintenance reports: Case study","volume":"58","author":"Janczarek","year":"2015","journal-title":"Inf. Softw. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"84253","DOI":"10.1109\/ACCESS.2019.2924084","article-title":"A Generalized Software Reliability Growth Model With Consideration of the Uncertainty of Operating Environments","volume":"7","author":"Li","year":"2019","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Tariq, I., Maqsood, T.B., Hayat, B., Hameed, K., Nasir, M., and Jahangir, M. (2018, January 3\u20134). The comprehensive study on software reliability. Proceedings of the 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), Sukkur, Pakistan.","DOI":"10.1109\/ICOMET.2018.8346316"},{"key":"ref_9","unstructured":"Korpalski, M., and Sosnowski, J. (June, January 27). Correlating software metrics with software defects. Proceedings of the Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments, Wilga, Poland."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1007\/s11219-014-9241-7","article-title":"Which process metrics can significantly improve defect prediction models? An empirical study","volume":"23","author":"Madeyski","year":"2015","journal-title":"Softw. Qual. J."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Park, N.J., George, K.M., and Park, N. (2010, January 18\u201320). A multiple regression model for trend change prediction. Proceedings of the 2010 International Conference on Financial Theory and Engineering, Dubai, UAE.","DOI":"10.1109\/ICFTE.2010.5499430"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Aiyin, W., and Yanmei, X. (2018, January 26\u201327). Multiple Linear Regression Analysis of Real Estate Price. Proceedings of the 2018 International Conference on Robots & Intelligent System (ICRIS), Changsha, China.","DOI":"10.1109\/ICRIS.2018.00145"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Rahil, A., Mbarek, N., Togni, O., Atieh, M., and Fouladkar, A. (May, January 29). Statistical learning and multiple linear regression model for network selection using MIH. Proceedings of the Third International Conference on e-Technologies and Networks for Development (ICeND2014), Beirut, Lebanon.","DOI":"10.1109\/ICeND.2014.6991378"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Singh, V.B., Sharma, M., and Pham, H. (2017). Entropy based software reliability analysis of multi-version open source software. IEEE Trans. Softw. Eng.","DOI":"10.1109\/TSE.2017.2766070"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Lavazza, L., Morasca, S., Taibi, D., and Tosi, D. (2012, January 26\u201330). An empirical investigation of perceived reliability of open source Java programs. Proceedings of the 27th Annual ACM Symposium on Applied Computing (SAC \u201912), Trento, Italy.","DOI":"10.1145\/2245276.2231951"},{"key":"ref_16","unstructured":"(2020, October 14). The Apache Software Foundation, The Apache HTTP Server Project. Available online: http:\/\/httpd.apache.org\/."},{"key":"ref_17","first-page":"273","article-title":"Dependability analysis tool based on multi-dimensional stochastic noisy model for cloud computing with big data","volume":"2","author":"Tamura","year":"2017","journal-title":"Int. J. Math. Eng. Manag. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1007\/s41872-017-0009-5","article-title":"Open source software cost analysis with fault severity levels based on stochastic differential equation models","volume":"6","author":"Tamura","year":"2017","journal-title":"J. Life Cycle Reliab. Saf. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Tamura, Y., and Yamada, S. (2017). Dependability analysis tool considering the optimal data partitioning in a mobile cloud. Reliability Modeling with Computer and Maintenance Applications, World Scientific.","DOI":"10.1142\/9789813224506_0003"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1850014-1","DOI":"10.1142\/S0218539318500146","article-title":"Multi-dimensional software tool for OSS project management considering cloud with big data","volume":"25","author":"Tamura","year":"2018","journal-title":"Int. J. Reliab. Qual. Saf. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Tamura, Y., and Yamada, S. (2019). Maintenance effort management based on double jump diffusion model for OSS project. Ann. Oper. Res., 1\u201316.","DOI":"10.1007\/s10479-019-03170-w"},{"key":"ref_22","unstructured":"Freiberger, W. (1972). Software Reliability Research, in Statistical Computer Performance Evaluation, Academic Press."},{"key":"ref_23","unstructured":"Yin, L., and Trivedi, K.S. (1999, January 1\u20134). Confidence interval estimation of NHPP-based software reliability models. Proceedings of the 10th International Symposium on Software Reliability Engineering (Cat. No.PR00443), Boca Raton, FL, USA."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Okamura, H., Grottke, M., Dohi, T., and Trivedi, K.S. (2007, January 25\u201328). Variational Bayesian Approach for Interval Estimation of NHPP-Based Software Reliability Models. Proceedings of the 37th Annual IEEE\/IFIP International Conference on Dependable Systems and Networks (DSN\u201907), Edinburgh, UK.","DOI":"10.1109\/DSN.2007.101"}],"container-title":["Machine Learning and Knowledge Extraction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-4990\/2\/4\/24\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:24:38Z","timestamp":1760178278000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-4990\/2\/4\/24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,20]]},"references-count":24,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["make2040024"],"URL":"https:\/\/doi.org\/10.3390\/make2040024","relation":{},"ISSN":["2504-4990"],"issn-type":[{"value":"2504-4990","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,20]]}}}