{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T08:55:54Z","timestamp":1743152154748,"version":"3.40.3"},"publisher-location":"Cham","reference-count":51,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031365966"},{"type":"electronic","value":"9783031365973"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-36597-3_5","type":"book-chapter","created":{"date-parts":[[2023,7,7]],"date-time":"2023-07-07T15:01:58Z","timestamp":1688742118000},"page":"93-115","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Empirical Validation of\u00a0Entropy-Based Redundancy Metrics as\u00a0Reliability Indicators Using Fault-Proneness Attribute and\u00a0Complexity Metrics"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9938-2384","authenticated-orcid":false,"given":"Dalila","family":"Amara","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5657-4682","authenticated-orcid":false,"given":"Latifa","family":"Ben Arfa Rabai","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,8]]},"reference":[{"key":"5_CR1","unstructured":"Boehm Barry, W., Brown John R., Lipow, Mlity: Quantitative evaluation of software quality. In: Proceedings of the 2nd International Conference On Software Engineering, pp. 592\u2013605 (1976)"},{"key":"5_CR2","unstructured":"Iso, ISO: iec\/ieee international standard-systems and software engineering-vocabulary. In: ISO\/IEC\/IEEE 24765 (2017)"},{"key":"5_CR3","doi-asserted-by":"crossref","unstructured":"Fenton, N., Bieman, J.: Software metrics: a rigorous and practical approach. CRC Press (2014)","DOI":"10.1201\/b17461"},{"key":"5_CR4","doi-asserted-by":"crossref","unstructured":"Arvanitou, E., Ampatzoglou, A., Chatzigeorgiou, A., Galster M., Avgeriou, P.: A mapping study on design-time quality attributes and metrics. In: Journal of Systems and Software, pp. 52\u201377. Elsevier (2017)","DOI":"10.1016\/j.jss.2017.01.026"},{"key":"5_CR5","doi-asserted-by":"crossref","unstructured":"Fenton, N.: Software measurement: A necessary scientific basis. In: IEEE Transactions on Software Engineering, pp. 199\u2013206. Elsevier (1994)","DOI":"10.1109\/32.268921"},{"key":"5_CR6","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1007\/978-3-540-70621-2_14","volume-title":"Software and Data Technologies","author":"O G\u00f3mez","year":"2008","unstructured":"G\u00f3mez, O., Oktaba, H., Piattini, M., Garc\u00eda, F.: A systematic review measurement in software engineering: state-of-the-art in measures. In: Filipe, J., Shishkov, B., Helfert, M. (eds.) ICSOFT 2006. CCIS, vol. 10, pp. 165\u2013176. Springer, Heidelberg (2008). https:\/\/doi.org\/10.1007\/978-3-540-70621-2_14"},{"key":"5_CR7","unstructured":"Lyu Michael, R.: Handbook of software reliability engineering. In: IEEE Computer Society Press CA, pp. 165\u2013176. IEEE (1996)"},{"key":"5_CR8","doi-asserted-by":"crossref","unstructured":"Nu\u00f1ez-Varela, S., P\u00e9rez G., H\u00e9ctor, G., Mart\u00ednez P., Francisco E., Soubervielle-Montalvo, C.: Source code metrics: A systematic mapping study. In: Journal of Systems and Software, pp. 164\u2013197. Elsevier (2017)","DOI":"10.1016\/j.jss.2017.03.044"},{"key":"5_CR9","doi-asserted-by":"crossref","unstructured":"Reddivari, S., Raman, J.: Software Quality Prediction: An Investigation Based on Machine Learning. In: 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI), pp. 115\u2013122. IEEE (2019)","DOI":"10.1109\/IRI.2019.00030"},{"key":"5_CR10","doi-asserted-by":"crossref","unstructured":"Chidamber S.R., Kemerer, C.F.: A metrics suite for object oriented design. In: IEEE Transactions on software engineering, pp. 476\u2013493. IEEE (1994)","DOI":"10.1109\/32.295895"},{"key":"5_CR11","doi-asserted-by":"crossref","unstructured":"Li, W.: Another metric suite for object-oriented programming. In: Journal of Systems and Software, pp. 155\u2013162. Elsevier (1998)","DOI":"10.1016\/S0164-1212(98)10052-3"},{"key":"5_CR12","doi-asserted-by":"crossref","unstructured":"Briand, L.C., W\u00fcst, J.: Empirical studies of quality models in object-oriented systems. In: Advances in Computers, pp. 97\u2013166. Elsevier (2002)","DOI":"10.1016\/S0065-2458(02)80005-5"},{"issue":"3","key":"5_CR13","doi-asserted-by":"publisher","first-page":"640","DOI":"10.1007\/s10664-013-9291-7","volume":"20","author":"R Jabangwe","year":"2014","unstructured":"Jabangwe, R., B\u00f6rstler, J., \u0160mite, D., Wohlin, C.: Empirical evidence on the link between object-oriented measures and external quality attributes: a systematic literature review. Empirical Softw. Eng. 20(3), 640\u2013693 (2014). https:\/\/doi.org\/10.1007\/s10664-013-9291-7","journal-title":"Empirical Softw. Eng."},{"issue":"3","key":"5_CR14","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1007\/s11334-014-0233-3","volume":"10","author":"A Mili","year":"2014","unstructured":"Mili, A., Jaoua, A., Frias, M., Helali, R.G.M.: Semantic metrics for software products. Innov. Syst. Softw. Eng. 10(3), 203\u2013217 (2014). https:\/\/doi.org\/10.1007\/s11334-014-0233-3","journal-title":"Innov. Syst. Softw. Eng."},{"key":"5_CR15","unstructured":"Mili, A., Tchier, F.: Software testing: Concepts and operations. In: John Wiley & Sons. (2015)"},{"key":"5_CR16","doi-asserted-by":"crossref","unstructured":"Shannon, C.E.: A mathematical theory of communication. In: ACM SIGMOBILE Mobile Computing and Communications Review, pp. 3\u201355. Springer (2001)","DOI":"10.1145\/584091.584093"},{"key":"5_CR17","unstructured":"Singh, V.B., Chaturvedi, K.K.: Semantic metrics for software products. In: International Conference on Computational Science and Its Applications, pp. 408\u2013426. Springer (2013)"},{"key":"5_CR18","doi-asserted-by":"crossref","unstructured":"Singh, V.B., Chaturvedi, K.K.: Software reliability modeling based on ISO\/IEC SQuaRE. In: Information and Software Technology, pp. 18\u201329. Elsevier (2016)","DOI":"10.1016\/j.infsof.2015.09.006"},{"key":"5_CR19","doi-asserted-by":"crossref","unstructured":"Amara, D., Rabai, L.B.A.: Towards a new framework of software reliability measurement based on software metrics. In: Procedia Computer Science, pp. 725\u2013730. Elsevier (2017)","DOI":"10.1016\/j.procs.2017.05.428"},{"key":"5_CR20","doi-asserted-by":"crossref","unstructured":"Bansiya, J., Davis, C.G.: A hierarchical model for object-oriented design quality assessment. In: IEEE Transactions on software Engineering, pp. 4\u201317. IEEE, (2002)","DOI":"10.1109\/32.979986"},{"key":"5_CR21","doi-asserted-by":"crossref","unstructured":"Catal, C., Diri, B.: A systematic review of software fault prediction studies. In: Expert Systems with Applications, pp. 7346\u20137354. Elsevier (2009)","DOI":"10.1016\/j.eswa.2008.10.027"},{"key":"5_CR22","doi-asserted-by":"crossref","unstructured":"Radjenovi\u0107, D., Heri\u010dko, M., Torkar, R., \u017divkovi\u010d, A.: Software fault prediction metrics: A systematic literature review. In: Information and Software Technology, pp. 1397\u20131418. Elsevier (2013)","DOI":"10.1016\/j.infsof.2013.02.009"},{"key":"5_CR23","doi-asserted-by":"crossref","unstructured":"Asghari, S.A., Marvasti, M.B., Rahmani, A.M.: Enhancing transient fault tolerance in embedded systems through an OS task level redundancy approach. In: Future Generation Computer Systems, pp. 58\u201365. Elsevier (2018)","DOI":"10.1016\/j.future.2018.04.049"},{"key":"5_CR24","doi-asserted-by":"publisher","unstructured":"Dubrova, E.: Fault-tolerant design. Springer (2013). https:\/\/doi.org\/10.1007\/978-1-4614-2113-9","DOI":"10.1007\/978-1-4614-2113-9"},{"key":"5_CR25","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-29157-0_1","volume-title":"Software Technologies","author":"A Ayad","year":"2019","unstructured":"Ayad, A., Marsit, I., Mohamed Omri, N., Loh, J.M., Mili, A.: Using semantic metrics to predict mutation equivalence. In: van Sinderen, M., Maciaszek, L.A. (eds.) ICSOFT 2018. CCIS, vol. 1077, pp. 3\u201327. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-29157-0_1"},{"key":"5_CR26","doi-asserted-by":"crossref","unstructured":"Singh, A., Bhatia, R., Singhrova, A.: Taxonomy of machine learning algorithms in software fault prediction using object oriented metrics. In: Procedia Computer Science, pp. 993\u20131001. Elsevier (2018)","DOI":"10.1016\/j.procs.2018.05.115"},{"issue":"24","key":"5_CR27","doi-asserted-by":"publisher","first-page":"7417","DOI":"10.1007\/s00500-016-2284-x","volume":"21","author":"SS Rathore","year":"2016","unstructured":"Rathore, S.S., Kumar, S.: An empirical study of some software fault prediction techniques for the number of faults prediction. Soft Comput. 21(24), 7417\u20137434 (2016). https:\/\/doi.org\/10.1007\/s00500-016-2284-x","journal-title":"Soft Comput."},{"key":"5_CR28","doi-asserted-by":"crossref","unstructured":"Kumar, L., Misra, S., Rath, S.Ku.: An empirical analysis of the effectiveness of software metrics and fault prediction model for identifying faulty classes. In: Computer Standards & Interfaces, pp. 1\u201332. Elsevier (2017)","DOI":"10.1016\/j.csi.2017.02.003"},{"key":"5_CR29","doi-asserted-by":"crossref","unstructured":"Gondra, I.: Applying machine learning to software fault-proneness prediction. In: Journal of Systems and Software, pp. 186\u2013195. Elsevier (2008)","DOI":"10.1016\/j.jss.2007.05.035"},{"key":"5_CR30","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-29157-0_1","volume-title":"Software Technologies","author":"A Ayad","year":"2019","unstructured":"Ayad, A., Marsit, I., Mohamed Omri, N., Loh, J.M., Mili, A.: Using semantic metrics to predict mutation equivalence. In: van Sinderen, M., Maciaszek, L.A. (eds.) ICSOFT 2018. CCIS, vol. 1077, pp. 3\u201327. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-29157-0_1"},{"key":"5_CR31","unstructured":"Menzies, T., DiStefano, J., Orrego, A., Chapman, R.: Assessing predictors of software defects. In: Proceedings of the Workshop Predictive Software Models (2004)"},{"key":"5_CR32","doi-asserted-by":"crossref","unstructured":"Olague, H.M. Etzkorn, L.H., Gholston, S., Quattlebaum, S.: Empirical validation of three software metrics suites to predict fault-proneness of object-oriented classes developed using highly iterative or agile software development processes. In: IEEE Transactions on software Engineering, pp. 402\u2013419. IEEE (2007)","DOI":"10.1109\/TSE.2007.1015"},{"key":"5_CR33","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Xu, B., Leung, H.: On the ability of complexity metrics to predict fault-prone classes in object-oriented systems. In: Journal of Systems and Software, pp. 660\u2013674. Elsevier (2010)","DOI":"10.1016\/j.jss.2009.11.704"},{"key":"5_CR34","doi-asserted-by":"crossref","unstructured":"He, P., Li, B., Liu, X., Chen, J., Ma, Y.: An empirical study on software defect prediction with a simplified metric set. In: Information and Software Technology, pp. 170\u2013190. Elsevier (2015)","DOI":"10.1016\/j.infsof.2014.11.006"},{"key":"5_CR35","doi-asserted-by":"crossref","unstructured":"Kaur, A., Kaur, I.: An empirical evaluation of classification algorithms for fault prediction in open source projects. In: Journal of King Saud University-Computer and Information Sciences, pp. 2\u201317. Elsevier (2018)","DOI":"10.1016\/j.jksuci.2016.04.002"},{"key":"5_CR36","unstructured":"Lomio, F., Moreschini, S., Lenarduzzi, V.: Fault Prediction based on Software Metrics and SonarQube Rules. Machine or Deep Learning?. In: arXiv preprint arXiv:2103.11321 Elsevier (2021)"},{"key":"5_CR37","doi-asserted-by":"crossref","unstructured":"Kitchenham, B., Pfleeger, S.L., Fenton, N.: Towards a framework for software measurement validation. In: IEEE Transactions on Software Engineering, pp. 929\u2013944, IEEE (1995)","DOI":"10.1109\/32.489070"},{"key":"5_CR38","doi-asserted-by":"crossref","unstructured":"Basili, V.R., Briand, L.C., Melo, W.L.: A validation of object-oriented design metrics as quality indicators. In: IEEE Transactions on Software Engineering, pp. 751\u2013761, IEEE (1996)","DOI":"10.1109\/32.544352"},{"key":"5_CR39","doi-asserted-by":"crossref","unstructured":"Schneidewind, N.F.: Methodology for validating software metrics. In: IEEE Transactions on Software Engineering, pp. 410\u2013422, IEEE (1992)","DOI":"10.1109\/32.135774"},{"key":"5_CR40","doi-asserted-by":"crossref","unstructured":"Arvanitou, E.Maria., Ampatzoglou, A., Chatzigeorgiou, A., Avgeriou, P.: Software metrics fluctuation: a property for assisting the metric selection process. In: Information and Software Technology, pp. 110\u2013124, Elsevier (2016)","DOI":"10.1016\/j.infsof.2015.12.010"},{"key":"5_CR41","doi-asserted-by":"crossref","unstructured":"Kumar, L.N., Debendra, K., Rath, S.Ku.: Validating the effectiveness of object-oriented metrics for predicting maintainability. In: Procedia Computer Science, pp. 798\u2013806, Elsevier (2015)","DOI":"10.1016\/j.procs.2015.07.479"},{"key":"5_CR42","unstructured":"Verma, D.K., Kumar, S.: Prediction of Defect Density for Open Source Software using Repository Metrics. In: J. Web Eng, pp. 294\u2013311 (2017)"},{"key":"5_CR43","doi-asserted-by":"crossref","unstructured":"Malhotra, R.: A systematic review of machine learning techniques for software fault prediction. In: Applied Soft Computing, pp. 504\u2013518, Elsevier (2015)","DOI":"10.1016\/j.asoc.2014.11.023"},{"key":"5_CR44","doi-asserted-by":"crossref","unstructured":"Turabieh, H., Mafarja, M., Li, X.: Iterated feature selection algorithms with layered recurrent neural network for software fault prediction. In: Expert Systems with Applications, pp. 27\u201342, Elsevier (2019)","DOI":"10.1016\/j.eswa.2018.12.033"},{"key":"5_CR45","doi-asserted-by":"crossref","unstructured":"Amara, D., Fatnassi, E., Rabai, L.: An Empirical Assessment and Validation of Redundancy Metrics Using Defect Density as Reliability Indicator. In: Scientific Programming, Hindawi (2021)","DOI":"10.1155\/2021\/8325417"},{"issue":"12","key":"5_CR46","doi-asserted-by":"publisher","first-page":"8945","DOI":"10.1007\/s10489-021-02346-x","volume":"51","author":"SS Rathore","year":"2021","unstructured":"Rathore, S.S., Kumar, S.: Software fault prediction based on the dynamic selection of learning technique: findings from the eclipse project study. Appl. Intell. 51(12), 8945\u20138960 (2021). https:\/\/doi.org\/10.1007\/s10489-021-02346-x","journal-title":"Appl. Intell."},{"key":"5_CR47","doi-asserted-by":"crossref","unstructured":"Delahaye, M., Du Bousquet, L.: A comparison of mutation analysis tools for java. In: 13th International Conference on Quality Software, pp. 187\u2013195, IEEE (2013)","DOI":"10.1109\/QSIC.2013.47"},{"key":"5_CR48","doi-asserted-by":"crossref","unstructured":"Bansiya, J., Davis, C.G.: A hierarchical model for object-oriented design quality assessment. In: IEEE Transactions on Software Engineering, pp. 4\u201317, IEEE (2002)","DOI":"10.1109\/32.979986"},{"key":"5_CR49","doi-asserted-by":"crossref","unstructured":"Gall, CS., et al.: Semantic software metrics computed from natural language design specifications. In: IET Software, pp. 17\u201326, IET (2008)","DOI":"10.1049\/iet-sen:20070109"},{"key":"5_CR50","doi-asserted-by":"crossref","unstructured":"Koru, A.G., Liu, H.: Building effective defect-prediction models in practice. In: IEEE Software, pp. 23\u201329, IEEE (2005)","DOI":"10.1109\/MS.2005.149"},{"key":"5_CR51","doi-asserted-by":"crossref","unstructured":"Amara, D., Rabai, L.B.A.: Classification Techniques Use to Empirically Validate Redundancy Metrics as Reliability Indicators based on Fault-proneness Attribute. In: ENASE, pp. 209\u2013220, ENASE, (2022)","DOI":"10.5220\/0011081900003176"}],"container-title":["Communications in Computer and Information Science","Evaluation of Novel Approaches to Software Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-36597-3_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,7]],"date-time":"2023-07-07T15:02:39Z","timestamp":1688742159000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-36597-3_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031365966","9783031365973"],"references-count":51,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-36597-3_5","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"8 July 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ENASE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Evaluation of Novel Approaches to Software Engineering","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 April 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 April 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"enase2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/enase.scitevents.org\/?y=2022","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"PRIMORIS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"109","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"15","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"14% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}