{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T17:15:04Z","timestamp":1781284504899,"version":"3.54.1"},"reference-count":85,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2021,4,5]],"date-time":"2021-04-05T00:00:00Z","timestamp":1617580800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,4,5]],"date-time":"2021-04-05T00:00:00Z","timestamp":1617580800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Empir Software Eng"],"published-print":{"date-parts":[[2021,5]]},"DOI":"10.1007\/s10664-020-09906-8","type":"journal-article","created":{"date-parts":[[2021,4,5]],"date-time":"2021-04-05T18:02:44Z","timestamp":1617645764000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["How to Better Distinguish Security Bug Reports (Using Dual Hyperparameter Optimization)"],"prefix":"10.1007","volume":"26","author":[{"given":"Rui","family":"Shu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianpei","family":"Xia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianfeng","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laurie","family":"Williams","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5040-3196","authenticated-orcid":false,"given":"Tim","family":"Menzies","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,4,5]]},"reference":[{"key":"9906_CR1","doi-asserted-by":"crossref","unstructured":"Agrawal A, Menzies T (2018) Is \u201cBetter Data\u201d Better than \u201cBetter Data Miner\u201d? (on the benefits of tuning SMOTE for defect prediction). In: Proceedings of the 40th international conference on software engineering, ACM, pp 1050\u20131061","DOI":"10.1145\/3180155.3180197"},{"key":"9906_CR2","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.infsof.2018.02.005","volume":"98","author":"A Agrawal","year":"2018","unstructured":"Agrawal A, Fu W, Menzies T (2018) What is wrong with topic modeling? and how to fix it using search-based software engineering. Inf Softw Technol 98:74\u201388","journal-title":"Inf Softw Technol"},{"key":"9906_CR3","doi-asserted-by":"crossref","unstructured":"Agrawal A, Fu W, Chen D, Shen X, Menzies T (2019) How to \u201cDODGE\u201d complex software analytics. IEEE Trans Softw Eng","DOI":"10.1109\/TSE.2019.2945020"},{"key":"9906_CR4","doi-asserted-by":"publisher","unstructured":"Arcuri A, Briand L (2011) A practical guide for using statistical tests to assess randomized algorithms in software engineering. In: Proceedings of the 33rd international conference on software engineering ICSE \u201911. https:\/\/doi.org\/10.1145\/1985793.1985795. ACM, New York, pp 1\u201310","DOI":"10.1145\/1985793.1985795"},{"issue":"2","key":"9906_CR5","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1007\/s10664-018-9633-6","volume":"24","author":"KE Bennin","year":"2019","unstructured":"Bennin KE, Keung JW, Monden A (2019) On the relative value of data resampling approaches for software defect prediction. Empir Softw Eng 24 (2):602\u2013636","journal-title":"Empir Softw Eng"},{"issue":"Feb","key":"9906_CR6","first-page":"281","volume":"13","author":"J Bergstra","year":"2012","unstructured":"Bergstra J, Bengio Y (2012) Random search for hyper-parameter optimization. J Mach Learn Res 13(Feb):281\u2013305","journal-title":"J Mach Learn Res"},{"key":"9906_CR7","unstructured":"Bergstra JS, Bardenet R, Bengio Y, K\u00e9gl B (2011) Algorithms for hyper-parameter optimization. In: Advances in neural information processing systems, pp 2546\u20132554"},{"key":"9906_CR8","first-page":"35","volume":"1","author":"A Biedenkapp","year":"2018","unstructured":"Biedenkapp A, Eggensperger K, Elsken T, Falkner S, Feurer M, Gargiani M, Hutter F, Klein A, Lindauer M, Loshchilov I et al (2018) Hyperparameter optimization. Artif Intell 1:35","journal-title":"Artif Intell"},{"issue":"4","key":"9906_CR9","doi-asserted-by":"publisher","first-page":"2398","DOI":"10.1007\/s10664-017-9566-5","volume":"23","author":"D Binkley","year":"2018","unstructured":"Binkley D, Lawrie D, Morrell C (2018) The need for software specific natural language techniques. Empir Softw Eng 23(4):2398\u20132425","journal-title":"Empir Softw Eng"},{"key":"9906_CR10","doi-asserted-by":"crossref","unstructured":"Black PE, Badger L, Guttman B, Fong E (2016) Dramatically reducing software vulnerabilities. Report to the White House Office of Science and Technology Policy, Information Technology Laboratory","DOI":"10.6028\/NIST.IR.8151"},{"key":"9906_CR11","doi-asserted-by":"crossref","unstructured":"Chan S, Treleaven P, Capra L (2013) Continuous hyperparameter optimization for large-scale recommender systems. In: 2013 IEEE international conference on big data, IEEE, pp 350\u2013358","DOI":"10.1109\/BigData.2013.6691595"},{"key":"9906_CR12","unstructured":"Chen L et al (2013) R2fix: automatically generating bug fixes from bug reports. Proceedings of the 2013 IEEE 6th ICST"},{"issue":"4","key":"9906_CR13","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1162\/106365605774666895","volume":"13","author":"K Deb","year":"2005","unstructured":"Deb K, Mohan M, Mishra S (2005) Evaluating the \u03b5-domination based multi-objective evolutionary algorithm for a quick computation of pareto-optimal solutions. Evol Comput 13(4):501\u2013525","journal-title":"Evol Comput"},{"key":"9906_CR14","doi-asserted-by":"crossref","unstructured":"Deshmukh J, Podder S, Sengupta S, Dubash N, et al. (2017) Towards accurate duplicate bug retrieval using deep learning techniques. In: 2017 IEEE international conference on software maintenance and evolution (ICSME). IEEE, pp 115\u2013124","DOI":"10.1109\/ICSME.2017.69"},{"key":"9906_CR15","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.is.2018.01.003","volume":"74","author":"C Di Francescomarino","year":"2018","unstructured":"Di Francescomarino C, Dumas M, Federici M, Ghidini C, Maggi F M, Rizzi W, Simonetto L (2018) Genetic algorithms for hyperparameter optimization in predictive business process monitoring. Inf Syst 74:67\u201383","journal-title":"Inf Syst"},{"key":"9906_CR16","doi-asserted-by":"publisher","DOI":"10.1201\/9780429246593","volume-title":"An introduction to the bootstrap","author":"B Efron","year":"1994","unstructured":"Efron B, Tibshirani RJ (1994) An introduction to the bootstrap. CRC Press, Boca Raton"},{"key":"9906_CR17","doi-asserted-by":"crossref","unstructured":"Feurer M, Springenberg JT, Hutter F (2015) Initializing bayesian hyperparameter optimization via meta-learning. In: Twenty-Ninth AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v29i1.9354"},{"key":"9906_CR18","doi-asserted-by":"crossref","unstructured":"Fu W, Menzies T (2017) Easy over hard: A case study on deep learning. In: Proceedings of the 2017 11th joint meeting on foundations of software engineering. ACM, pp 49\u201360","DOI":"10.1145\/3106237.3106256"},{"key":"9906_CR19","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/j.infsof.2016.04.017","volume":"76","author":"W Fu","year":"2016","unstructured":"Fu W, Menzies T, Shen X (2016) Tuning for software analytics: is it really necessary? Inf Softw Technol 76:135\u2013146","journal-title":"Inf Softw Technol"},{"key":"9906_CR20","doi-asserted-by":"crossref","unstructured":"Gegick M, Rotella P, Xie T (2010) Identifying security bug reports via text mining: An industrial case study. In: 2010 7th IEEE working conference on mining software repositories (MSR). IEEE, pp 11\u201320","DOI":"10.1109\/MSR.2010.5463340"},{"key":"9906_CR21","unstructured":"Goldberg DE (2006) Genetic algorithms. Pearson Education India"},{"key":"9906_CR22","doi-asserted-by":"crossref","unstructured":"Goseva-Popstojanova K, Tyo J (2018) Identification of security related bug reports via text mining using supervised and unsupervised classification. In: 2018 IEEE international conference on software quality, reliability and security (QRS). IEEE, pp 344\u2013355","DOI":"10.1109\/QRS.2018.00047"},{"key":"9906_CR23","volume-title":"Hackers & painters: big ideas from the computer age","author":"P Graham","year":"2004","unstructured":"Graham P (2004) Hackers & painters: big ideas from the computer age. O\u2019Reilly Media, Inc"},{"key":"9906_CR24","doi-asserted-by":"crossref","unstructured":"Han X, Yu T, Lo D (2018) Perflearner: learning from bug reports to understand and generate performance test frames. In: Proceedings of the 33rd ACM\/IEEE international conference on automated software engineering. ACM, pp 17\u201328","DOI":"10.1145\/3238147.3238204"},{"key":"9906_CR25","unstructured":"Herodotou H, Lim H, Luo G, Borisov N, Dong L, Cetin FB, Babu S (2011) Starfish: a self-tuning system for big data analytics. In: Cidr, vol 11, pp 261\u2013272"},{"issue":"2","key":"9906_CR26","doi-asserted-by":"publisher","first-page":"368","DOI":"10.1007\/s10664-015-9387-3","volume":"21","author":"A Hindle","year":"2016","unstructured":"Hindle A, Alipour A, Stroulia E (2016) A contextual approach towards more accurate duplicate bug report detection and ranking. Empir Softw Eng 21 (2):368\u2013410","journal-title":"Empir Softw Eng"},{"issue":"1","key":"9906_CR27","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1038\/scientificamerican0792-66","volume":"267","author":"JH Holland","year":"1992","unstructured":"Holland JH (1992) Genetic algorithms. Sci Am 267(1):66\u201373","journal-title":"Sci Am"},{"key":"9906_CR28","doi-asserted-by":"crossref","unstructured":"Huang Q, Xia X, Lo D (2017) Supervised vs unsupervised models: A holistic look at effort-aware just-in-time defect prediction. In: 2017 IEEE international conference on software maintenance and evolution (ICSME). IEEE, pp 159\u2013170","DOI":"10.1109\/ICSME.2017.51"},{"issue":"5","key":"9906_CR29","doi-asserted-by":"publisher","first-page":"2823","DOI":"10.1007\/s10664-018-9661-2","volume":"24","author":"Q Huang","year":"2019","unstructured":"Huang Q, Xia X, Lo D (2019) Revisiting supervised and unsupervised models for effort-aware just-in-time defect prediction. Empir Softw Eng 24 (5):2823\u20132862","journal-title":"Empir Softw Eng"},{"key":"9906_CR30","doi-asserted-by":"crossref","unstructured":"Jalali O, Menzies T, Feather M (2008) Optimizing requirements decisions with keys. In: Proceedings of the 4th international workshop on predictor models in software engineering. ACM, pp 79\u201386","DOI":"10.1145\/1370788.1370807"},{"issue":"11\u201312","key":"9906_CR31","doi-asserted-by":"publisher","first-page":"1073","DOI":"10.1016\/j.infsof.2007.02.015","volume":"49","author":"VB Kampenes","year":"2007","unstructured":"Kampenes VB, Dyb\u00e5 T, Hannay JE, Sj\u00f8berg DIK (2007) A systematic review of effect size in software engineering experiments. Inf Softw Technol 49(11\u201312):1073\u20131086","journal-title":"Inf Softw Technol"},{"key":"9906_CR32","doi-asserted-by":"crossref","unstructured":"Keller JM, Gray MR, Givens JA (1985) A fuzzy k-nearest neighbor algorithm. IEEE Trans Sys Man Cybern (4)580\u2013585","DOI":"10.1109\/TSMC.1985.6313426"},{"key":"9906_CR33","doi-asserted-by":"crossref","unstructured":"Kim S, Zhang H, Wu R, Gong L (2011) Dealing with noise in defect prediction. In: 2011 33rd international conference on software engineering (ICSE). IEEE, pp 481\u2013490","DOI":"10.1145\/1985793.1985859"},{"issue":"4598","key":"9906_CR34","doi-asserted-by":"publisher","first-page":"671","DOI":"10.1126\/science.220.4598.671","volume":"220","author":"S Kirkpatrick","year":"1983","unstructured":"Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671\u2013680","journal-title":"Science"},{"key":"9906_CR35","doi-asserted-by":"crossref","unstructured":"Kochhar PS, Xia X, Lo D, Li S (2016) Practitioners\u2019 expectations on automated fault localization. In: Proceedings of the 25th international symposium on software testing and analysis. ACM, pp 165\u2013176","DOI":"10.1145\/2931037.2931051"},{"key":"9906_CR36","doi-asserted-by":"crossref","unstructured":"Lamkanfi A, Demeyer S, Giger E, Goethals B (2010) Predicting the severity of a reported bug. In: 2010 7th IEEE working conference on mining software repositories (MSR). IEEE, pp 1\u201310","DOI":"10.1109\/MSR.2010.5463284"},{"key":"9906_CR37","doi-asserted-by":"crossref","unstructured":"Lazar A, Ritchey S, Sharif B (2014) Improving the accuracy of duplicate bug report detection using textual similarity measures. In: Proceedings of the 11th working conference on mining software repositories. ACM, pp 308\u2013311","DOI":"10.1145\/2597073.2597088"},{"issue":"4","key":"9906_CR38","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1109\/TSE.2008.35","volume":"34","author":"S Lessmann","year":"2008","unstructured":"Lessmann S, Baesens B, Mues C, Pietsch S (2008) Benchmarking classification models for software defect prediction: a proposed framework and novel findings. IEEE Trans Softw Eng 34(4):485\u2013496","journal-title":"IEEE Trans Softw Eng"},{"issue":"1","key":"9906_CR39","first-page":"6765","volume":"18","author":"L Li","year":"2017","unstructured":"Li L, Jamieson K, DeSalvo G, Rostamizadeh A, Talwalkar A (2017) Hyperband: a novel bandit-based approach to hyperparameter optimization. J Mach Learn Res 18(1):6765\u20136816","journal-title":"J Mach Learn Res"},{"key":"9906_CR40","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.infsof.2019.04.005","volume":"112","author":"T Menzies","year":"2019","unstructured":"Menzies T, Shepperd M (2019) \u201cBad smells\u201d in software analytics papers. Inf Softw Technol 112:35\u201347","journal-title":"Inf Softw Technol"},{"issue":"1","key":"9906_CR41","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1109\/TSE.2007.256941","volume":"33","author":"T Menzies","year":"2006","unstructured":"Menzies T, Greenwald J, Frank A (2006) Data mining static code attributes to learn defect predictors. IEEE Trans Softw Eng 33(1):2\u201313","journal-title":"IEEE Trans Softw Eng"},{"issue":"9","key":"9906_CR42","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1109\/TSE.2007.70721","volume":"33","author":"T Menzies","year":"2007","unstructured":"Menzies T, Dekhtyar A, Distefano J, Greenwald J (2007a) Problems with precision: a response to\u201d comments on\u2019data mining static code attributes to learn defect predictors\u2019\u201d. IEEE Trans Softw Eng 33(9):637\u2013640","journal-title":"IEEE Trans Softw Eng"},{"key":"9906_CR43","doi-asserted-by":"publisher","unstructured":"Menzies T, Elrawas O, Hihn J, Feather M, Madachy R, Boehm B (2007b) The business case for automated software engineering. In: Proceedings of the Twenty-second IEEE\/ACM international conference on automated software engineering ASE \u201907. https:\/\/doi.org\/10.1145\/1321631.1321676. ACM, New York, pp 303\u2013312","DOI":"10.1145\/1321631.1321676"},{"key":"9906_CR44","doi-asserted-by":"crossref","unstructured":"Menzies T, Greenwald J, Frank A (2007c) Data mining static code attributes to learn defect predictors. IEEE Trans Softw Engineering (1) 2\u201313","DOI":"10.1109\/TSE.2007.256941"},{"key":"9906_CR45","unstructured":"Menzies T, Majumder S, Balaji N, Brey K, Fu W (2018) 500+ times faster than deep learning:(a case study exploring faster methods for text mining stackoverflow). In: 2018 IEEE\/ACM 15Th international conference on mining software repositories (MSR). IEEE, pp 554\u2013563"},{"key":"9906_CR46","unstructured":"MITRE (2017) Common Vulnerabilities and Exposures (CVE). https:\/\/cve.mitre.org\/about\/terminology.html#vulnerability"},{"issue":"4","key":"9906_CR47","doi-asserted-by":"publisher","first-page":"537","DOI":"10.1109\/TSE.2012.45","volume":"39","author":"N Mittas","year":"2013","unstructured":"Mittas N, Angelis L (2013) Ranking and clustering software cost estimation models through a multiple comparisons algorithm. IEEE Trans Softw Eng 39(4):537\u2013551","journal-title":"IEEE Trans Softw Eng"},{"key":"9906_CR48","unstructured":"Nair V, Yu Z, Menzies T, Siegmund N, Apel S (2018) Finding faster configurations using flash. IEEE Trans Softw Eng"},{"key":"9906_CR49","unstructured":"Neuhaus S, Zimmermann T (2009) The beauty and the beast: vulnerabilities in red hat\u2019s packages. In: USENIX annual technical conference"},{"key":"9906_CR50","doi-asserted-by":"crossref","unstructured":"Neuhaus S, Zimmermann T, Holler C, Zeller A (2007) Predicting vulnerable software components. In: Proceedings of the 14th ACM conference on computer and communications security. ACM, pp 529\u2013540","DOI":"10.1145\/1315245.1315311"},{"key":"9906_CR51","doi-asserted-by":"crossref","unstructured":"Nguyen VH, Tran LMS (2010) Predicting vulnerable software components with dependency graphs. In: Proceedings of the 6th international workshop on security measurements and metrics. ACM, p 3","DOI":"10.1145\/1853919.1853923"},{"key":"9906_CR52","doi-asserted-by":"crossref","unstructured":"Novielli N, Girardi D, Lanubile F (2018) A benchmark study on sentiment analysis for software engineering research. In: 2018 IEEE\/ACM 15Th international conference on mining software repositories (MSR). IEEE, pp 364\u2013375","DOI":"10.1145\/3196398.3196403"},{"key":"9906_CR53","doi-asserted-by":"crossref","unstructured":"Ohira M, Kashiwa Y, Yamatani Y, Yoshiyuki H, Maeda Y, Limsettho N, Fujino K, Hata H, Ihara A, Matsumoto K (2015) A dataset of high impact bugs: manually-classified issue reports. In: 2015 IEEE\/ACM 12th working conference on mining software repositories (MSR). IEEE, pp 518\u2013521","DOI":"10.1109\/MSR.2015.78"},{"key":"9906_CR54","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.eswa.2016.06.005","volume":"62","author":"A Onan","year":"2016","unstructured":"Onan A, Koruko\u011flu S, Bulut H (2016) A multiobjective weighted voting ensemble classifier based on differential evolution algorithm for text sentiment classification. Expert Syst Appl 62:1\u201316","journal-title":"Expert Syst Appl"},{"key":"9906_CR55","doi-asserted-by":"crossref","unstructured":"Osman H, Ghafari M, Nierstrasz O (2017) Hyperparameter optimization to improve bug prediction accuracy. In: IEEE workshop on machine learning techniques for software quality evaluation (maLTeSQue). IEEE, pp 33\u201338","DOI":"10.1109\/MALTESQUE.2017.7882014"},{"key":"9906_CR56","doi-asserted-by":"crossref","unstructured":"Panichella A, Dit B, Oliveto R, Di Penta M, Poshyvanyk D, De Lucia A (2013) How to effectively use topic models for software engineering tasks? An approach based on genetic algorithms. In: International conference on software engineering","DOI":"10.1109\/ICSE.2013.6606598"},{"key":"9906_CR57","doi-asserted-by":"crossref","unstructured":"Parnin C, Orso A (2011) Are automated debugging techniques actually helping programmers?. In: Proceedings of the 2011 international symposium on software testing and analysis. ACM, pp 199\u2013209","DOI":"10.1145\/2001420.2001445"},{"key":"9906_CR58","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V et al (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825\u20132830","journal-title":"J Mach Learn Res"},{"key":"9906_CR59","doi-asserted-by":"crossref","unstructured":"Peters F, Tun T, Yu Y, Nuseibeh B (2018) Text filtering and ranking for security bug report prediction. IEEE Trans Softw Eng:Early\u2013Access","DOI":"10.1109\/TSE.2017.2787653"},{"issue":"10","key":"9906_CR60","doi-asserted-by":"publisher","first-page":"993","DOI":"10.1109\/TSE.2014.2340398","volume":"40","author":"R Scandariato","year":"2014","unstructured":"Scandariato R, Walden J, Hovsepyan A, Joosen W (2014) Predicting vulnerable software components via text mining. IEEE Trans Softw Eng 40(10):993\u20131006","journal-title":"IEEE Trans Softw Eng"},{"issue":"4","key":"9906_CR61","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1023\/A:1008202821328","volume":"11","author":"R Storn","year":"1997","unstructured":"Storn R, Price K (1997) Differential evolution\u2013a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341\u2013359","journal-title":"J Glob Optim"},{"key":"9906_CR62","doi-asserted-by":"crossref","unstructured":"Sun C, Lo D, Khoo SC, Jiang J (2011) Towards more accurate retrieval of duplicate bug reports. In: Proceedings of the 2011 26th IEEE\/ACM international conference on automated software engineering. IEEE Computer Society, pp 253\u2013262","DOI":"10.1109\/ASE.2011.6100061"},{"key":"9906_CR63","doi-asserted-by":"crossref","unstructured":"Tantithamthavorn C, McIntosh S, Hassan AE, Matsumoto K (2016) Automated parameter optimization of classification techniques for defect prediction models. In: 2016 IEEE\/ACM 38th international conference on software engineering (ICSE). IEEE, pp 321\u2013332","DOI":"10.1145\/2884781.2884857"},{"key":"9906_CR64","unstructured":"Tantithamthavorn C, Hassan AE, Matsumoto K (2018) The impact of class rebalancing techniques on the performance and interpretation of defect prediction models. IEEE Trans Softw Eng"},{"key":"9906_CR65","unstructured":"The Equifax Data Breach (2019) https:\/\/epic.org\/privacy\/data-breach\/equifax\/"},{"key":"9906_CR66","doi-asserted-by":"crossref","unstructured":"Thornton C, Hutter F, Hoos HH, Leyton-Brown K (2013) Auto-weka: combined selection and hyperparameter optimization of classification algorithms. In: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 847\u2013855","DOI":"10.1145\/2487575.2487629"},{"key":"9906_CR67","doi-asserted-by":"crossref","unstructured":"Tian Y, Lo D, Sun C (2012) Information retrieval based nearest neighbor classification for fine-grained bug severity prediction. In: 2012 19th working conference on reverse engineering. IEEE, pp 215\u2013224","DOI":"10.1109\/WCRE.2012.31"},{"issue":"5","key":"9906_CR68","doi-asserted-by":"publisher","first-page":"1354","DOI":"10.1007\/s10664-014-9331-y","volume":"20","author":"Y Tian","year":"2015","unstructured":"Tian Y, Lo D, Xia X, Sun C (2015) Automated prediction of bug report priority using multi-factor analysis. Empir Softw Eng 20(5):1354\u20131383","journal-title":"Empir Softw Eng"},{"key":"9906_CR69","doi-asserted-by":"crossref","unstructured":"Van Aken D, Pavlo A, Gordon GJ, Zhang B (2017) Automatic database management system tuning through large-scale machine learning. In: Proceedings of the 2017 ACM international conference on management of data. ACM, pp 1009\u20131024","DOI":"10.1145\/3035918.3064029"},{"key":"9906_CR70","unstructured":"Vesterstr\u00f8m J, Thomsen R (2004) A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems. In: Congress on evolutionary computation. IEEE"},{"issue":"2","key":"9906_CR71","doi-asserted-by":"publisher","first-page":"855","DOI":"10.1016\/j.eswa.2014.08.018","volume":"42","author":"L Wang","year":"2015","unstructured":"Wang L, Zeng Y, Chen T (2015) Back propagation neural network with adaptive differential evolution algorithm for time series forecasting. Expert Syst Appl 42(2):855\u2013863","journal-title":"Expert Syst Appl"},{"key":"9906_CR72","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/j.dss.2017.11.001","volume":"105","author":"Y Wang","year":"2018","unstructured":"Wang Y, Xu W (2018) Leveraging deep learning with lda-based text analytics to detect automobile insurance fraud. Decis Support Syst 105:87\u201395","journal-title":"Decis Support Syst"},{"key":"9906_CR73","unstructured":"WannaCry Ransomware Attack (2017) https:\/\/en.wikipedia.org\/wiki\/WannaCry_ransomware_attack"},{"key":"9906_CR74","doi-asserted-by":"crossref","unstructured":"Wijayasekara D, Manic M, McQueen M (2014) Vulnerability identification and classification via text mining bug databases. In: IECON 2014-40th annual conference of the IEEE industrial electronics society. IEEE, pp 3612\u20133618","DOI":"10.1109\/IECON.2014.7049035"},{"issue":"1","key":"9906_CR75","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1109\/4235.585893","volume":"1","author":"DH Wolpert","year":"1997","unstructured":"Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67\u201382","journal-title":"IEEE Trans Evol Comput"},{"key":"9906_CR76","doi-asserted-by":"crossref","unstructured":"Xia X, Lo D, Qiu W, Wang X, Zhou B (2014) Automated configuration bug report prediction using text mining. In: 2014 IEEE 38Th annual computer software and applications conference (COMPSAC). IEEE, pp 107\u2013116","DOI":"10.1109\/COMPSAC.2014.17"},{"issue":"3","key":"9906_CR77","doi-asserted-by":"publisher","first-page":"1094","DOI":"10.1109\/TR.2015.2484074","volume":"65","author":"X Xia","year":"2016","unstructured":"Xia X, Lo D, Shihab E, Wang X (2016) Automated bug report field reassignment and refinement prediction. IEEE Trans Reliab 65 (3):1094\u20131113","journal-title":"IEEE Trans Reliab"},{"key":"9906_CR78","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1016\/j.eswa.2017.02.017","volume":"78","author":"Y Xia","year":"2017","unstructured":"Xia Y, Liu C, Li Y, Liu N (2017) A boosted decision tree approach using bayesian hyper-parameter optimization for credit scoring. Expert Syst Appl 78:225\u2013241","journal-title":"Expert Syst Appl"},{"key":"9906_CR79","doi-asserted-by":"crossref","unstructured":"Yang X, Lo D, Huang Q, Xia X, Sun J (2016) Automated identification of high impact bug reports leveraging imbalanced learning strategies. In: 2016 IEEE 40Th annual computer software and applications conference (COMPSAC), vol 1. IEEE, pp 227\u2013232","DOI":"10.1109\/COMPSAC.2016.67"},{"issue":"1","key":"9906_CR80","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1007\/s11390-017-1713-3","volume":"32","author":"XL Yang","year":"2017","unstructured":"Yang XL, Lo D, Xia X, Huang Q, Sun JL (2017) High-impact bug report identification with imbalanced learning strategies. J Comput Sci Technol 32(1):181\u2013198","journal-title":"J Comput Sci Technol"},{"key":"9906_CR81","doi-asserted-by":"publisher","first-page":"112949","DOI":"10.1016\/j.eswa.2019.112949","volume":"141","author":"G Yildizdan","year":"2020","unstructured":"Yildizdan G, Baykan \u00d6K (2020) A novel modified bat algorithm hybridizing by differential evolution algorithm. Expert Syst Appl 141:112949","journal-title":"Expert Syst Appl"},{"key":"9906_CR82","doi-asserted-by":"crossref","unstructured":"Zaman S, Adams B, Hassan AE (2011) Security versus performance bugs: a case study on firefox. In: Proceedings of the 8th working conference on mining software repositories. ACM, pp 93\u2013102","DOI":"10.1145\/1985441.1985457"},{"key":"9906_CR83","doi-asserted-by":"crossref","unstructured":"Zhang T, Yang G, Lee B, Chan AT (2015) Predicting severity of bug report by mining bug repository with concept profile. In: Proceedings of the 30th annual ACM symposium on applied computing. ACM, pp 1553\u20131558","DOI":"10.1145\/2695664.2695872"},{"key":"9906_CR84","doi-asserted-by":"crossref","unstructured":"Zhou Y, Sharma A (2017) Automated identification of security issues from commit messages and bug reports. In: Proceedings of the 2017 11th joint meeting on foundations of software engineering, pp 914\u2013919","DOI":"10.1145\/3106237.3117771"},{"issue":"3","key":"9906_CR85","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1002\/smr.1770","volume":"28","author":"Y Zhou","year":"2016","unstructured":"Zhou Y, Tong Y, Gu R, Gall H (2016) Combining text mining and data mining for bug report classification. J Softw Evol Process 28(3):150\u2013176","journal-title":"J Softw Evol Process"}],"container-title":["Empirical Software Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10664-020-09906-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10664-020-09906-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10664-020-09906-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,23]],"date-time":"2022-12-23T12:03:20Z","timestamp":1671797000000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10664-020-09906-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,5]]},"references-count":85,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2021,5]]}},"alternative-id":["9906"],"URL":"https:\/\/doi.org\/10.1007\/s10664-020-09906-8","relation":{},"ISSN":["1382-3256","1573-7616"],"issn-type":[{"value":"1382-3256","type":"print"},{"value":"1573-7616","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,5]]},"assertion":[{"value":"6 November 2020","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 April 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"53"}}