{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T17:02:12Z","timestamp":1771261332586,"version":"3.50.1"},"reference-count":80,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2018,6,4]],"date-time":"2018-06-04T00:00:00Z","timestamp":1528070400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100002790","name":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["RGPIN-2017-05609"],"award-info":[{"award-number":["RGPIN-2017-05609"]}],"id":[{"id":"10.13039\/501100002790","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Empir Software Eng"],"published-print":{"date-parts":[[2019,4]]},"DOI":"10.1007\/s10664-018-9629-2","type":"journal-article","created":{"date-parts":[[2018,6,4]],"date-time":"2018-06-04T10:35:05Z","timestamp":1528108505000},"page":"562-601","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["What can Android mobile app developers do about the energy consumption of machine learning?"],"prefix":"10.1007","volume":"24","author":[{"given":"Andrea","family":"McIntosh","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Safwat","family":"Hassan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4373-4958","authenticated-orcid":false,"given":"Abram","family":"Hindle","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,6,4]]},"reference":[{"issue":"1","key":"9629_CR1","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1109\/TKDE.2010.36","volume":"23","author":"H Abdulsalam","year":"2011","unstructured":"Abdulsalam H, Skillicorn DB, Martin P (2011) Classification using streaming random forests. IEEE Trans Knowl Data Eng 23(1):22\u201336","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"9629_CR2","doi-asserted-by":"crossref","unstructured":"Aggarwal K, Hindle A, Stroulia E (2015a) Greenadvisor: A tool for analyzing the impact of software evolution on energy consumption. In: International Conference on Software Maintenance and Evolution (ICSME 2015), pp 311\u2013320. http:\/\/softwareprocess.ca\/pubs\/aggarwal2015ICSME-greenadvisor.pdf","DOI":"10.1109\/ICSM.2015.7332477"},{"key":"9629_CR3","doi-asserted-by":"crossref","unstructured":"Aggarwal K, Hindle A, Stroulia E (2015b) Greenadvisor: a tool for analyzing the impact of software evolution on energy consumption. In: 31St IEEE international conference on software maintenance and evolution. IEEE computer society","DOI":"10.1109\/ICSM.2015.7332477"},{"key":"9629_CR4","doi-asserted-by":"publisher","unstructured":"Agolli T, Pollock L, Clause J (2017) Investigating decreasing energy usage in mobile apps via indistinguishable color changes. In: 2017 IEEE\/ACM 4Th international conference on mobile software engineering and systems (MOBILESoft), pp 30\u201334. https:\/\/doi.org\/10.1109\/MOBILESoft.2017.17","DOI":"10.1109\/MOBILESoft.2017.17"},{"key":"9629_CR5","unstructured":"Akdeniz: Google Play Crawler (2013) https:\/\/github.com\/Akdeniz\/google-play-crawler (Last accessed: May 2018)"},{"key":"9629_CR6","first-page":"37","volume":"6","author":"D Aha","year":"1991","unstructured":"Aha D, Kibler D (1991) Instance-based learning algorithms. Mach Learn 6:37\u201366","journal-title":"Mach Learn"},{"key":"9629_CR7","unstructured":"App Annie (2018a) App Annie. https:\/\/www.appannie.com\/ (Last accessed: May 2018)"},{"key":"9629_CR8","unstructured":"AppBrain (2018b) Top Android phones. http:\/\/www.appbrain.com\/stats\/top-android-phones (Last accessed: May 2018)"},{"key":"9629_CR9","unstructured":"Apple Inc (2017) The future is here: iphone https:\/\/www.apple.com\/newsroom\/2017\/09\/the-future-is-here-iphone-x\/ (Retrieved April 2018)"},{"key":"9629_CR10","doi-asserted-by":"crossref","unstructured":"Banerjee A, Chong LK, Chattopadhyay S, Roychoudhury A (2014) Detecting energy bugs and hotspots in mobile apps. In: Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering. ACM, pp 588\u2013598","DOI":"10.1145\/2635868.2635871"},{"key":"9629_CR11","unstructured":"Benfield L (2018) Cfr - another java decompiler. http:\/\/www.benf.org\/other\/cfr\/ (Last accessed: May 2018)"},{"key":"9629_CR12","doi-asserted-by":"publisher","unstructured":"Bhattacharya S, Lane ND (2016) Sparsification and separation of deep learning layers for constrained resource inference on wearables. In: Proceedings of the 14th ACM Conference on Embedded Network Sensor Systems CD-ROM, SenSys \u201916. ACM, New York, pp 176\u2013189. https:\/\/doi.org\/10.1145\/2994551.2994564","DOI":"10.1145\/2994551.2994564"},{"key":"9629_CR13","volume-title":"Online Algorithms and Stochastic Approximations","author":"L Bottou","year":"1998","unstructured":"Bottou L (1998) Online Algorithms and Stochastic Approximations. Cambridge University Press, Cambridge"},{"issue":"1","key":"9629_CR14","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45(1):5\u201332","journal-title":"Mach Learn"},{"key":"9629_CR15","doi-asserted-by":"publisher","unstructured":"Bruce BR, Petke J, Harman M (2015) Reducing energy consumption using genetic improvement. In: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, GECCO \u201915. ACM, New York, pp 1327\u20131334. https:\/\/doi.org\/10.1145\/2739480.2754752","DOI":"10.1145\/2739480.2754752"},{"key":"9629_CR16","doi-asserted-by":"crossref","unstructured":"Chenlei Z, Hindle A, German DM (2014) The impact of user choice on energy consumption. IEEE Software, pp 69\u201375. http:\/\/softwareprocess.ca\/pubs\/zhang2014IEEESoftware-user-choice.pdf","DOI":"10.1109\/MS.2014.27"},{"key":"9629_CR17","doi-asserted-by":"publisher","unstructured":"Chowdhury SA, Hindle A (2016a) Greenoracle: Estimating software energy consumption with energy measurement corpora. In: Proceedings of the 13th International Conference on Mining Software Repositories, MSR \u201916. ACM, New York, pp 49\u201360. https:\/\/doi.org\/10.1145\/2901739.2901763","DOI":"10.1145\/2901739.2901763"},{"key":"9629_CR18","doi-asserted-by":"publisher","unstructured":"Chowdhury S, Sapra V, Hindle A (2016b) Client-side energy efficiency of http\/2 for web and mobile app developers. In: 23rd IEEE International Conference on Software Analysis, Evolution, and Reengineering (SANER 2016), pp 529\u2013540. https:\/\/doi.org\/10.1109\/SANER.2016.77 . http:\/\/softwareprocess.ca\/pubs\/chowdhury2016SANER-http2.pdf","DOI":"10.1109\/SANER.2016.77"},{"key":"9629_CR19","unstructured":"Christina B (2013) Your smartphone gains a mind of its own. Conde Nast http:\/\/www.wired.com\/2013\/07\/ai-apps-trend\/"},{"key":"9629_CR20","unstructured":"Dex2jar download - Sourceforge.net (2018) http:\/\/sourceforge.net\/projects\/dex2jar\/ . (Last accessed: May 2018)"},{"key":"9629_CR21","doi-asserted-by":"crossref","unstructured":"Di Nucci D, Palomba F, Prota A, Panichella A, Zaidman A, De Lucia A (2017) Software-based energy profiling of android apps: simple, efficient and reliable?. In: 2017 IEEE 24th international conference on Software analysis, evolution and reengineering (SANER). IEEE, pp 103\u2013114","DOI":"10.1109\/SANER.2017.7884613"},{"key":"9629_CR22","unstructured":"Frank E (2016) Class j48 http:\/\/weka.sourceforge.net\/doc.dev\/weka\/classifiers\/trees\/J48.html"},{"key":"9629_CR23","unstructured":"Good O (2015) How google translate squeezes deep learning onto a phone. Google Research Blog https:\/\/research.googleblog.com\/2015\/07\/how-google-translate-squeezes-deep.html"},{"key":"9629_CR24","unstructured":"Google (2016) Find time for your goals with google calendar. Google Blog https:\/\/googleblog.blogspot.ca\/2016\/04\/find-time-goals-google-calendarhtml"},{"key":"9629_CR25","unstructured":"Google (2017) Neural networks api: Android developers https:\/\/developer.android.com\/ndk\/guides\/neuralnetworks\/index.html (Last accessed: May 2018)"},{"key":"9629_CR26","unstructured":"Google (2018) Mobile vision https:\/\/developers.google.com\/vision\/ (Last accessed: May 2018)"},{"key":"9629_CR27","unstructured":"Greene T (2017) Google brings on-device machine learning to mobile with tensorflow lite https:\/\/thenextweb.com\/artificial-intelligence\/2017\/11\/15\/google-brings-on-device-machine-learning-to-mobile-with-tensorflow-lite\/ (Retrieved January 2018)"},{"key":"9629_CR28","doi-asserted-by":"publisher","unstructured":"Gui J, Mcilroy S, Nagappan M, Halfond WGJ (2015) Truth in advertising: The hidden cost of mobile ads for software developers. In: 37Th IEEE\/ACM international conference on software engineering, ICSE 2015, florence, Italy. IEEE, vol 1, pp 100\u2013110. https:\/\/doi.org\/10.1109\/ICSE.2015.32","DOI":"10.1109\/ICSE.2015.32"},{"key":"9629_CR29","doi-asserted-by":"crossref","unstructured":"Gui J, Li D, Wan M, Halfond WG (2016) Lightweight measurement and estimation of mobile ad energy consumption. In: 2016 IEEE\/ACM 5th international workshop on Green and sustainable software (GREENS). IEEE, pp 1\u20137","DOI":"10.1145\/2896967.2896970"},{"issue":"1","key":"9629_CR30","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1145\/1656274.1656278","volume":"11","author":"M Hall","year":"2009","unstructured":"Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The weka data mining software: An update. SIGKDD Explor Newsl 11 (1):10\u201318. https:\/\/doi.org\/10.1145\/1656274.1656278","journal-title":"SIGKDD Explor Newsl"},{"key":"9629_CR31","doi-asserted-by":"publisher","unstructured":"Hasan S, King Z, Hafiz M, Sayagh M, Adams B, Hindle A (2016) Energy profiles of java collections classes. In: International Conference on Software Engineering (ICSE 2016), pp 225\u2013236. https:\/\/doi.org\/10.1145\/2884781.2884869 http:\/\/softwareprocess.ca\/pubs\/hasan2016ICSE-Energy-Profiles-of-Java-Collections-Classes.pdf","DOI":"10.1145\/2884781.2884869"},{"key":"9629_CR32","unstructured":"Hern Alex, A (2015) Smartphone now most popular way to browse internet \u2013 ofcom report. https:\/\/www.theguardian.com\/technology\/2015\/aug\/06\/smartphones-most-popular-way-to-browse-internet-ofcom\/ (last accessed: May 2018)"},{"issue":"6","key":"9629_CR33","doi-asserted-by":"publisher","first-page":"1125","DOI":"10.1007\/s10664-012-9209-9","volume":"18","author":"A Hindle","year":"2013","unstructured":"Hindle A, Ernst NA, Godfrey MW, Mylopoulos J (2013) Automated topic naming supporting cross-project analysis of software maintenance activities. J Empir Softw Eng 18(6):1125\u20131155. http:\/\/softwareprocess.ca\/pubs\/hindle2011EMSE-automated-topic-naming.pdf","journal-title":"J Empir Softw Eng"},{"key":"9629_CR34","doi-asserted-by":"crossref","unstructured":"Hindle A, Wilson A, Rasmussen K, Barlow EJ, Campbell J, Romansky S (2014) Greenminer: a hardware based mining software repositories software energy consumption framework. In: International Working Conference on Mining Software Repositories (MSR 2014), pp 12\u201321. http:\/\/softwareprocess.ca\/pubs\/hindle2014MSR-greenminer.pdf","DOI":"10.1145\/2597073.2597097"},{"key":"9629_CR35","unstructured":"Inc. A (2017) Core ml: Apple developer documentation https:\/\/developer.apple.com\/documentation\/coreml (Last accessed: May 2018)"},{"key":"9629_CR36","unstructured":"Inc. W (2018) Wit.ai: Natural language for developers https:\/\/wit.ai\/ (Last accessed: May 2018)"},{"key":"9629_CR37","doi-asserted-by":"crossref","unstructured":"Platt J (1998) Fast training of support vector machines using sequential minimal optimization. In: Schoelkopf B, Burges C, Smola A (eds) Advances in Kernel Methods - Support Vector Learning. http:\/\/research.microsoft.com\/~jplatt\/smo.html . MIT Press","DOI":"10.7551\/mitpress\/1130.003.0016"},{"key":"9629_CR38","unstructured":"Jabbarvand R, Sadeghi A, Garcia J, Malek S, Ammann P (2015) Ecodroid: an approach for energy-based ranking of android apps. In: Proceedings of the Fourth International Workshop on Green and Sustainable Software. IEEE Press, pp 8\u201314"},{"key":"9629_CR39","unstructured":"John GH, Langley P (1995) Estimating continuous distributions in bayesian classifiers. In: Eleventh conference on uncertainty in artificial intelligence. Morgan kaufmann, pp 338\u2013345"},{"key":"9629_CR40","unstructured":"Konradsson T (2015) Art and dalvik performance compared. Master\u2019s thesis, UmeUniversity. http:\/\/www8.cs.umu.se\/education\/examina\/Rapporter\/TobiasKonradsson.pdf"},{"key":"9629_CR41","unstructured":"LeCun Y, Cortes C, Burges CJ (1998) The mnist database of handwritten digits. http:\/\/yann.lecun.com\/exdb\/mnist\/"},{"key":"9629_CR42","doi-asserted-by":"publisher","unstructured":"Li D, Hao S, Gui J, Halfond WGJ (2014) An empirical study of the energy consumption of android applications. In: 30Th IEEE international conference on software maintenance and evolution, victoria, BC. IEEE Computer Society, pp 121\u2013130. https:\/\/doi.org\/10.1109\/ICSME.2014.34","DOI":"10.1109\/ICSME.2014.34"},{"key":"9629_CR43","doi-asserted-by":"publisher","unstructured":"Li D, Lyu Y, Gui J, Halfond WGJ (2016) Automated energy optimization of http requests for mobile applications. In: Proceedings of the 38th International Conference on Software Engineering, ICSE \u201916. ACM, New York, pp 249\u2013260. https:\/\/doi.org\/10.1145\/2884781.2884867","DOI":"10.1145\/2884781.2884867"},{"key":"9629_CR44","doi-asserted-by":"publisher","unstructured":"Linares-V\u00e1squez M, Bavota G, Bernal-C\u00e1rdenas C, Oliveto R, Di Penta M, Poshyvanyk D (2014) Mining energy-greedy api usage patterns in android apps: an empirical study. In: Proceedings of the 11th Working Conference on Mining Software Repositories, MSR 2014. ACM, New York, pp 2\u201311. https:\/\/doi.org\/10.1145\/2597073.2597085","DOI":"10.1145\/2597073.2597085"},{"key":"9629_CR45","doi-asserted-by":"publisher","unstructured":"Linares-V\u00e1squez M, Bavota G, C\u00e1rdenas CEB, Oliveto R, Di Penta M, Poshyvanyk D (2015) Optimizing energy consumption of guis in android apps: a multi-objective approach. In: Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering. ACM, New York, pp 143\u2013154. https:\/\/doi.org\/10.1145\/2786805.2786847","DOI":"10.1145\/2786805.2786847"},{"key":"9629_CR46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jss.2017.04.018","volume":"130","author":"M Linares-V\u00e1squez","year":"2017","unstructured":"Linares-V\u00e1squez M, Vendome C, Tufano M, Poshyvanyk D (2017) How developers micro-optimize android apps. J Syst Softw 130:1\u201323","journal-title":"J Syst Softw"},{"key":"9629_CR47","unstructured":"Lichman M (2013) UCI machine learning repository. http:\/\/archive.ics.uci.edu\/ml"},{"key":"9629_CR48","unstructured":"Machine Learning Laboratory (2015) Mnist arff files. http:\/\/axon.cs.byu.edu\/data\/mnist\/"},{"key":"9629_CR49","doi-asserted-by":"crossref","unstructured":"Malik H, Zhao P, Godfrey M (2015) Going green: An exploratory analysis of energy-related questions. In: Proceedings of the 12th Working Conference on Mining Software Repositories, MSR \u201915. IEEE Press, Piscataway, pp 418\u2013421. http:\/\/dl.acm.org\/citation.cfm?id=2820518.2820576","DOI":"10.1109\/MSR.2015.53"},{"key":"9629_CR50","doi-asserted-by":"publisher","unstructured":"Manotas I, Pollock L, Clause J (2014) Seeds: a software engineer\u2019s energy-optimization decision support framework. In: Proceedings of the 36th International Conference on Software Engineering, ICSE 2014. ACM, New York, pp 503\u2013514. https:\/\/doi.org\/10.1145\/2568225.2568297","DOI":"10.1145\/2568225.2568297"},{"key":"9629_CR51","doi-asserted-by":"publisher","unstructured":"Manotas I, Bird C, Zhang R, Shepherd D, Jaspan C, Sadowski C, Pollock L, Clause J (2016) An empirical study of practitioners\u2019 perspectives on green software engineering. In: Proceedings of the 38th International Conference on Software Engineering, ICSE \u201916. ACM, New York, pp 237\u2013248. https:\/\/doi.org\/10.1145\/2884781.2884810","DOI":"10.1145\/2884781.2884810"},{"key":"9629_CR52","unstructured":"Matyunina J (2017) How do i apply machine learning in an android app? https:\/\/www.quora.com\/How-do-I-apply-machine-learning-in-an-android-app (Last accessed: May 2018)"},{"key":"9629_CR53","unstructured":"Minka TP (2007) A comparison of numerical optimizers for logistic regression. Unpublished paper available at http:\/\/research.microsoft.com\/en-us\/um\/people\/minka\/papers\/logreg\/minka-logreg.pdf"},{"key":"9629_CR54","unstructured":"Mizutani E, Dreyfus SE (2001) On complexity analysis of supervised mlp-learning for algorithmic comparisons. In: Neural networks. IEEE, vol 1, pp 347\u2013352"},{"key":"9629_CR55","unstructured":"OpenCV Team (2018) Android - opencv library. https:\/\/opencv.org\/platforms\/android\/ (Last accessed: May 2018)"},{"key":"9629_CR56","unstructured":"Padraig C, Sarah JD (2007) k-nearest neighbour classifiers. Technical Report UCD-CSI-2007-4, University College Dublin. https:\/\/csiweb.ucd.ie\/files\/UCD-CSI-2007-4.pdf"},{"key":"9629_CR57","doi-asserted-by":"crossref","unstructured":"Pang C, Hindle A, Adams B, Hassan AE (2015) What do programmers know about the energy consumption of software? IEEE Software, pp 83\u201389. http:\/\/softwareprocess.ca\/pubs\/pang2015IEEESoftware.pdf","DOI":"10.1109\/MS.2015.83"},{"key":"9629_CR58","doi-asserted-by":"crossref","unstructured":"Pathak A, Hu YC, Zhang M (2011a) Bootstrapping energy debugging on smartphones: a first look at energy bugs in mobile devices. In: Proceedings of the 10th ACM Workshop on Hot Topics in Networks, HotNets-X, pp 5:1\u20135:6","DOI":"10.1145\/2070562.2070567"},{"key":"9629_CR59","doi-asserted-by":"publisher","unstructured":"Pathak A, Hu YC, Zhang M, Bahl P, Wang YM (2011b) Fine-grained Power Modeling for Smartphones Using System Call Tracing. In: Eurosys \u201911. Salzburg, pp 153\u2013168. https:\/\/doi.org\/10.1145\/1966445.1966460","DOI":"10.1145\/1966445.1966460"},{"key":"9629_CR60","doi-asserted-by":"crossref","unstructured":"Pereira R, Couto M, Saraiva Ja, Cunha J, Fernandes J a P (2016) The influence of the java collection framework on overall energy consumption. In: Proceedings of the 5th International Workshop on Green and Sustainable Software, GREENS \u201916, pp 15\u201321","DOI":"10.1145\/2896967.2896968"},{"key":"9629_CR61","doi-asserted-by":"publisher","unstructured":"Pinto G, Castor F, Liu YD (2014) Mining Questions About Software Energy Consumption. In: MSR 2014, pp 22\u201331. https:\/\/doi.org\/10.1145\/2597073.2597110","DOI":"10.1145\/2597073.2597110"},{"key":"9629_CR62","doi-asserted-by":"crossref","unstructured":"Rasmussen K, Wilson A, Hindle A (2014) Green mining: energy consumption of advertisement blocking methods. In: Proceedings of the 3rd International Workshop on Green and Sustainable Software (GREENS 2014), pp 38\u201345. http:\/\/softwareprocess.ca\/pubs\/rasmussen2014GREENS-adblock.pdf","DOI":"10.1145\/2593743.2593749"},{"key":"9629_CR63","unstructured":"Release v2.1-20171001-lanchon . dexpatcher\/dex2jar . github (2018) https:\/\/github.com\/DexPatcher\/dex2jar\/releases\/tag\/v2.1-20171001-lanchon . (Last accessed: May 2018)"},{"key":"9629_CR64","doi-asserted-by":"publisher","unstructured":"Saborido R, Beltrame G, Khomh F, Alba E, Antoniol G (2016) Optimizing user experience in choosing android applications. In: 2016 IEEE 23Rd international conference on software analysis, evolution, and reengineering (SANER), vol 1, pp 438\u2013448. https:\/\/doi.org\/10.1109\/SANER.2016.64","DOI":"10.1109\/SANER.2016.64"},{"key":"9629_CR65","doi-asserted-by":"crossref","unstructured":"Sahin C, Tornquist P, Mckenna R, Pearson Z, Clause J (2014) How does code obfuscation impact energy usage?. In: ICSME. IEEE Computer Society, pp 131\u2013140","DOI":"10.1109\/ICSME.2014.35"},{"key":"9629_CR66","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1016\/j.jss.2016.03.031","volume":"117","author":"C Sahin","year":"2016","unstructured":"Sahin C, Pollock L, Clause J (2016a) From benchmarks to real apps: Exploring the energy impacts of performance-directed changes. J Syst Softw 117:307\u2013316","journal-title":"J Syst Softw"},{"issue":"7","key":"9629_CR67","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1002\/smr.1762","volume":"28","author":"C Sahin","year":"2016","unstructured":"Sahin C, Wan M, Tornquist P, McKenna R, Pearson Z, Halfond WG, Clause J (2016b) How does code obfuscation impact energy usage? J Softw Evol Process 28(7):565\u2013588","journal-title":"J Softw Evol Process"},{"key":"9629_CR68","unstructured":"Selby JWA (2011) Unconventional applications of compiler analysis. Ph.D. thesis, University of Waterloo"},{"key":"9629_CR69","unstructured":"Sevarac Z, Goloskokovic I, Tait J, Carter-Greaves L, Morgan A, Steinhauer V (2016) Neuroph: Java neural network framework. http:\/\/neuroph.sourceforge.net\/"},{"issue":"3","key":"9629_CR70","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1162\/089976601300014493","volume":"13","author":"SS Keerthi","year":"2001","unstructured":"Keerthi SS, Shevade SK, Bhattacharyya C, Murthy KRK (2001) Improvements to platt\u2019s smo algorithm for svm classifier design. Neural Comput 13(3):637\u2013649","journal-title":"Neural Comput"},{"key":"9629_CR71","unstructured":"Su J, Zhang H (2006) A fast decision tree learning algorithm. In: American association for artificial intelligence, vol 6, pp 500\u2013505"},{"key":"9629_CR72","unstructured":"TensorFlow (2016) Mobile tensorflow. https:\/\/www.tensorflow.org\/mobile.html"},{"key":"9629_CR73","unstructured":"Tomita TM, Maggioni M, Vogelstein JT (2015) Randomer forests. arXiv: 1506.03410"},{"key":"9629_CR74","unstructured":"Triposo (2016) Triposo. https:\/\/www.triposo.com\/"},{"key":"9629_CR75","doi-asserted-by":"crossref","unstructured":"Wan M, Jin Y, Li D, Gui J, Mahajan S, Halfond WG (2017) Detecting display energy hotspots in android apps. Software Testing Verification and Reliability 27(6). https:\/\/onlinelibrary.wiley.com\/doi\/abs\/10.1002\/stvr.1635","DOI":"10.1002\/stvr.1635"},{"key":"9629_CR76","unstructured":"Webservices A (2018) Amazon aws machine learning https:\/\/aws.amazon.com\/machine-learning\/ (Last accessed: May 2018)"},{"key":"9629_CR77","unstructured":"Weotta (2016) About weotta. http:\/\/www.weotta.com\/about"},{"key":"9629_CR78","volume-title":"Data Mining: Practical machine learning tools and techniques","author":"IH Witten","year":"2011","unstructured":"Witten IH, Frank E (2011) Data Mining: Practical machine learning tools and techniques, 3rd edn. Morgan Kaufmann, Burlington","edition":"3rd edn."},{"key":"9629_CR79","unstructured":"Woollaston V (2015) Customers really want better battery life. http:\/\/www.dailymail.co.uk\/sciencetech\/article-2715860\/Mobile-phone-customers-really-want-better-battery-life-waterproof-screens-poll-reveals.html (last accessed: May 2018)"},{"key":"9629_CR80","doi-asserted-by":"crossref","unstructured":"Yang Y, Zhang J, Kisiel B (2003) A scalability analysis of classifiers in text categorization. In: Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval. ACM, pp 96\u2013103","DOI":"10.1145\/860435.860455"}],"container-title":["Empirical Software Engineering"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10664-018-9629-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10664-018-9629-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10664-018-9629-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,2]],"date-time":"2023-09-02T23:38:47Z","timestamp":1693697927000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10664-018-9629-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,6,4]]},"references-count":80,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2019,4]]}},"alternative-id":["9629"],"URL":"https:\/\/doi.org\/10.1007\/s10664-018-9629-2","relation":{},"ISSN":["1382-3256","1573-7616"],"issn-type":[{"value":"1382-3256","type":"print"},{"value":"1573-7616","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,6,4]]},"assertion":[{"value":"4 June 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}