{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T04:27:36Z","timestamp":1777523256977,"version":"3.51.4"},"reference-count":91,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,6,13]],"date-time":"2024-06-13T00:00:00Z","timestamp":1718236800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,6,13]],"date-time":"2024-06-13T00:00:00Z","timestamp":1718236800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100000038","name":"NSERC","doi-asserted-by":"crossref","award":["RGPIN-2021-02575"],"award-info":[{"award-number":["RGPIN-2021-02575"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Empir Software Eng"],"published-print":{"date-parts":[[2024,7]]},"DOI":"10.1007\/s10664-024-10450-y","type":"journal-article","created":{"date-parts":[[2024,6,13]],"date-time":"2024-06-13T03:39:08Z","timestamp":1718249948000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["How far are we with automated machine learning? characterization and challenges of AutoML toolkits"],"prefix":"10.1007","volume":"29","author":[{"given":"Md Abdullah","family":"Al Alamin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1376-095X","authenticated-orcid":false,"given":"Gias","family":"Uddin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,6,13]]},"reference":[{"key":"10450_CR1","doi-asserted-by":"crossref","unstructured":"Abdellatif A, Costa D, Badran K, Abdalkareem R, Shihab E (2020) Challenges in chatbot development: A study of stack overflow posts. In: Proceedings of the 17th international conference on mining software repositories, MSR \u201920, New York, NY, USA, 2020. Association for Computing Machinery pp 174-185","DOI":"10.1145\/3379597.3387472"},{"key":"10450_CR2","doi-asserted-by":"crossref","unstructured":"Agrapetidou A, Charonyktakis P, Gogas P, Papadimitriou T, Tsamardinos I (2021) An automl application to forecasting bank failures. Appl Econ Lett 28(1):5\u20139","DOI":"10.1080\/13504851.2020.1725230"},{"key":"10450_CR3","doi-asserted-by":"crossref","unstructured":"Ahmed S, Bagherzadeh M (2018) What do concurrency developers ask about? a large-scale study using stack overflow. In: Proceedings of the 12th ACM\/IEEE International symposium on empirical software engineering and measurement, ESEM \u201918, New York, NY, USA . Association for Computing Machinery","DOI":"10.1145\/3239235.3239524"},{"key":"10450_CR4","doi-asserted-by":"crossref","unstructured":"Alamin MAA, Malakar S, Uddin G, Afroz S, Haider TB, Iqbal A (2021) An empirical study of developer discussions on low-code software development challenges. In 2021 IEEE\/ACM 18th International conference on mining software repositories (MSR), IEEE pp 46\u201357","DOI":"10.1109\/MSR52588.2021.00018"},{"key":"10450_CR5","doi-asserted-by":"crossref","unstructured":"Alamin MAA, Uddin G, Malakar S, Afroz S, Haider TB, Iqbal A (2022) Developer discussion topics on the adoption and barriers of low code software development platforms. Empirical Software Engineering","DOI":"10.1007\/s10664-022-10244-0"},{"key":"10450_CR6","doi-asserted-by":"crossref","unstructured":"Alshangiti M, Sapkota H, Murukannaiah PK, Liu X, Yu Q (2019) Why is developing machine learning applications challenging? a study on stack overflow posts. In: 2019 ACM\/IEEE international symposium on empirical software engineering and measurement (ESEM), IEEE. pp 1\u201311","DOI":"10.1109\/ESEM.2019.8870187"},{"key":"10450_CR7","unstructured":"Amazon Lex - Conversational AI and Chatbots (2022) Available: https:\/\/aws.amazon.com\/lex\/. [Online; accessed 5-Nov-2022]"},{"key":"10450_CR8","unstructured":"Amazon SageMaker Overview (2022) Available: https:\/\/aws.amazon.com\/sagemaker\/ . [Online; accessed 5-Nov-2022]"},{"key":"10450_CR9","doi-asserted-by":"publisher","unstructured":"Arpteg A, Brinne B, Crnkovic-Friis L, Bosch J (2018) Software engineering challenges of deep learning. In: 2018 44th Euromicro conference on software engineering and advanced applications (SEAA). Prague, pp 50\u201359, https:\/\/doi.org\/10.1109\/SEAA.2018.00018","DOI":"10.1109\/SEAA.2018.00018"},{"key":"10450_CR10","doi-asserted-by":"crossref","unstructured":"Arun R, Suresh V, Madhavan CV, Murthy MN (2010) On finding the natural number of topics with latent dirichlet allocation: Some observations. In: Pacific-Asia conference on knowledge discovery and data mining, Springer. pp 391\u2013402","DOI":"10.1007\/978-3-642-13657-3_43"},{"key":"10450_CR11","doi-asserted-by":"crossref","unstructured":"Asuncion HU, Asuncion AU, Taylor RN (2010) Software traceability with topic modeling. In: 2010 ACM\/IEEE 32nd international conference on software engineering, vol 1, pp 95\u2013104. IEEE","DOI":"10.1145\/1806799.1806817"},{"key":"10450_CR12","unstructured":"AutoFolio Automated Algorithm Selection with Hyperparameter Optimization Library (2022) Available: https:\/\/github.com\/automl\/AutoFolio . [Online; accessed 5-Nov-2022]"},{"key":"10450_CR13","unstructured":"Automated Machine Learning Market Size & Share Analysis - Growth Trends & Forecasts (2024 - 2029). Available: https:\/\/www.mordorintelligence.com\/industry-reports\/automated-machine-learning-market. [Online; accessed 14 Jan 2024]"},{"key":"10450_CR14","unstructured":"AWS Announces Nine New Amazon SageMaker Capabilities (2022) Available: https:\/\/www.businesswire.com\/news\/home\/20201208005335\/en\/AWS-Announces-Nine-New-Amazon-SageMaker-Capabilities . [Online; accessed 5-Nov-2022]"},{"key":"10450_CR15","unstructured":"Azure Machine Learning - ML as a Service (2022) Available: https:\/\/azure.microsoft.com\/en-us\/services\/machine-learning\/ . [Online; accessed 5-Nov-2022]"},{"key":"10450_CR16","doi-asserted-by":"crossref","unstructured":"Bagherzadeh M, Khatchadourian R (2019) Going big: A large-scale study on what big data developers ask. In Proceedings of the 2019 27th ACM joint meeting on European software engineering conference and symposium on the foundations of software engineering, ESEC\/FSE 2019, New York, NY, USA, ACM pp 432\u2013442","DOI":"10.1145\/3338906.3338939"},{"key":"10450_CR17","unstructured":"Bahrampour S, Ramakrishnan N, Schott L, Shah M (2018) Comparative study of deep learning software frameworks. arXiv preprint arXiv:1511.06435"},{"key":"10450_CR18","doi-asserted-by":"crossref","unstructured":"Bajaj K, Pattabiraman K, Mesbah A (2014) Mining questions asked by web developers. In: Proceedings of the 11th working conference on mining software repositories, pp 112\u2013121","DOI":"10.1145\/2597073.2597083"},{"key":"10450_CR19","doi-asserted-by":"crossref","unstructured":"Bangash AA, Sahar H, Chowdhury S, Wong AW, Hindle A, Ali K (2019) What do developers know about machine learning: a study of ml discussions on stackoverflow. In: 2019 IEEE\/ACM 16th international conference on mining software repositories (MSR), IEEE. pp 260\u2013264","DOI":"10.1109\/MSR.2019.00052"},{"issue":"3","key":"10450_CR20","doi-asserted-by":"publisher","first-page":"619","DOI":"10.1007\/s10664-012-9231-y","volume":"19","author":"A Barua","year":"2014","unstructured":"Barua A, Thomas SW, Hassan AE (2014) What are developers talking about? an analysis of topics and trends in stack overflow. Empir Softw Eng 19(3):619\u2013654","journal-title":"Empir Softw Eng"},{"issue":"7","key":"10450_CR21","doi-asserted-by":"publisher","first-page":"671","DOI":"10.1109\/TSE.2013.60","volume":"40","author":"G Bavota","year":"2014","unstructured":"Bavota G, Oliveto R, Gethers M, Poshyvanyk D, Lucia AD (2014) Methodbook: Recommending move method refactorings via relational topic models. IEEE Trans Software Eng 40(7):671\u2013694","journal-title":"IEEE Trans Software Eng"},{"key":"10450_CR22","doi-asserted-by":"publisher","unstructured":"Amershi S, et\u00a0al. (2019) Software engineering for machine learning: A case study. In: 2019 IEEE\/ACM 41st international conference on software engineering: software engineering in practice (ICSE-SEIP). Montreal, pp 291\u2013300, https:\/\/doi.org\/10.1109\/ICSE-SEIP.2019.00042","DOI":"10.1109\/ICSE-SEIP.2019.00042"},{"issue":"4\u20135","key":"10450_CR23","first-page":"993","volume":"3","author":"DM Blei","year":"2003","unstructured":"Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3(4\u20135):993\u20131022","journal-title":"J Mach Learn Res"},{"key":"10450_CR24","unstructured":"Bubeck S, Chandrasekaran V, Eldan R, Gehrke J, Horvitz E, Kamar E, Lee P, Lee YT, Li Y, Lundberg S, Nori H, Palangi H, Ribeiro MT, Zhang Y (2023) Sparks of artificial general intelligence: Early experiments with gpt-4"},{"key":"10450_CR25","unstructured":"Chen T-H, Thomas SW, Nagappan M, Hassan AE (2012) Explaining software defects using topic models. In: 9th working conference on mining software repositories, pp 189\u2013198"},{"issue":"5","key":"10450_CR26","doi-asserted-by":"publisher","first-page":"1843","DOI":"10.1007\/s10664-015-9402-8","volume":"21","author":"T-HP Chen","year":"2016","unstructured":"Chen T-HP, Thomas SW, Hassan AE (2016) A survey on the use of topic models when mining software repositories. Empir Softw Eng 21(5):1843\u20131919","journal-title":"Empir Softw Eng"},{"key":"10450_CR27","doi-asserted-by":"crossref","unstructured":"Chen Z, Cao Y, Liu Y, Wang H, Xie T, Liu X (2020) A comprehensive study on challenges in deploying deep learning based software. In: Proceedings of the 28th ACM joint meeting on European software engineering conference and symposium on the foundations of software engineering, pp 750\u2013762","DOI":"10.1145\/3368089.3409759"},{"key":"10450_CR28","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1007\/s10664-008-9095-3","volume":"14","author":"B Cleary","year":"2009","unstructured":"Cleary B, Exton C, Buckley J, English M (2009) An empirical analysis of information retrieval based concept location techniques in software comprehension. Empir Softw Eng 14:93\u2013130","journal-title":"Empir Softw Eng"},{"key":"10450_CR29","unstructured":"Cloud AutoML Custom Machine Learning Models (2022) Available: https:\/\/cloud.google.com\/automl . [Online; accessed 5-Nov-2022]"},{"key":"10450_CR30","doi-asserted-by":"crossref","unstructured":"Cummaudo A, Vasa R, Barnett S, Grundy J, Abdelrazek M (2020) Interpreting cloud computer vision pain-points: A mining study of stack overflow. In: 2020 IEEE\/ACM 42nd international conference on software engineering (ICSE), IEEE. pp 1584\u20131596","DOI":"10.1145\/3377811.3380404"},{"key":"10450_CR31","unstructured":"Custom models with ml kit (2023) Available: https:\/\/developers.google.com\/ml-kit\/custom-models. [Online; accessed 6-Nov-2023]"},{"issue":"2","key":"10450_CR32","first-page":"1301","volume":"5","author":"K Das","year":"2017","unstructured":"Das K, Behera RN (2017) A survey on machine learning: concept, algorithms and applications. Int J Innov Res Comput Commun Eng 5(2):1301\u20131309","journal-title":"Int J Innov Res Comput Commun Eng"},{"key":"10450_CR33","doi-asserted-by":"crossref","unstructured":"Drozdal J, Weisz J, Wang D, Dass G, Yao B, Zhao C, Muller M, Ju L, Su H (2020) Trust in automl: exploring information needs for establishing trust in automated machine learning systems. In: 25th International conference on intelligent user interfaces (IUI), pp 297\u2013307","DOI":"10.1145\/3377325.3377501"},{"issue":"1","key":"10450_CR34","first-page":"1997","volume":"20","author":"T Elsken","year":"2019","unstructured":"Elsken T, Metzen JH, Hutter F (2019) Neural architecture search: A survey. The J Mach Learn Res 20(1):1997\u20132017","journal-title":"The J Mach Learn Res"},{"key":"10450_CR35","unstructured":"Exchange S (2020) Stack exchange data dump . Available: https:\/\/archive.org\/details\/stackexchange. [Online; accessed 5-Nov-2022]"},{"issue":"3","key":"10450_CR36","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1111\/j.1468-0394.2005.00299.x","volume":"22","author":"S Fincher","year":"2005","unstructured":"Fincher S, Tenenberg J (2005) Making sense of card sorting data. Expert Syst 22(3):89\u201393","journal-title":"Expert Syst"},{"key":"10450_CR37","unstructured":"G2 overview (2022) Available: https:\/\/www.g2.com\/. [Online; accessed 5-Nov-2022]"},{"key":"10450_CR38","unstructured":"Garner overview (2020) Available: https:\/\/www.gartner.com. [Online; accessed 5-Nov-2022]"},{"key":"10450_CR39","unstructured":"H2O (2022) ai: AI Cloud Platform. Available: https:\/\/h2o.ai\/. [Online; accessed 5-Nov-2022]"},{"issue":"4","key":"10450_CR40","doi-asserted-by":"publisher","first-page":"2694","DOI":"10.1007\/s10664-020-09819-6","volume":"25","author":"J Han","year":"2020","unstructured":"Han J, Shihab E, Wan Z, Deng S, Xia X (2020) What do programmers discuss about deep learning frameworks. Empir Softw Eng 25(4):2694\u20132747","journal-title":"Empir Softw Eng"},{"key":"10450_CR41","doi-asserted-by":"crossref","unstructured":"Haque MU, Iwaya LH, Babar MA (2020) Challenges in docker development: A large-scale study using stack overflow. In Proceedings of the 14th ACM\/IEEE international symposium on empirical software engineering and measurement (ESEM), pp 1\u201311","DOI":"10.1145\/3382494.3410693"},{"key":"10450_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106622","volume":"212","author":"X He","year":"2021","unstructured":"He X, Zhao K, Chu X (2021) Automl: A survey of the state-of-the-art. Knowl-Based Syst 212:106622","journal-title":"Knowl-Based Syst"},{"key":"10450_CR43","doi-asserted-by":"crossref","unstructured":"Humbatova N, Jahangirova G, Bavota G, Riccio V, Stocco A, Tonella P (2020) Taxonomy of real faults in deep learning systems. In Proceedings of the ACM\/IEEE 42nd international conference on software engineering, pp 1110\u20131121","DOI":"10.1145\/3377811.3380395"},{"key":"10450_CR44","doi-asserted-by":"crossref","unstructured":"Hu J, Sun X, Lo D, Li B (2015) Modeling the evolution of development topics using dynamic topic models. In IEEE 22nd international conference on software analysis, evolution, and reengineering, pp 3\u201312","DOI":"10.1109\/SANER.2015.7081810"},{"key":"10450_CR45","unstructured":"Hutter F, Kotthoff L, Vanschoren J (2019) AutoML: Methods, systems, challenges. Springer series on challenges in machine learning"},{"key":"10450_CR46","doi-asserted-by":"crossref","unstructured":"Hu S, Xie S, Zheng H, Liu C, Shi J, Liu X, Lin D (2020) Dsnas: Direct neural architecture search without parameter retraining. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 12084\u201312092","DOI":"10.1109\/CVPR42600.2020.01210"},{"key":"10450_CR47","unstructured":"Islam MJ, Nguyen HA, Pan R, Rajan H (2019) What do developers ask about ml libraries? a large-scale study using stack overflow. arXiv preprint arXiv:1906.11940"},{"key":"10450_CR48","unstructured":"Jiang H, et\u00a0al. (2018) To trust or not to trust a classifier. In: Proc NeurIPS, pp 5546\u20135557"},{"issue":"8","key":"10450_CR49","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3470918","volume":"54","author":"SK Karmaker","year":"2021","unstructured":"Karmaker SK, Hassan MM, Smith MJ, Xu L, Zhai C, Veeramachaneni K (2021) Automl to date and beyond: Challenges and opportunities. ACM Computing Surveys (CSUR) 54(8):1\u201336","journal-title":"ACM Computing Surveys (CSUR)"},{"issue":"1","key":"10450_CR50","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1093\/biomet\/30.1-2.81","volume":"30","author":"MG Kendall","year":"1938","unstructured":"Kendall MG (1938) A new measure of rank correlation. Biometrika 30(1):81\u201393","journal-title":"Biometrika"},{"issue":"279","key":"10450_CR51","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1080\/01621459.1957.10501395","volume":"52","author":"WH Kruskal","year":"1957","unstructured":"Kruskal WH (1957) Historical notes on the wilcoxon unpaired two-sample test. J Am Stat Assoc 52(279):356\u2013360","journal-title":"J Am Stat Assoc"},{"key":"10450_CR52","unstructured":"Lee DJ-L, Macke S (2020) A human-in-the-loop perspective on automl: Milestones and the road ahead. IEEE Data Engineering Bulletin"},{"key":"10450_CR53","doi-asserted-by":"publisher","first-page":"2655","DOI":"10.1007\/s10664-018-9595-8","volume":"23","author":"H Li","year":"2018","unstructured":"Li H, Chen T-HP, Shang W, Hassan AE (2018) Studying software logging using topic models. Empir Softw Eng 23:2655\u20132694","journal-title":"Empir Softw Eng"},{"key":"10450_CR54","doi-asserted-by":"crossref","unstructured":"Linares-V\u00e1squez M, Dit B, Poshyvanyk D (2013) An exploratory analysis of mobile development issues using stack overflow. In 2013 10th working conference on mining software repositories (MSR), IEEE. pp 93\u201396","DOI":"10.1109\/MSR.2013.6624014"},{"key":"10450_CR55","doi-asserted-by":"crossref","unstructured":"Li Y, Shen Y, Zhang W, Zhang C, Cui B (2022) Volcanoml: speeding up end-to-end automl via scalable search space decomposition. The VLDB Journal, pp 1\u201325","DOI":"10.1007\/s00778-022-00752-2"},{"key":"10450_CR56","doi-asserted-by":"crossref","unstructured":"Li Y, Wang Z, Xie Y, Ding B, Zeng K, Zhang C (2021) Automl: From methodology to application. In: Proceedings of the 30th ACM international conference on information & knowledge management, pp 4853\u20134856","DOI":"10.1145\/3459637.3483279"},{"key":"10450_CR57","doi-asserted-by":"crossref","unstructured":"Li C, Yuan X, Lin C, Guo M, Wu W, Yan J, Ouyang W (2019) Am-lfs: Automl for loss function search. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 8410\u20138419","DOI":"10.1109\/ICCV.2019.00850"},{"key":"10450_CR58","unstructured":"Majidi F, Openja M, Khomh F, Li H (2020) An empirical study on the usage of automated machine learning tools. pp 59\u201370"},{"key":"10450_CR59","doi-asserted-by":"crossref","unstructured":"Mazzawi H, Gonzalvo X, Kracun A, Sridhar P, Subrahmanya NA, Lopez-Moreno I, Jin Park H, Violette P (2019) Improving keyword spotting and language identification via neural architecture search at scale. In Interspeech","DOI":"10.21437\/Interspeech.2019-1916"},{"key":"10450_CR60","unstructured":"McCallum AK (2002) Mallet: A machine learning for language toolkit. http:\/\/mallet. cs. umass. edu"},{"key":"10450_CR61","unstructured":"Mellor J, Turner J, Storkey A, Crowley EJ (2021) Neural architecture search without training. In: International conference on machine learning, PMLR. pp 7588\u20137598"},{"key":"10450_CR62","unstructured":"MLBox platform overview (2022) Available: https:\/\/bigml.com\/. [Online; accessed 5-Nov-2022]"},{"key":"10450_CR63","unstructured":"OpenAI (2023) Gpt-4 technical report"},{"key":"10450_CR64","unstructured":"Patel K, Fogarty J, Landay JA, Harrison BL (2008) Examining difficulties software developers encounter in the adoption of statistical machine learning. In AAAI, pp 1563\u20131566"},{"key":"10450_CR65","unstructured":"Pham H, Guan M, Zoph B, Le Q, Dean J (2018) Efficient neural architecture search via parameters sharing. In International conference on machine learning, PMLR. pp 4095\u20134104"},{"issue":"6","key":"10450_CR66","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1109\/TSE.2007.1016","volume":"33","author":"D Poshyvanyk","year":"2007","unstructured":"Poshyvanyk D, Gu\u00e9h\u00e9neuc Y-G, Marcus A, Antoniol G, Rajlich VT (2007) Feature location using probabilistic ranking of methods based on execution scenarios and information retrieval. IEEE Trans Softw Eng 33(6):420\u2013432","journal-title":"IEEE Trans Softw Eng"},{"issue":"12","key":"10450_CR67","first-page":"4536","volume":"2","author":"C Ramasubramanian","year":"2013","unstructured":"Ramasubramanian C, Ramya R (2013) Effective pre-processing activities in text mining using improved porter\u2019s stemming algorithm. Int J Adv Res Comput Commun Eng 2(12):4536\u20134538","journal-title":"Int J Adv Res Comput Commun Eng"},{"key":"10450_CR68","doi-asserted-by":"crossref","unstructured":"Rao S, Kak AC (2011) Retrieval from software libraries for bug localization: a comparative study of generic and composite text models. In 8th working conference on mining software repositories, pp 43-52","DOI":"10.1145\/1985441.1985451"},{"key":"10450_CR69","unstructured":"RapidMiner (2022) Amplify the Impact of Your People, Expertise. Available: https:\/\/rapidminer.com\/ . [Online; accessed 5-Nov-2022]"},{"key":"10450_CR70","unstructured":"Rehurek R, Sojka P (2010) Software framework for topic modelling with large corpora. In: Proceedings of the LREC 2010 workshop on new challenges for NLP frameworks. Citeseer"},{"key":"10450_CR71","doi-asserted-by":"crossref","unstructured":"R\u00f6der M, Both A, Hinneburg A (2015) Exploring the space of topic coherence measures. In Proceedings of the eighth ACM international conference on Web search and data mining, pp 399\u2013408","DOI":"10.1145\/2684822.2685324"},{"key":"10450_CR72","doi-asserted-by":"publisher","unstructured":"Roscher R, Bohn B, Duarte, MF, Garcke J (2020) Explainable machine learning for scientific insights and discoveries. IEEE Access 8:42200\u201342216. https:\/\/doi.org\/10.1109\/ACCESS.2020.2976199","DOI":"10.1109\/ACCESS.2020.2976199"},{"issue":"3","key":"10450_CR73","doi-asserted-by":"publisher","first-page":"1192","DOI":"10.1007\/s10664-015-9379-3","volume":"21","author":"C Rosen","year":"2016","unstructured":"Rosen C, Shihab E (2016) What are mobile developers asking about? a large scale study using stack overflow. Empir Softw Eng 21(3):1192\u20131223","journal-title":"Empir Softw Eng"},{"key":"10450_CR74","unstructured":"Sculley D, et\u00a0al. (2015) Hidden technical debt in machine learning systems. In: 28th International conference on neural information processing systems, vol 2, pp 2503\u20132511"},{"key":"10450_CR75","doi-asserted-by":"crossref","unstructured":"Shah V, Lacanlale J, Kumar P, Yang K, Kumar A (2021) Towards benchmarking feature type inference for automl platforms. In: Proceedings of the 2021 international conference on management of data, pp 1584\u20131596","DOI":"10.1145\/3448016.3457274"},{"key":"10450_CR76","unstructured":"Splunk (2022) The Data Platform for the Hybrid World. Available: https:\/\/www.splunk.com\/. [Online; accessed 5-Nov-2022]"},{"key":"10450_CR77","doi-asserted-by":"publisher","first-page":"671","DOI":"10.1016\/j.infsof.2015.05.003","volume":"66","author":"X Sun","year":"2015","unstructured":"Sun X, Li B, Leung H, Li B, Li Y (2015) Msr4sm: Using topic models to effectively mining software repositories for software maintenance tasks. Inf Softw Technol 66:671\u2013694","journal-title":"Inf Softw Technol"},{"key":"10450_CR78","doi-asserted-by":"crossref","unstructured":"Sun X, Li B, Li Y, Chen Y (2015) What information in software historical repositories do we need to support software maintenance tasks? an approach based on topic model. Computer and Information Science, pp 22\u201337","DOI":"10.1007\/978-3-319-10509-3_3"},{"key":"10450_CR79","doi-asserted-by":"crossref","unstructured":"Sun X, Liu X, Li B, Duan Y, Yang H, Hu J (2016) Exploring topic models in software engineering data analysis: A survey. In: 17th IEEE\/ACIS International conference on software engineering, artificial intelligence, networking and parallel\/distributed computing, pp 357\u2013362","DOI":"10.1109\/SNPD.2016.7515925"},{"key":"10450_CR80","doi-asserted-by":"crossref","unstructured":"Thomas SW, Adams B, Hassan AE, Blostein D (2011) Modeling the evolution of topics in source code histories. In: 8th working conference on mining software repositories, pp 173\u2013182","DOI":"10.1145\/1985441.1985467"},{"key":"10450_CR81","doi-asserted-by":"crossref","unstructured":"Thomas SW, Adams B, Hassan AE, Blostein D (2014) Studying software evolution using topic models. Sci Comput Program 80(B):457\u2013479","DOI":"10.1016\/j.scico.2012.08.003"},{"key":"10450_CR82","doi-asserted-by":"crossref","unstructured":"Tian K, Revelle M, Poshyvanyk D (2009) Using latent dirichlet allocation for automatic categorization of software. In: 6th international working conference on mining software repositories, pp 163\u2013166","DOI":"10.1109\/MSR.2009.5069496"},{"key":"10450_CR83","doi-asserted-by":"crossref","unstructured":"Truong A, Walters A, Goodsitt J, Hines K, Bruss CB, Farivar R (2019) Towards automated machine learning: Evaluation and comparison of automl approaches and tools. In: 2019 IEEE 31st international conference on tools with artificial intelligence (ICTAI), IEEE. pp 1471\u20131479","DOI":"10.1109\/ICTAI.2019.00209"},{"key":"10450_CR84","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1007\/s10664-021-10021-5","volume":"26","author":"G Uddin","year":"2021","unstructured":"Uddin G, Sabir F, Gu\u00e9h\u00e9neuc Y-G, Alam O, Khomh F (2021) An empirical study of iot topics in iot developer discussions on stack overflow. Empirical Softw Eng 26:11","journal-title":"Empirical Softw Eng"},{"key":"10450_CR85","doi-asserted-by":"crossref","unstructured":"Uddin G, Khomh F (2017) Automatic summarization of api reviews. In: 2017 32nd IEEE\/ACM international conference on automated software engineering (ASE), IEEE. pp 159\u2013170","DOI":"10.1109\/ASE.2017.8115629"},{"key":"10450_CR86","doi-asserted-by":"crossref","unstructured":"Wan Z, et\u00a0al. (2019) How does machine learning change software development practices? In: TSE","DOI":"10.1109\/TSE.2019.2937083"},{"key":"10450_CR87","unstructured":"Wang D, Liao QV, Zhang Y, Khurana U, Samulowitz H, Park S, Muller M, Amini L (2021) How much automation does a data scientist want?"},{"key":"10450_CR88","unstructured":"Wan Z, Xia X, Hassan AE (2019) What is discussed about blockchain? a case study on the use of balanced lda and the reference architecture of a domain to capture online discussions about blockchain platforms across the stack exchange communities. IEEE Trans Softw Eng"},{"key":"10450_CR89","doi-asserted-by":"crossref","unstructured":"Xin D, Wu EY, Lee DJ-L, Salehi N, Parameswaran A (2021) Whither automl? understanding the role of automation in machine learning workflows. In: Conference on human factors in computing systems (CHI), pp 1\u201316","DOI":"10.1145\/3411764.3445306"},{"issue":"5","key":"10450_CR90","doi-asserted-by":"publisher","first-page":"910","DOI":"10.1007\/s11390-016-1672-0","volume":"31","author":"X-L Yang","year":"2016","unstructured":"Yang X-L, Lo D, Xia X, Wan Z-Y, Sun J-L (2016) What security questions do developers ask? a large-scale study of stack overflow posts. J Comput Sci Technol 31(5):910\u2013924","journal-title":"J Comput Sci Technol"},{"key":"10450_CR91","doi-asserted-by":"crossref","unstructured":"Zhang Y, Chen Y, Cheung S-C, Xiong Y, Zhang L (2018) An empirical study on tensorflow program bugs. In: Proceedings of the 27th ACM SIGSOFT International symposium on software testing and analysis, pp 129\u2013140","DOI":"10.1145\/3213846.3213866"}],"container-title":["Empirical Software Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10664-024-10450-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10664-024-10450-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10664-024-10450-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,5]],"date-time":"2024-07-05T15:19:37Z","timestamp":1720192777000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10664-024-10450-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,13]]},"references-count":91,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,7]]}},"alternative-id":["10450"],"URL":"https:\/\/doi.org\/10.1007\/s10664-024-10450-y","relation":{},"ISSN":["1382-3256","1573-7616"],"issn-type":[{"value":"1382-3256","type":"print"},{"value":"1573-7616","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,13]]},"assertion":[{"value":"23 January 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 June 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"91"}}