Relevant bibliographies by topics / Stackoverflow

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Stackoverflow'

Author: Grafiati
Published: 4 June 2021
Last updated: 18 June 2025

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Journal articles on the topic "Stackoverflow"

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Joorabchi, Arash, Michael English, and Abdulhussain E. Mahdi. "Text mining stackoverflow." Journal of Enterprise Information Management 29, no. 2 (2016): 255–75. http://dx.doi.org/10.1108/jeim-11-2014-0109.

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Purpose – The use of social media and in particular community Question Answering (Q & A) websites by learners has increased significantly in recent years. The vast amounts of data posted on these sites provide an opportunity to investigate the topics under discussion and those receiving most attention. The purpose of this paper is to automatically analyse the content of a popular computer programming Q & A website, StackOverflow (SO), determine the exact topics of posted Q & As, and narrow down their categories to help determine subject difficulties of learners. By doing so, the authors have been able to rank identified topics and categories according to their frequencies, and therefore, mark the most asked about subjects and, hence, identify the most difficult and challenging topics commonly faced by learners of computer programming and software development. Design/methodology/approach – In this work the authors have adopted a heuristic research approach combined with a text mining approach to investigate the topics and categories of Q & A posts on the SO website. Almost 186,000 Q & A posts were analysed and their categories refined using Wikipedia as a crowd-sourced classification system. After identifying and counting the occurrence frequency of all the topics and categories, their semantic relationships were established. This data were then presented as a rich graph which could be visualized using graph visualization software such as Gephi. Findings – Reported results and corresponding discussion has given an indication that the insight gained from the process can be further refined and potentially used by instructors, teachers, and educators to pay more attention to and focus on the commonly occurring topics/subjects when designing their course material, delivery, and teaching methods. Research limitations/implications – The proposed approach limits the scope of the analysis to a subset of Q & As which contain one or more links to Wikipedia. Therefore, developing more sophisticated text mining methods capable of analysing a larger portion of available data would improve the accuracy and generalizability of the results. Originality/value – The application of text mining and data analytics technologies in education has created a new interdisciplinary field of research between the education and information sciences, called Educational Data Mining (EDM). The work presented in this paper falls under this field of research; and it is an early attempt at investigating the practical applications of text mining technologies in the area of computer science (CS) education.
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Xiong, Yunxiang, Zhangyuan Meng, Beijun Shen, and Wei Yin. "Developer Identity Linkage and Behavior Mining Across GitHub and StackOverflow." International Journal of Software Engineering and Knowledge Engineering 27, no. 09n10 (2017): 1409–25. http://dx.doi.org/10.1142/s0218194017400034.

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Nowadays, software developers are increasingly involved in GitHub and StackOverflow, creating a lot of valuable data in the two communities. Researchers mine the information in these software communities to understand developer behaviors, while previous works mainly focus on mining data within a single community. In this paper, we propose a novel approach to developer identity linkage and behavior mining across GitHub and StackOverflow. This approach links the accounts from two communities using a CART decision tree, leveraging the features from usernames, user behaviors and writing styles. Then, it explores cross-site developer behaviors through [Formula: see text]-graph analysis, LDA-based topics clustering and cross-site tagging. We conducted several experiments to evaluate this approach. The results show that the precision and [Formula: see text]-score of our identity linkage method are higher than previous methods in software communities. Especially, we discovered that (1) active issue committers are also active question askers; (2) for most developers, the topics of their contents in GitHub are similar to those of those questions and answers in StackOverflow; (3) developers’ concerns in StackOverflow shift over the time of their current participating projects in GitHub; (4) developers’ concerns in GitHub are more relevant to their answers than questions and comments in StackOverflow.
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Putra, Bagus Geriansyah, and Naim Rochmawati. "Klasifikasi Berdasarkan Question dalam Stack Overflow Menggunakan Algoritma Naïve Bayes." Journal of Informatics and Computer Science (JINACS) 2, no. 04 (2021): 259–67. http://dx.doi.org/10.26740/jinacs.v2n04.p259-267.

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Abstrak—Stackoverflow merupakan sebuah website yang menyediakan banyak informasi tentang pemrograman. Pengguna dapat berinteraksi dengan pengguna lainnya dalam sebuah forum diskusi yang diajukan. Pengguna dapat mengajukan sebuah pertanyaan yang kemudian akan ditanggapi oleh pengguna lain. Ketika mengajukan sebuah pertanyaan, pengguna harus memasukkan kategori yang tepat pada pertanyaan yang diajukan agar mendapatkan respons atau jawaban yang sesuai. Berdasarkan beberapa kasus yang terjadi masih banyak pengguna website mengalami kebingungan ketika memilih kategori pertanyaan yang diajukan. Akibatnya, pertanyaan yang diajukan tidak mendapat respons yang tepat atau kurang sesuai. Sehingga, penelitian ini diajukan untuk membantu proses pengkategorian pertanyaan pada website Stackoverflow. Penelitian menggunakan Algoritma Naïve Bayes untuk memprediksi kategori pertanyaan yang diajukan. Pada penelitian ini dilakukan beberapa proses, dimulai dengan proses input dataset dilanjutkan dengan pembacaan file dataset. Kemudian dataset akan melalui preprocessing yang dilanjutkan dengan pembobotan dan proses ekstraksi fitur dengan Algoritma TF-IDF. Selanjutnya, data diproses menggunakan Algoritma Naïve Bayes yang akan menghasilkan kategori pertanyaan. Selanjutnya dilakukan proses evaluasi model untuk menentukan model terbaik yang akan digunakan untuk tampilan antarmuka aplikasi. Hasil yang didapat dari tahap evaluasi model dengan 4 kali percobaan menggunakan 10.000-40.000 data menghasilkan nilai akurasi, precision, recall, dan f1-score tertinggi sebesar 75%, 75%, 75% dan 74%. Dari hasil pengujian yang telah dilakukan Algoritma Naïve Bayes dapat digunakan sebagai klasifikasi text dan menghasilkan nilai yang cukup baik.
 Kata Kunci— text mining, Algoritma Naïve Bayes, stackoverflow, Algoritma TF-IDF
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Singh, Prabhnoor, Rajkanwar Chopra, Ojasvi Sharma, and Rekha Singla. "Stackoverflow tag prediction using tag associations and code analysis." Journal of Discrete Mathematical Sciences and Cryptography 23, no. 1 (2020): 35–43. http://dx.doi.org/10.1080/09720529.2020.1721857.

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Pagano, Dennis, and Walid Maalej. "How Do Developers Blog?" ACM SIGSOFT Software Engineering Notes 46, no. 3 (2021): 26–29. http://dx.doi.org/10.1145/3468744.3468753.

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A decade ago, the rise of GitHub and StackOverflow as social version control and knowledge sharing environments was about to start. Social media like Twitter were mocked by some software engineering researchers and practitioners as "tools for kids not professionals". At that time, we published one of the first papers [12] on social media in software engineering at MSR 2011, the Mining Software Repositories Conference.
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Sankha Subhra Paul, R. R., and Ashish Tripathi. "Social Influence and learning pattern analysis: Case studies in Stackoverflow." International Journal of IT-based Social Welfare Promotion and Management 2, no. 1 (2015): 1–10. http://dx.doi.org/10.21742/ijswpm.2015.2.1.01.

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Dargahi Nobari, Arash, Mahmood Neshati, and Sajad Sotudeh Gharebagh. "Quality-aware skill translation models for expert finding on StackOverflow." Information Systems 87 (January 2020): 101413. http://dx.doi.org/10.1016/j.is.2019.07.003.

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Huang, Weizhi, Wenkai Mo, Beijun Shen, Yu Yang, and Ning Li. "Automatically Modeling Developer Programming Ability and Interest Across Software Communities." International Journal of Software Engineering and Knowledge Engineering 26, no. 09n10 (2016): 1493–510. http://dx.doi.org/10.1142/s0218194016400143.

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Developer profile plays an important role in software project planning, developer recommendation, personnel training, and other tasks. Modeling the ability and interest of developers is its key issue. However, most existing approaches require manual assessment, like 360[Formula: see text] performance evaluation. With the emergence of social networking sites such as StackOverflow and Github, a vast amount of developer information is created on a daily basis. Such personal and social context data has huge potential to support automatic and effective developer ability evaluation and interest mining. In this paper, we propose CPDScorer, a novel approach for modeling and scoring the programming ability and interest of developers through mining heterogeneous information from both community question answering (CQA) sites and open-source software (OSS) communities. CPDScorer analyzes the questions and answers posted in CQA sites, and evaluates the projects submitted in OSS communities to assign expertise scores as well as interest scores to developers, considering both the quantitative and qualitative factors. When profiling developer's ability and interest, a programming term extraction algorithm is also designed based on set covering. We have conducted experiments on StackOverflow and Github to measure the effectiveness of CPDScorer. The results show that our approach is feasible and practical in user programming ability and interest modeling. In particular, the precision of our approach reaches 80%.
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Abdalkareem, Rabe, Emad Shihab, and Juergen Rilling. "On code reuse from StackOverflow: An exploratory study on Android apps." Information and Software Technology 88 (August 2017): 148–58. http://dx.doi.org/10.1016/j.infsof.2017.04.005.

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Shejwalkar, Virat, Arun Ganesh, Rajiv Mathews, et al. "Recycling Scraps: Improving Private Learning by Leveraging Checkpoints]{Recycling Scraps: Improving Private Learning by Leveraging Checkpoints." Proceedings on Privacy Enhancing Technologies 2025, no. 2 (2025): 607–28. https://doi.org/10.56553/popets-2025-0079.

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DP training pipelines for modern neural networks are iterative and generate multiple checkpoints. However, all except the final checkpoint are discarded after training. In this work, we propose novel methods to utilize intermediate checkpoints to improve prediction accuracy and estimate uncertainty in DP predictions. First, we design a general framework that uses aggregates of intermediate checkpoints during training to increase the accuracy of DP ML techniques. Specifically, we demonstrate that training over aggregates can provide significant gains in prediction accuracy over the existing state-of-the-art for StackOverflow, CIFAR10 and CIFAR100 datasets. For instance, we improve the state-of-the-art DP StackOverflow accuracies to 22.74% (+2.06% relative) for epsilon=8.2, and 23.90% (+2.09%) for epsilon=18.9. Furthermore, these gains magnify in settings with periodically varying training data distributions. We also demonstrate that our methods achieve relative improvements of 0.54% and 62.6% in terms of utility and variance, on a proprietary, production-grade pCVR task. Lastly, we initiate an exploration into estimating the uncertainty (variance) that DP noise adds in the predictions of DP ML models. We prove that, under standard assumptions on the loss function, the sample variance from last few checkpoints provides a good approximation of the variance of the final model of a DP run. Empirically, we show that the last few checkpoints can provide a reasonable lower bound for the variance of a converged DP model. Crucially, all the methods proposed in this paper operate on a single training run of the DP ML technique, thus incurring no additional privacy cost.
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Dissertations / Theses on the topic "Stackoverflow"

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Fernandes, Teresa do Carmo Barr?to. "ExMinerSOF: minerando informa??es excepcionais do Stackoverflow." PROGRAMA DE P?S-GRADUA??O EM SISTEMAS E COMPUTA??O, 2017. https://repositorio.ufrn.br/jspui/handle/123456789/24202.

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Submitted by Automa??o e Estat?stica (sst@bczm.ufrn.br) on 2017-11-01T21:17:48Z No. of bitstreams: 1 TeresaDoCarmoBarretoFernandes_DISSERT.pdf: 5261298 bytes, checksum: 1a7e32ec8483e6e7e31101df7f8675f9 (MD5)<br>Approved for entry into archive by Arlan Eloi Leite Silva (eloihistoriador@yahoo.com.br) on 2017-11-07T21:08:03Z (GMT) No. of bitstreams: 1 TeresaDoCarmoBarretoFernandes_DISSERT.pdf: 5261298 bytes, checksum: 1a7e32ec8483e6e7e31101df7f8675f9 (MD5)<br>Made available in DSpace on 2017-11-07T21:08:03Z (GMT). No. of bitstreams: 1 TeresaDoCarmoBarretoFernandes_DISSERT.pdf: 5261298 bytes, checksum: 1a7e32ec8483e6e7e31101df7f8675f9 (MD5) Previous issue date: 2017-06-30<br>Exce??es n?o capturadas (do ingl?s: uncaught) n?o s?o cen?rios excepcionais nas aplica??es Java atuais. Eles s?o, na verdade, uma das principais causas de falha das aplica??es Java - que podem originar-se de erros de programa??o (e.g., acesso a refer?ncias nulas); falhas no hardware ou em APIs utilizadas. Essas exce??es uncaught resultam em stack traces que s?o frequentemente usados pelos desenvolvedores como fonte de informa??es para a depura??o. Atualmente, essa informa??o ? frequentemente usada pelos desenvolvedores em mecanismos de busca ou sites de perguntas e respostas (do ingl?s: Question and Answer - Q&A) para tentar compreender melhor a causa do crash e assim poder resolv?lo. Este estudo fez a minera??o de stack traces inclu?das nas perguntas e respostas do StackOverflow (SOF). O objetivo deste estudo foi: (i) identificar caracter?sticas das stack traces mineradas do SOF e (ii) investigar como tais informa??es podem ser usadas para evitar exce??es uncaught durante o desenvolvimento de software. Neste estudo, 121.253 stack traces foram extra?das e analisadas em combina??o com inspe??es de postagens do SOF. Tamb?m ? proposta a ferramenta ExMinerSOF, que alerta o desenvolvedor sobre as exce??es que podem ser potencialmente sinalizadas por um m?todo de API. Essas informa??es s?o descobertas aplicando uma estrat?gia de minera??o apresentada neste trabalho. Ao faz?-lo, a ferramenta permite que o desenvolvedor evite falhas com base em falhas relatadas por outros desenvolvedores.<br>Uncaught exceptions are not an exceptional sce- nario in current Java applications. They are actually one of the main causes of applications crashes, which can originate from programming errors on the application itself (null pointer dereferences); faults in underlying hardware or re-used APIs. Such uncaught exceptions result in exception stack traces that are often used by developers as a source of information for debugging. Currently, this information is ofttimes used by developers on search engines or Question and Answer sites while the developer tries to: better understand the cause of the crash and solve it. This study mined the exception stack traces embedded on StackOverflow (SOF) questions and answers. The goal of this work was to two-fold: to identify characteristics of stack traces mined from SOF and to investigate how such information can be used to prevent uncaught exceptions during software development. Overall 121.253 exception stack traces were extracted and analyzed in combination with Q&A inspections. Hence, this study proposes ExMinerSOF tool, which alerts the developer about the exceptions that can be potentially signaled by an API method but are not part of the API documentation - and was discovered by applying a mining strategy in SOF repository. Doing so, the tool enable the developer to prevent faults based on failures reported by the crowd.
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Klock, Robert. "Quality of SQL Code Security on StackOverflow and Methods of Prevention." Oberlin College Honors Theses / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=oberlin1625831198110328.

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Jadhav, Sanket Prabhakar. "SOCIAL NETWORK FOR SOFTWARE DEVELOPERS." CSUSB ScholarWorks, 2018. https://scholarworks.lib.csusb.edu/etd/782.

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This project is the design and implementation of a web-based message board for software developers. The purpose of “Social Network for Software Developers” is to connect inexperienced software developers with experienced software developers.
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Visaggi, Salvatore. "Multimodal Side-Tuning for Code Snippets Programming Language Recognition." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22993/.

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Identificare in modo automatico il linguaggio di programmazione di una porzione di codice sorgente è uno dei temi che ancora oggi presenta diverse difficoltà. Il numero di linguaggi di programmazione, la quantità di codice pubblicato e reso open source, e il numero di sviluppatori che producono e pubblicano nuovo codice sorgente è in continuo aumento. Le motivazioni che richiedono la necessità di disporre di strumenti in grado di riconoscere il tipo di linguaggio per snippet di codice sorgente sono svariate. Ad esempio, tali strumenti trovano applicazione in ambiati quali: la ricerca di codice sorgente; la ricerca di possibili vulnerabilità nel codice; la syntax highlighting; o semplicemente per comprendere il contenuto di progetti software. Nasce così l'esigenza di disporre di dataset di snippet di codice allineati in modo adeguato con il linguaggio di programmazione. StackOverflow, una piattaforma di condivisione di conoscenza tra sviluppatori, offre la possibilità di avere accesso a centinaia di migliaia di snippet di codice sorgente scritti nei linguaggi più usati dagli sviluppatori, rendendolo il luogo ideale da cui estrarre snippet per la risoluzione del task proposto. Nel lavoro svolto si è dedicata molta attenzione a tale problematica, iterando sull'approccio scelto al fine di ottenere una metodologia che ha permesso l'estrazione di un dataset adeguato. Al fine di risolvere il task dell'identificazione del linguaggio per gli snippet estratti da StackOverflow, nel lavoro svolto si fa uso di un approccio multimodale (considerando rappresentazioni testuali e di immagini degli snippet), prendendo in esame la tecnica innovativa di side-tuning (basata sull'adattamento incrementale di una rete neurale pre-addestrata). I risultati ottenuti sono confrontabili con lo stato dell'arte e in alcuni casi migliori, in considerazione della difficoltà del task affrontato nel caso di snippet di codice sorgente che presentano poche linee di codice.
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"Exploring Generic Features for Online Large-Scale Discussion Forum Comments." Master's thesis, 2016. http://hdl.handle.net/2286/R.I.38694.

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abstract: Online discussion forums have become an integral part of education and are large repositories of valuable information. They facilitate exploratory learning by allowing users to review and respond to the work of others and approach learning in diverse ways. This research investigates the different comment semantic features and the effect they have on the quality of a post in a large-scale discussion forum. We survey the relevant literature and employ the key content quality identification features. We then construct comment semantics features and build several regression models to explore the value of comment semantics dynamics. The results reconfirm the usefulness of several essential quality predictors, including time, reputation, length, and editorship. We also found that comment semantics are valuable to shape the answer quality. Specifically, the diversity of comments significantly contributes to the answer quality. In addition, when searching for good quality answers, it is important to look for global semantics dynamics (diversity), rather than observe local differences (disputable content). Finally, the presence of comments shepherd the community to revise the posts by attracting attentions to the posts and eventually facilitate the editing process.<br>Dissertation/Thesis<br>Masters Thesis Computer Science 2016
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Book chapters on the topic "Stackoverflow"

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de Azevedo, Renato Preigschadt, Pedro Rangel Henriques, and Maria João Varanda Pereira. "Extending PythonQA with Knowledge from StackOverflow." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77703-0_56.

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Pöial, Jaanus. "Challenges of Teaching Programming in StackOverflow Era." In Educating Engineers for Future Industrial Revolutions. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68198-2_65.

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Gruetze, Toni, Ralf Krestel, and Felix Naumann. "Topic Shifts in StackOverflow: Ask it Like Socrates." In Natural Language Processing and Information Systems. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41754-7_18.

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Yang, Jie, Ke Tao, Alessandro Bozzon, and Geert-Jan Houben. "Sparrows and Owls: Characterisation of Expert Behaviour in StackOverflow." In User Modeling, Adaptation, and Personalization. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08786-3_23.

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Mandal, Nibir Chandra, Tashreef Muhammad, and G. M. Shahariar. "Can Transformer Models Effectively Detect Software Aspects in StackOverflow Discussion?" In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-34622-4_18.

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Paul, Sankha Subhra, Ashish Tripathi, and R. R. Tewari. "Social Influence and Learning Pattern Analysis: Case Studies in Stackoverflow." In Advances in Computer and Computational Sciences. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3773-3_12.

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Sedhain, Abim, Sruti Srinivasa Ragavan, Brett McKinney, Shahnewaz Leon, and Sandeep Kaur Kuttal. "Unveiling Value-Cost Dynamics in StackOverflow with IFT-Enhanced Clustering." In Communications in Computer and Information Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-94156-6_36.

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Mandal, Nibir Chandra, G. M. Shahariar, and Md Tanvir Rouf Shawon. "Effectiveness of Transformer Models on IoT Security Detection in StackOverflow Discussions." In Proceedings of International Conference on Information and Communication Technology for Development. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7528-8_10.

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Tereszkowski-Kaminski, Michal, Santanu Kumar Dash, and Guillermo Suarez-Tangil. "A Study of Malicious Source Code Reuse Among GitHub, StackOverflow and Underground Forums." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70896-1_3.

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Kavaler, David, Daryl Posnett, Clint Gibler, Hao Chen, Premkumar Devanbu, and Vladimir Filkov. "Using and Asking: APIs Used in the Android Market and Asked about in StackOverflow." In Lecture Notes in Computer Science. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03260-3_35.

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Conference papers on the topic "Stackoverflow"

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Oliveira, André, Paulo Matos, and Pedro Filipe Oliveira. "Sahub - Stackoverflow and Comments Integrations." In 2024 International Conference on Engineering and Emerging Technologies (ICEET). IEEE, 2024. https://doi.org/10.1109/iceet65156.2024.10913764.

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Chen, Xinwei, Kun Li, Tianyou Song, and Jiangjian Guo. "Few-Shot Name Entity Recognition on StackOverflow." In 2024 9th International Conference on Intelligent Computing and Signal Processing (ICSP). IEEE, 2024. http://dx.doi.org/10.1109/icsp62122.2024.10743392.

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Vasaniya, Raj, Meet Visodiya, and Anand K. Patel. "TechAssist: A RAG-Based Chatbot for Accessing Technical Information from StackOverflow." In 2025 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS). IEEE, 2025. https://doi.org/10.1109/sceecs64059.2025.10940184.

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Khan, Shafaq, Seyede Sanaz Jedari Jafari, Neha Anand, Xinyu Wang, and Yugapriya Shankar. "STACKOVERFLOW DATAWAREHOUSE SYSTEM." In ICISE 2023: 2023 8th International Conference on Information Systems Engineering. ACM, 2023. http://dx.doi.org/10.1145/3641032.3641057.

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Pereira, Igor Muzetti. "Avaliando a Geração de Buzz de Issues em Comunidades de Softwares de Código-Aberto." In Congresso Latino-Americano de Software Livre e Tecnologias Abertas. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/latinoware.2020.18601.

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O desenvolvimento de sistemas open-source constrói implicitamente uma comunidade que contribui, reporta problemas e compartilha dúvidas entre os seus colaboradores. Dessa forma, diversas plataformas auxiliam essas comunidades, tais como o Github e o StackOverflow, responsáveis por hospedar código-fonte, gerir problemas relacionadas a bugs, fazer controle de versão e solucionar dúvidas de usuários. Entretanto, pouco se sabe sobre a relação que existe entre ambas as comunidades, especificamente o buzz gerado entre elas a partir de problemas encontrados no código-fonte. Dessa forma, neste trabalho estuda-se o buzz gerado no StackOverflow a partir de issues reportadas no Github, bem como o impacto dessas perguntas dentro da comunidade do StackOverflow. Para tanto, foram analisados oito sistemas JavaScript, divididos em dois grupos: populares e não populares, e suas issues reportadas em um mês. Como resultado, observou-se a pequena interação entre ambas as comunidades, assim como um baixo impacto dos temas discutidos no StackOverflow relacionados aos problemas reportados no Github.
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Liu, Xuliang, and Hao Zhong. "Mining stackoverflow for program repair." In 2018 IEEE 25th International Conference on Software Analysis, Evolution and Reengineering (SANER). IEEE, 2018. http://dx.doi.org/10.1109/saner.2018.8330202.

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Rahman, Musfiqur, Peter Rigby, Dharani Palani, and Tien Nguyen. "Cleaning StackOverflow for Machine Translation." In 2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR). IEEE, 2019. http://dx.doi.org/10.1109/msr.2019.00021.

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Slag, Rogier, Mike de Waard, and Alberto Bacchelli. "One-Day Flies on StackOverflow - Why the Vast Majority of StackOverflow Users Only Posts Once." In 2015 IEEE/ACM 12th Working Conference on Mining Software Repositories (MSR). IEEE, 2015. http://dx.doi.org/10.1109/msr.2015.63.

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Zhang, Yu, Yunyi Zhang, Yucheng Jiang, et al. "Entity Set Co-Expansion in StackOverflow." In 2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022. http://dx.doi.org/10.1109/bigdata55660.2022.10020770.

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Bazelli, Blerina, Abram Hindle, and Eleni Stroulia. "On the Personality Traits of StackOverflow Users." In 2013 IEEE International Conference on Software Maintenance (ICSM). IEEE, 2013. http://dx.doi.org/10.1109/icsm.2013.72.

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Reports on the topic "Stackoverflow"

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Osadcha, Kateryna P., and Viacheslav V. Osadchyi. The use of cloud computing technology in professional training of future programmers. [б. в.], 2021. http://dx.doi.org/10.31812/123456789/4435.

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Abstract:
The article provides a brief analysis of the current state of the study of cloud technologies by future software engineers at foreign and Ukrainian universities. The author experience in the application of cloud technologies in the training of future software engineers in Ukraine is presented. The application of cloud business automation systems, online services to monitor the implementation of the software projects, Google services for collaboration, planning and productivity while studying professional disciplines and carrying out diploma projects is described. Based on the survey conducted at Stackoverflow, the state of application of cloud technologies by software engineers around the world has been analyzed. The cloud technologies that are not studied at the analyzed universities of Ukraine and those that are not popular with software developers in the world, but studied at Ukrainian universities by future software engineers are outlined. Conclusions are made on the modernization of training programs for future software engineers. Topics for the study of cloud technologies by future software engineers in the content of professional disciplines are proposed.
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