Classifying Stack Overflow Questions Quality using SVM

Atiku, Muhammad Bello; Ismail, Naayif; Alhadji, Mohamadou

dc.contributor.author	Atiku, Muhammad Bello
dc.contributor.author	Ismail, Naayif
dc.contributor.author	Alhadji, Mohamadou
dc.date.accessioned	2023-01-27T09:55:29Z
dc.date.available	2023-01-27T09:55:29Z
dc.date.issued	2022-05-30
dc.identifier.citation	Asaduzzaman, M. Mashiyat, A. S., Roy, C. K., and Schneider, K. A. (2013). Answering questions about unanswered questions of stack overflow. In Proceedings of the 10th Working Conference on Mining Software Repositories, MSR ’13, pages 97–100, Piscataway, NJ, USA. IEEE Press. www.researchgate.net/ublication/333574762/ Towards an Accurate Prediction of the Question Quality on SO using a Deep Learning-Based (NPL Approach). Goodspeed, E. (2015). A diagram showing a perceptron updating its linear boundary as more training examples are added. https://en.wikipedia.org/wiki/Perceptron/. Duijn, M., Kuˇcera, A., and Bacchelli, A. (2015). Quality questions need quality code: Classifying code fragments on stack overflow. In Proceedings of the 12th Working Conference on Mining Software Repositories, MSR ’15, pages 410–413, Piscataway, NJ, USA. IEEE Press. Luhn, H. P. (1957). A statistical approach to mechanized encoding and searching of literary information. IBM Journal of Research and Development, 1(4):309–317. L. Ponzanelli, A. Mocci, A. Bacchelli, M. Lanza, and D. Fullerton,” Improving Low Quality Stack Overflow Post Detection,” in University of Lugano, 2014. [Online]. Available: http://www.inf.usi.ch/phd/ponzanelli/profile/publications/2014e/Ponz2 014e.pdf. Baltadzhieva, A. and Chrupała, G. (2015). Predicting the Quality of Questions on Stack Overflow. In Proc. of the International Conference Recent Advances in Natural Language Processing. Barua, A., Thomas, S. W., and Hassan, A. E. (2014). What are developers talking about? An analysis of topics and trends in Stack Overflow. Empirical Software Engineering. Correa, D. and Sureka, A. (2013). Fit or unfit: Analysis and prediction of ’closed questions’ on stack overflow. In Proc. of the First ACM Conference on Online Social Networks. Saini, T. and Tripathi, S. (2018). Predicting tags for stack overflow questions using different classifiers. In 2018 4th International Conference on Recent Advances in Information Technology, pages 1–5. Schuster, S., Zhu, W., and Cheng, Y. (2017). Predicting Tags for Stack Overflow Questions. In Proc. of the LWDA 2017 Workshops: KDML, FGWM, IR, and FGDB. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15:1929–1958. Sutskever, I., Martens, J., Dahl, G., and Hinton, G. (2013). On the importance of initialization and momentum in deep learning. In Dasgupta, S. and Mc Allester, D., editors, Proceedings of the 30th International Conference on Machine Learning, volume 28 of Proceedings of Machine Learning Research, pages 1139–1147, Atlanta, Georgia, USA.	en_US
dc.identifier.uri	http://hdl.handle.net/123456789/1672
dc.description	Supervised by Md Jubair Ibna Mostafa, Lecturer, Department of Computer Science and Engineering (CSE), Islamic University of Technology (IUT), Board Bazar, Gazipur-1704. Bangladesh. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.	en_US
dc.description.abstract	This thesis aims to discover indicators of quality, and to use this knowledge to correctly classify the quality of questions and answers from Stack Overflow. The proliferation of technical questions and answers on Q&A websites such as Stack Overflow means there is more information available than ever. However, the ease of publishing such information also tends to mean the quality varies significantly. The job of moderating Stack Overflow is left to the community. Stack Overflow performs some basic quality analysis, but this is an area where improvement would have many benefits to not only Stack Overflow, but many other domains where the quality of text is important.	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh	en_US
dc.subject	SVM, NLP, TF, IDF, SO, RBF, HTML, XML, Q&A	en_US
dc.title	Classifying Stack Overflow Questions Quality using SVM	en_US
dc.type	Thesis	en_US