Stack Overflow Assistant Chatbot Using NLP Techniques

Authors

  • Arun Karthik S  B.Tech in Electronics and Communication Engineering, School of Electronics Engineering, Vellore Institute of Technology (VIT), Vellore, India
  • Shiv Sanjaybhai Makadia  Assistant Professor (Senior), School of Electronics Engineering, Vellore Institute of Technology (VIT), Vellore, India
  • Jasmine Batra  
  • Sankar Ganesh S  

DOI:

https://doi.org/10.32628/CSEIT228557

Keywords:

Stack Overflow, Chatbot, Natural Language Processing, Telegram

Abstract

Searching on the Stack Overflow website, a platform for the programming community that features questions and answers on an extensive range of computer programming topics, can be arduous, laborious, and time-consuming at times. Aimed to address this issue, this paper proposes a conversational chatbot to assist with Stack Overflow search. The dialogue chatbot will answer questions related to programming and simulate dialogue and chit-chat on all non-programming-related questions, thus helping users find answers to programming questions present on the Stack Overflow website and also holding conversations with them. Using an access token, the bot is integrated with the Telegram messenger application that serves as a medium for a user to ask questions and for the bot to respond to them. This integration enables us to talk to the bot in Telegram. Results show that the selected algorithms are in accordance with the implementation of the chatbot approach with good test accuracies.

References

  1. A. Ricci and A. Santi, “From actors to agent-oriented programming abstractions in simpal,” Proceedings of the 3rd annual conference on Systems, programming, and applications: software for humanity - SPLASH ’12, 2012.
  2. A. Mondal, M. Dey, D. Das, S. Nagpal, and K. Garda, “Chatbot: An automated conversation system for the educational domain,” 2018 International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP), 2018.
  3. P. N. Robillard, “The role of knowledge in software development,” Communications of the ACM, vol. 42, no. 1, pp. 87–92, 1999.
  4. S. M. Nasehi, J. Sillito, F. Maurer, and C. Burns, “What makes a good code example?: A study of programming Q&A in stackoverflow,” 2012 28th IEEE International Conference on Software Maintenance (ICSM), 2012.
  5. R. Winkler and M. Soellner, “Unleashing the potential of Chatbots in Education: A state-of-the-art analysis,” Academy of Management Proceedings, vol. 2018, no. 1, p. 15903, 2018.
  6. M. Akhtar, J. Neidhardt, and H. Werthner, “The potential of Chatbots: Analysis of chatbot conversations,” 2019 IEEE 21st Conference on Business Informatics (CBI), 2019.
  7. S. Hussain, O. Ameri Sianaki, and N. Ababneh, “A survey on conversational agents/Chatbots classification and Design Techniques,” Advances in Intelligent Systems and Computing, pp. 946–956, 2019.
  8. D. Braun, A. Hernandez-Mendez, F. Matthes, and M. Langen, “Evaluating natural language understanding services for conversational question answering systems,” Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue, 2017.
  9. N. N. Khin and K. M. Soe, “Question answering based university chatbot using sequence to sequence model,” 2020 23rd Conference of the Oriental COCOSDA International Committee for the Co-ordination and Standardization of Speech Databases and Assessment Techniques (O-COCOSDA), 2020.
  10. B. R. Ranoliya, N. Raghuwanshi, and S. Singh, “Chatbot for university related faqs,” 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2017.
  11. B. Setiaji and F. W. Wibowo, “Chatbot using a knowledge in database: Human-to-machine conversation modeling,” 2016 7th International Conference on Intelligent Systems, Modelling and Simulation (ISMS), 2016.
  12. M. Shen and R. Huang, “A personal conversation assistant based on Seq2seq with word2vec cognitive map,” 2018 7th International Congress on Advanced Applied Informatics (IIAI-AAI), 2018.
  13. M. Y. Helmi Setyawan, R. M. Awangga, and S. R. Efendi, “Comparison of multinomial naive Bayes algorithm and logistic regression for intent classification in Chatbot,” 2018 International Conference on Applied Engineering (ICAE), 2018.
  14. Y. Wang, S. Liu, N. Afzal, M. Rastegar-Mojarad, L. Wang, F. Shen, P. Kingsbury, and H. Liu, “A comparison of word embeddings for the Biomedical Natural Language Processing,” Journal of Biomedical Informatics, vol. 87, pp. 12–20, 2018.
  15. H. Purohit, G. Dong, V. Shalin, K. Thirunarayan, and A. Sheth, “Intent classification of short-text on social media,” 2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity), 2015.
  16. S. Sonam, A. Verma, S. Lal, and N. Sardana, “TagStack: Automated system for predicting tags in stackoverflow,” 2019 International Conference on Signal Processing and Communication (ICSC), 2019.
  17. L. T. Hien, L. Tran Thi Ly, C. Pham-Nguyen, T. Le Dinh, H. Tiet Gia, and L. N. Hoai Nam, “Towards chatbot-based interactive what- and how-question answering systems: The ADOBOT approach,” 2020 RIVF International Conference on Computing and Communication Technologies (RIVF), 2020.
  18. T. Lalwani, S. Bhalotia, A. Pal, S. Bisen, and V. Rathod, “Implementation of a chat bot system using AI and NLP,” International Journal of Innovative Research in Computer Science & Technology, vol. 6, no. 3, pp. 26–30, 2018.
  19. M. T. Mutiwokuziva, M. W. Chanda, P. Kadebu, A. Mukwazvure, and T. T. Gotora, “A neural-network based chat bot,” 2017 2nd International Conference on Communication and Electronics Systems (ICCES), 2017.
  20. J. Torres, C. Vaca, L. Terán, and C. L. Abad, “Seq2Seq models for recommending short text conversations,” Expert Systems with Applications, vol. 150, p. 113270, 2020.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2022-11-30

Issue

Volume 8, Issue 6, November-December-2022

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Arun Karthik S, Shiv Sanjaybhai Makadia, Jasmine Batra, Sankar Ganesh S, " Stack Overflow Assistant Chatbot Using NLP Techniques" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 6, pp.01-11, November-December-2022. Available at doi : https://doi.org/10.32628/CSEIT228557