skip to main content
research-article
Open access

A Weak Supervision-Based Approach to Improve Chatbots for Code Repositories

Published: 12 July 2024 Publication History

Abstract

Software chatbots are growing in popularity and have been increasingly used in software projects due to their benefits in saving time, cost, and effort. At the core of every chatbot is a Natural Language Understanding (NLU) component that enables chatbots to comprehend the users' queries. Prior work shows that chatbot practitioners face challenges in training the NLUs because the labeled training data is scarce. Consequently, practitioners resort to user queries to enhance chatbot performance. They annotate these queries and use them for NLU training. However, such training is done manually and prohibitively expensive. Therefore, we propose AlphaBot to automate the query annotation process for SE chatbots. Specifically, we leverage weak supervision to label users' queries posted to a software repository-based chatbot. To evaluate the impact of using AlphaBot on the NLU's performance, we conducted a case study using a dataset that comprises 749 queries and 52 intents. The results show that using AlphaBot improves the NLU's performance in terms of F1-score, with improvements ranging from 0.96% to 35%. Furthermore, our results show that applying more labeling functions improves the NLU's classification of users' queries. Our work enables practitioners to focus on their chatbots' core functionalities rather than annotating users' queries.

References

[1]
Ahmad Abdellatif, Khaled Badran, Diego Costa, and Emad Shihab. 2021. A Comparison of Natural Language Understanding Platforms for Chatbots in Software Engineering. IEEE Transactions on Software Engineering (TSE), 1–1. https://doi.org/10.1109/TSE.2021.3078384
[2]
Ahmad Abdellatif, Khaled Badran, and Emad Shihab. 2020. MSRBot: Using Bots to Answer Questions from Software Repositories. Empirical Software Engineering (EMSE), 25 (2020), 1834–1863. https://doi.org/10.1007/s10664-019-09788-5
[3]
Ahmad Abdellatif, Khaled Badran, and Emad Shihab. 2021. AskGit. https://askgit.io/ (Accessed on 12/07/2023)
[4]
Ahmad Abdellatif, Diego Elias Costa, Khaled Badran, Rabe Abdelkareem, and Emad Shihab. 2020. Challenges in Chatbot Development: A Study of Stack Overflow Posts. In Proceedings of the 17th International Conference on Mining Software Repositories (MSR’20). To Appear. https://doi.org/10.1145/3379597.3387472
[5]
Enrique Alfonseca, Katja Filippova, Jean-Yves Delort, and Guillermo Garrido. 2012. Pattern Learning for Relation Extraction with a Hierarchical Topic Model. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2 (ACL ’12). Association for Computational Linguistics, USA. 54–59.
[6]
Chidubem Arachie and Bert Huang. 2021. A General Framework for Adversarial Label Learning. Journal of Machine Learning Research, 22, 118 (2021), 1–33.
[7]
Julie Ask, Michael Facemire, and Andrew Hogan. 2016. The State Of Chatbots. Forrester.com report, 20 (2016).
[8]
Stephen H Bach, Bryan He, Alexander Ratner, and Christopher Ré. 2017. Learning the structure of generative models without labeled data. In International Conference on Machine Learning. 273–282.
[9]
Stephen H. Bach, Daniel Rodriguez, Yintao Liu, Chong Luo, Haidong Shao, Cassandra Xia, Souvik Sen, Alex Ratner, Braden Hancock, Houman Alborzi, Rahul Kuchhal, Chris Ré, and Rob Malkin. 2019. Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scale. In Proceedings of the 2019 International Conference on Management of Data (SIGMOD ’19). Association for Computing Machinery, New York, NY, USA. 362–375. isbn:9781450356435 https://doi.org/10.1145/3299869.3314036
[10]
Chetashri Bhadane, Hardi Dalal, and Heenal Doshi. 2015. Sentiment Analysis: Measuring Opinions. Procedia Computer Science, 45 (2015), 808–814. issn:1877-0509 https://doi.org/10.1016/j.procs.2015.03.159 International Conference on Advanced Computing Technologies and Applications (ICACTA)
[11]
Urmil Bharti, Deepali Bajaj, Hunar Batra, Shreya Lalit, Shweta Lalit, and Aayushi Gangwani. 2020. Medbot: Conversational Artificial Intelligence Powered Chatbot for Delivering Tele-Health after COVID-19. In 2020 5th International Conference on Communication and Electronics Systems (ICCES). 870–875. https://doi.org/10.1109/ICCES48766.2020.9137944
[12]
Nick C. Bradley, Thomas Fritz, and Reid Holmes. 2018. Context-Aware Conversational Developer Assistants. In Proceedings of the 40th International Conference on Software Engineering (ICSE ’18). Association for Computing Machinery, New York, NY, USA. 993–1003. isbn:9781450356381 https://doi.org/10.1145/3180155.3180238
[13]
Daniel Braun, Adrian Hernandez Mendez, Florian Matthes, and Manfred Langen. 2017. Evaluating Natural Language Understanding Services for Conversational Question Answering Systems. In Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue. Association for Computational Linguistics, Saarbrücken, Germany. 174–185. https://doi.org/10.18653/v1/W17-5522
[14]
Metz C. 2016. Google’s Hand-Fed AI Now Gives Answers, Not Just Search Results. https://www.wired.com/2016/11/googles-search-engine-can-now-answer-questions-human-help/ (Accessed on 08/02/2023)
[15]
Fabio Clarizia, Francesco Colace, Marco Lombardi, Francesco Pascale, and Domenico Santaniello. 2018. Chatbot: An Education Support System for Student. In Cyberspace Safety and Security, Arcangelo Castiglione, Florin Pop, Massimo Ficco, and Francesco Palmieri (Eds.). Springer International Publishing, Cham. 291–302. isbn:978-3-030-01689-0
[16]
Allan Peter Davis, Thomas C Wiegers, Phoebe M Roberts, Benjamin L King, Jean M Lay, Kelley Lennon-Hopkins, Daniela Sciaky, Robin Johnson, Heather Keating, and Nigel Greene. 2013. A CTD–Pfizer collaboration: manual curation of 88 000 scientific articles text mined for drug–disease and drug–phenotype interactions. Database, 2013 (2013).
[17]
Dialogflow. 2021. Dialogflow Official Website. https://dialogflow.cloud.google.com/ (Accessed on 01/08/2023)
[18]
James Dominic, Jada Houser, Igor Steinmacher, Charles Ritter, and Paige Rodeghero. 2020. Conversational Bot for Newcomers Onboarding to Open Source Projects. In Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops (ICSEW’20). Association for Computing Machinery, New York, NY, USA. 46–50. isbn:9781450379632 https://doi.org/10.1145/3387940.3391534
[19]
Farbod Farhour, Ahmad Abdellatif, Essam Mansour, and Emad Shihab. 2023. A Weak Supervision-based Approach to Improve Chatbots for Code Repositories. https://zenodo.org/records/10714394 (Accessed on 28/09/2023)
[20]
Jason A. Fries, Ethan Steinberg, Saelig Khattar, Scott L. Fleming, Jose Posada, Alison Callahan, and Nigam H. Shah. 2021. Ontology-driven weak supervision for clinical entity classification in electronic health records. Nature Communications, 12, 1 (2021), 2017. isbn:2041-1723 https://doi.org/10.1038/s41467-021-22328-4
[21]
Xin Geng and Kate Smith-Miles. 2009. Incremental Learning. Springer US, Boston, MA. 731–735. isbn:978-0-387-73003-5
[22]
Sebastian Romy Gomes, Sk Golam Saroar, Md Mosfaiul, Alam Telot, Behroz Newaz Khan, Amitabha Chakrabarty, and Moin Mostakim. 2017. A comparative approach to email classification using Naive Bayes classifier and hidden Markov model. In 2017 4th International Conference on Advances in Electrical Engineering (ICAEE). 482–487. https://doi.org/10.1109/ICAEE.2017.8255404
[23]
GoodRebels. [n. d.]. The impact of conversational bots in the customer experience. https://www.goodrebels.com/the-impact-of-conversational-bots-in-the–experience/ (Accessed on 12/07/2023)
[24]
Braden Hancock, Antoine Bordes, Pierre-Emmanuel Mazare, and Jason Weston. 2019. Learning from dialogue after deployment: Feed yourself, chatbot!. arXiv preprint arXiv:1901.05415.
[25]
Howe and Jeff. 2006. The Rise of Crowdsourcing. Wired, 14 (2006), 01.
[26]
Mia Mohammad Imran, Yashasvi Jain, Preetha Chatterjee, and Kostadin Damevski. 2023. Data Augmentation for Improving Emotion Recognition in Software Engineering Communication. In Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering (ASE ’22). Association for Computing Machinery, New York, NY, USA. Article 29, 13 pages. isbn:9781450394758 https://doi.org/10.1145/3551349.3556925
[27]
Zhao Jianqiang and Gui Xiaolin. 2017. Comparison Research on Text Pre-processing Methods on Twitter Sentiment Analysis. IEEE Access, 5 (2017), 2870–2879. https://doi.org/10.1109/ACCESS.2017.2672677
[28]
jsphdnl. 2017. nlp - Conversational Data for building a chat bot - Stack Overflow. https://stackoverflow.com/questions/45821517/conversational-data-for-building-a-chat-bot (Accessed on 08/14/2023)
[29]
Pensive Knave. 2021. reactjs - How to create CSS underline which partially covers the word? - Stack Overflow. https://stackoverflow.com/questions/67694697/how-to-create-css-underline-which-partially-covers-the-word (Accessed on 08/25/2023)
[30]
Carlene Lebeuf, Margaret-Anne Storey, and Alexey Zagalsky. 2018. Software Bots. IEEE Software, 35, 1 (2018), 18–23. https://doi.org/10.1109/MS.2017.4541027
[31]
Chun-Ting Lin, Shang-Pin Ma, and Yu-Wen Huang. 2020. MSABot: A Chatbot Framework for Assisting in the Development and Operation of Microservice-Based Systems. In Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops (ICSEW’20). Association for Computing Machinery, New York, NY, USA. 36–40. isbn:9781450379632 https://doi.org/10.1145/3387940.3391501
[32]
Neil Mallinar, Abhishek Shah, Rajendra Ugrani, Ayush Gupta, Manikandan Gurusankar, Tin Kam Ho, Q Vera Liao, Yunfeng Zhang, Rachel KE Bellamy, and Robert Yates. 2019. Bootstrapping conversational agents with weak supervision. In Proceedings of the AAAI Conference on Artificial Intelligence. 33, 9528–9533.
[33]
Gideon S. Mann and Andrew McCallum. 2010. Generalized Expectation Criteria for Semi-Supervised Learning with Weakly Labeled Data. Journal of Machine Learning Research, 11, 32 (2010), 955–984.
[34]
Yu Meng, Jiaming Shen, Chao Zhang, and Jiawei Han. 2018. Weakly-Supervised Neural Text Classification. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM ’18). Association for Computing Machinery, New York, NY, USA. 983–992. isbn:9781450360142 https://doi.org/10.1145/3269206.3271737
[35]
Microsoft. 2021. Language Understanding - Bot Service. https://docs.microsoft.com/en-us/azure/bot-service/bot-builder-concept-luis?view=azure-bot-service-4.0##best-practices-for-language-understanding (Accessed on 08/12/2023)
[36]
Microsoft. 2021. LUIS (Language Understanding) - Cognitive Services. https://www.luis.ai/ (Accessed on 12/09/2023)
[37]
Mike Mintz, Steven Bills, Rion Snow, and Dan Jurafsky. 2009. Distant Supervision for Relation Extraction without Labeled Data. In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2 (ACL ’09). Association for Computational Linguistics, USA. 1003–1011. isbn:9781932432466
[38]
Eadicicco L. Baidu’s Andrew Ng. 2017. on the future of artificial intelligence. https://time.com/4631730/andrew-ng-artificial-intelligence-2017/ (Accessed on 07/24/2023)
[39]
T. Okuda and S. Shoda. 2018. AI-based chatbot service for financial industry. Fujitsu Scientific and Technical Journal, 54 (2018), 04, 4–8.
[40]
Sergio Oramas, Massimo Quadrana, and Fabien Gouyon. 2021. Bootstrapping a Music Voice Assistant with Weak Supervision. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers. Association for Computational Linguistics, Online. 49–55. https://doi.org/10.18653/v1/2021.naacl-industry.7
[41]
Elahe Paikari, JaeEun Choi, SeonKyu Kim, Sooyoung Baek, MyeongSoo Kim, SeungEon Lee, ChaeYeon Han, YoungJae Kim, KaHye Ahn, Chan Cheong, and André van der hoek. 2019. A Chatbot for Conflict Detection and Resolution. In 2019 IEEE/ACM 1st International Workshop on Bots in Software Engineering (BotSE). 29–33. https://doi.org/10.1109/BotSE.2019.00016
[42]
Jerrod Parker and Shi Yu. 2021. Named Entity Recognition through Deep Representation Learning and Weak Supervision. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. Association for Computational Linguistics, Online. 3828–3839. https://doi.org/10.18653/v1/2021.findings-acl.335
[43]
R. Polikar, L. Upda, S.S. Upda, and V. Honavar. 2001. Learn++: an incremental learning algorithm for supervised neural networks. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 31, 4 (2001), 497–508. https://doi.org/10.1109/5326.983933
[44]
Ilham A. Qasse, Shailesh Mishra, and Mohammad Hamdaqa. 2021. iContractBot: A Chatbot for Smart Contracts’ Specification and Code Generation. In Proceedings of the IEEE/ACM 43th International Conference on Software Engineering Workshops. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1109/BotSE52550.2021.00015
[45]
Nikitha Rao, Chetan Bansal, and Joe Guan. 2021. Search4Code: Code Search Intent Classification Using Weak Supervision. In 2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR). 575–579. https://doi.org/10.1109/MSR52588.2021.00077
[46]
Rasa. [n. d.]. Duckling. https://duckling.wit.ai/ (Accessed on 08/14/2023)
[47]
Rasa. 2021. Introduction to Rasa X. https://rasa.com/docs/rasa-x/ (Accessed on 07/29/2023)
[48]
RASA. 2021. Open source conversational AI | Rasa. https://rasa.com/ (Accessed on 12/09/2023)
[49]
Alexander Ratner, Stephen H Bach, Henry Ehrenberg, Jason Fries, Sen Wu, and Christopher Ré. 2017. Snorkel: Rapid training data creation with weak supervision. In Proceedings of the VLDB Endowment. International Conference on Very Large Data Bases. 11, 269.
[50]
Alexander Ratner, Stephen H. Bach, Henry Ehrenberg, Jason Fries, Sen Wu, and Christopher Ré. 2017. Snorkel: Rapid Training Data Creation with Weak Supervision. Proc. VLDB Endow., 11, 3 (2017), Nov., 269–282. issn:2150-8097
[51]
Alexander Ratner, Stephen H Bach, Henry Ehrenberg, Jason Fries, Sen Wu, and Christopher Ré. 2020. Snorkel: Rapid training data creation with weak supervision. The VLDB Journal, 29, 2 (2020), 709–730. https://doi.org/10.1007/s00778-019-00552-1
[52]
Daniele Ravì, Charence Wong, Fani Deligianni, Melissa Berthelot, Javier Andreu-Perez, Benny Lo, and Guang-Zhong Yang. 2017. Deep Learning for Health Informatics. IEEE Journal of Biomedical and Health Informatics, 21, 1 (2017), 4–21. https://doi.org/10.1109/JBHI.2016.2636665
[53]
Georgios Rizos, Konstantin Hemker, and Björn Schuller. 2019. Augment to Prevent: Short-Text Data Augmentation in Deep Learning for Hate-Speech Classification. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM ’19). Association for Computing Machinery, New York, NY, USA. 991–1000. isbn:9781450369763 https://doi.org/10.1145/3357384.3358040
[54]
Ricardo Romero, Esteban Parra, and Sonia Haiduc. 2020. Experiences Building an Answer Bot for Gitter. In Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops (ICSEW’20). Association for Computing Machinery, New York, NY, USA. 66–70. isbn:9781450379632 https://doi.org/10.1145/3387940.3391505
[55]
Massimo Ruffolo. and Francesco Visalli. 2020. A Weak-supervision Method for Automating Training Set Creation in Multi-domain Aspect Sentiment Classification. In Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,. SciTePress, 249–256. isbn:978-989-758-395-7 issn:2184-433X
[56]
V. Selvi, S. Saranya, K. Chidida, and R. Abarna. 2019. Chatbot and bullyfree Chat. In 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN). 1–5. https://doi.org/10.1109/ICSCAN.2019.8878779
[57]
Dragos Şerban, Bart Golsteijn, Ralph Holdorp, and Alexander Serebrenik. 2021. SAW-BOT: Proposing Fixes for Static Analysis Warnings with GitHub Suggestions. In Workshop on Bots in Software Engineering. IEEE Computer Society, United States. https://doi.org/10.1109/BotSE52550.2021.00013
[58]
Sheri. 2020. python - Intent classification for Chatbot - Stack Overflow. https://stackoverflow.com/questions/62970861/intent-classification-for-chatbot (Accessed on 09/05/2023)
[59]
Xiaoming Shi, Haifeng Hu, Wanxiang Che, Zhongqian Sun, Ting Liu, and Junzhou Huang. 2020. Understanding Medical Conversations with Scattered Keyword Attention and Weak Supervision from Responses. Proceedings of the AAAI Conference on Artificial Intelligence, 34, 05 (2020), Apr., 8838–8845. https://doi.org/10.1609/aaai.v34i05.6412
[60]
Kai Shu, Subhabrata Mukherjee, Guoqing Zheng, Ahmed Hassan Awadallah, Milad Shokouhi, and Susan Dumais. 2020. Learning with Weak Supervision for Email Intent Detection. Association for Computing Machinery, New York, NY, USA. 1051–1060. isbn:9781450380164 https://doi.org/10.1145/3397271.3401121
[61]
Spacy. 2021. Industrial-strength Natural Language Processing in Python. https://spacy.io/ (Accessed on 01/09/2023)
[62]
Margaret-Anne Storey and Alexey Zagalsky. 2016. Disrupting Developer Productivity One Bot at a Time. In Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering (FSE 2016). Association for Computing Machinery, New York, NY, USA. 928–931. isbn:9781450342186 https://doi.org/10.1145/2950290.2983989
[63]
Snorkel Team. 2020. An Overview of Weak Supervision. https://www.snorkel.org/blog/weak-supervision (Accessed on 07/17/2023)
[64]
Snorkel Team. 2020. Snorkel Intro Tutorial: Data Labeling. https://www.snorkel.org/use-cases/01-spam-tutorial (Accessed on 07/09/2023)
[65]
Carlos Toxtli, Andrés Monroy-Hernández, and Justin Cranshaw. 2018. Understanding Chatbot-Mediated Task Management. Association for Computing Machinery, New York, NY, USA. isbn:9781450356206
[66]
Kieu Tran. 2020. nlp - Building a chatbot about literary novel - Stack Overflow. https://stackoverflow.com/questions/64007306/building-a-chatbot-about-literary-novel (Accessed on 07/16/2023)
[67]
RASA. 2021. Incremental Training. https://rasa.com/docs/rasa/next/migration-guide##incremental-training (Accessed on 11/08/2023)
[68]
S Vijayarani, Ms J Ilamathi, and Ms Nithya. 2015. Preprocessing techniques for text mining-an overview. International Journal of Computer Science & Communication Networks, 5, 1 (2015), 7–16.
[69]
Peilin Yu, Tiffany Ding, and Stephen H Bach. 2021. Learning from Multiple Noisy Partial Labelers. arXiv preprint arXiv:2106.04530.
[70]
Man-Ching Yuen, Irwin King, and Kwong-Sak Leung. 2011. A Survey of Crowdsourcing Systems. In 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third International Conference on Social Computing. 766–773. https://doi.org/10.1109/PASSAT/SocialCom.2011.203
[71]
Ce Zhang, Christopher Ré, Michael Cafarella, Christopher De Sa, Alex Ratner, Jaeho Shin, Feiran Wang, and Sen Wu. 2017. DeepDive: Declarative Knowledge Base Construction. Commun. ACM, 60, 5 (2017), apr, 93–102. issn:0001-0782 https://doi.org/10.1145/3060586
[72]
N. Zhang, Q. Huang, X. Xia, Y. Zou, D. Lo, and Z. Xing. 2020. Chatbot4QR: Interactive Query Refinement for Technical Question Retrieval. IEEE Transactions on Software Engineering, 1–1. https://doi.org/10.1109/TSE.2020.3016006
[73]
Zhi-Hua Zhou. 2017. A brief introduction to weakly supervised learning. National Science Review, 5, 1 (2017), 08, 44–53. issn:2095-5138 https://doi.org/10.1093/nsr/nwx106

Cited By

View all
  • (2024)A Transformer-based Approach for Augmenting Software Engineering Chatbots DatasetsProceedings of the 18th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement10.1145/3674805.3686695(359-370)Online publication date: 24-Oct-2024

Index Terms

  1. A Weak Supervision-Based Approach to Improve Chatbots for Code Repositories

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image Proceedings of the ACM on Software Engineering
    Proceedings of the ACM on Software Engineering  Volume 1, Issue FSE
    July 2024
    2770 pages
    EISSN:2994-970X
    DOI:10.1145/3554322
    Issue’s Table of Contents
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 12 July 2024
    Published in PACMSE Volume 1, Issue FSE

    Author Tags

    1. Software Chatbots
    2. Weak Supervision
    3. and Data Augmentation

    Qualifiers

    • Research-article

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)165
    • Downloads (Last 6 weeks)34
    Reflects downloads up to 25 Dec 2024

    Other Metrics

    Citations

    Cited By

    View all
    • (2024)A Transformer-based Approach for Augmenting Software Engineering Chatbots DatasetsProceedings of the 18th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement10.1145/3674805.3686695(359-370)Online publication date: 24-Oct-2024

    View Options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Login options

    Full Access

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media