Cited By
View all- Yu TGu XShen B(2022)Code Question Answering via Task-Adaptive Sequence-to-Sequence Pre-training2022 29th Asia-Pacific Software Engineering Conference (APSEC)10.1109/APSEC57359.2022.00035(229-238)Online publication date: Dec-2022
Answering Software Deployment Questions via Neural Machine Reading at Scale
Authors: 
Guanjie Qiu, Diwei Chen, Shuai Zhang, Yitian Chai, Xiaodong Gu, Beijun ShenAuthors Info & Claims
Guanjie Qiu, Diwei Chen, Shuai Zhang, Yitian Chai, Xiaodong Gu, Beijun ShenAuthors Info & ClaimsASE '22: Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering
Article No.: 151, Pages 1 - 4
Abstract
As software systems continue to grow in complexity and scale, deploying and delivering them becomes increasingly difficult. In this work, we develop DeployQA, a novel QA bot that automatically answers software deployment questions over user manuals and Stack Overflow posts. DeployQA is built upon RoBERTa. To bridge the gap between natural language and the domain of software deployment, we propose three adaptations in terms of vocabulary, pre-training, and fine-tuning, respectively. We evaluate our approach on our constructed DeQuAD dataset. The results show that DeployQA remarkably outperforms baseline methods by leveraging the three domain adaptation strategies.
Repository: https://github.com/Smallqqqq/DeployQA
Video: https://www.youtube.com/watch?v=TVf9w8gD3Ho
References
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Lili Bo and Jinting Lu. 2021. Bug Question Answering with Pretrained Encoders. In International Conference on Software Analysis, Evolution, and Reengineering (SANER). 654–660.
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Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes. 2017. Reading Wikipedia to Answer Open-Domain Questions. In Annual Meeting of the Association for Computational Linguistics (ACL). 1870–1879.
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Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT). 4171–4186.
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Tianjiao Du, Junming Cao, Qinyue Wu, Wei Li, Beijun Shen, and Yuting Chen. 2019. Cocoqa: Question Answering for Coding Conventions over Knowledge Graphs. In International Conference on Automated Software Engineering (ASE). 1086–1089.
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Gene Kim, Jez Humble, Patrick Debois, and John Willis. Nov 30, 2021. The DevOps Handbook: How to Create World-Class Agility, Reliability & Security in Technology Organizations. IT Revolution Press.
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Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. RoBERTa: A Robustly Optimized BERT Pretraining Approach. CoRR abs/1907.11692(2019).
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Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016. SQuAD: 100, 000+ Questions for Machine Comprehension of Text. In 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2383–2392.
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Stephen Robertson and Hugo Zaragoza. 2009. The Probabilistic Relevance Framework: BM25 and Beyond. Now Publishers Inc.
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Index Terms
- Answering Software Deployment Questions via Neural Machine Reading at Scale
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Information & Contributors
Information
Published In
October 2022
2006 pages
ISBN:9781450394758
DOI:10.1145/3551349
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 05 January 2023
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- Short-paper
- Research
- Refereed limited
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- CCF-Tencent Rhino-Bird Young Faculty Open Research Fund
- National Natural Science Foundation of China
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ASE '22
ASE '22: 37th IEEE/ACM International Conference on Automated Software Engineering
October 10 - 14, 2022
MI, Rochester, USA
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Overall Acceptance Rate 82 of 337 submissions, 24%
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Cited By
View all- Yu TGu XShen B(2022)Code Question Answering via Task-Adaptive Sequence-to-Sequence Pre-training2022 29th Asia-Pacific Software Engineering Conference (APSEC)10.1109/APSEC57359.2022.00035(229-238)Online publication date: Dec-2022
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