2023

CUPID: Leveraging ChatGPT for More Accurate Duplicate Bug Report Detection

Zhang, Ting, Irsan, Ivana Clairine, Thung, Ferdian et al.

Understand

Duplicate bug report detection (DBRD) is a long-standing challenge in both academia and industry.

Over the past decades, researchers have proposed various approaches to detect duplicate bug reports more accurately.
With the recent advancement of deep learning, researchers have also proposed several deep learning-based approaches to address the DBRD task.
In the bug repositories with many bug reports, deep learning-based approaches have shown promising performance.

Built on

McCloskey, M., Cohen, N.J.: Catastrophic interference in connectionist networks: The sequential learning problem. In: Psychology of Learning and Motivation vol. 24, pp. 109–165. Elsevier, ??? (1989)
1989
Earlier work this paper cites.
Robertson, S., Zaragoza, H., Taylor, M.: Simple bm25 extension to multiple weighted fields. In: Proceedings of the Thirteenth ACM International Conference on Information and Knowledge Management, pp. 42–49 (2004)
2004
Earlier work this paper cites.
Zaragoza, H., Craswell, N., Taylor, M.J., Saria, S., Robertson, S.E.: Microsoft cambridge at trec 13: Web and hard tracks. In: Trec, vol. 4, pp. 1–1 (2004)
2004
Earlier work this paper cites.
Runeson, P., Alexandersson, M., Nyholm, O.: Detection of duplicate defect reports using natural language processing. In: 29th International Conference on Software Engineering (ICSE’07), pp. 499–510 (2007). IEEE
2007
Earlier work this paper cites.
Jalbert, N., Weimer, W.: Automated duplicate detection for bug tracking systems. In: 2008 IEEE International Conference on Dependable Systems and Networks With FTCS and DCC (DSN), pp. 52–61 (2008). IEEE
2008
Earlier work this paper cites.
Bettenburg, N., Just, S., Schröter, A., Weiss, C., Premraj, R., Zimmermann, T.: What makes a good bug report? In: Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of Software Engineering, pp. 308–318 (2008)
2008
Earlier work this paper cites.
Wang, X., Zhang, L., Xie, T., Anvik, J., Sun, J.: An approach to detecting duplicate bug reports using natural language and execution information. In: Proceedings of the 30th International Conference on Software Engineering, pp. 461–470 (2008)
2008
Earlier work this paper cites.
Schütze, H., Manning, C.D., Raghavan, P.: Introduction to Information Retrieval vol. 39. Cambridge University Press Cambridge, ??? (2008)
2008
Earlier work this paper cites.
El-Beltagy, S.R., Rafea, A.: Kp-miner: A keyphrase extraction system for english and arabic documents. Information systems 34
2009
Earlier work this paper cites.
Sun, C., Lo, D., Wang, X., Jiang, J., Khoo, S.-C.: A discriminative model approach for accurate duplicate bug report retrieval. In: Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering-Volume 1, pp. 45–54 (2010)
2010
Earlier work this paper cites.
Sureka, A., Jalote, P.: Detecting duplicate bug report using character n-gram-based features. In: 2010 Asia Pacific Software Engineering Conference, pp. 366–374 (2010). IEEE
2010
Earlier work this paper cites.
Rose, S., Engel, D., Cramer, N., Cowley, W.: Automatic keyword extraction from individual documents. Text mining: applications and theory, 1–20 (2010)
2010
Earlier work this paper cites.
Sun, C., Lo, D., Khoo, S.-C., Jiang, J.: Towards more accurate retrieval of duplicate bug reports. In: 2011 26th IEEE/ACM International Conference on Automated Software Engineering (ASE 2011), pp. 253–262 (2011). IEEE
2011
Earlier work this paper cites.
Nguyen, A.T., Nguyen, T.T., Nguyen, T.N., Lo, D., Sun, C.: Duplicate bug report detection with a combination of information retrieval and topic modeling. In: Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering, pp. 70–79 (2012)
2012
Earlier work this paper cites.
Zhou, J., Zhang, H., Lo, D.: Where should the bugs be fixed? more accurate information retrieval-based bug localization based on bug reports. In: 2012 34th International Conference on Software Engineering (ICSE), pp. 14–24 (2012). IEEE
2012
Earlier work this paper cites.
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
Original
2013
Earlier work this paper cites.
Amoui, M., Kaushik, N., Al-Dabbagh, A., Tahvildari, L., Li, S., Liu, W.: Search-based duplicate defect detection: An industrial experience. In: 2013 10th Working Conference on Mining Software Repositories (MSR), pp. 173–182 (2013). IEEE
2013
Earlier work this paper cites.
Lazar, A., Ritchey, S., Sharif, B.: Generating duplicate bug datasets. In: Proceedings of the 11th Working Conference on Mining Software Repositories, pp. 392–395 (2014)
2014
Earlier work this paper cites.
Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)
2014
Earlier work this paper cites.
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27
2014
Earlier work this paper cites.
Boudin, F.: pke: an open source python-based keyphrase extraction toolkit. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations, Osaka, Japan, pp. 69–73 (2016). http://aclweb.org/anthology/C16-2015
2015
Earlier work this paper cites.
Kochhar, P.S., Xia, X., Lo, D., Li, S.: Practitioners’ expectations on automated fault localization. In: Proceedings of the 25th International Symposium on Software Testing and Analysis, pp. 165–176 (2016)
2016
Earlier work this paper cites.
Parikh, A., Täckström, O., Das, D., Uszkoreit, J.: A decomposable attention model for natural language inference. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2249–2255 (2016)
2016
Earlier work this paper cites.
Deshmukh, J., Annervaz, K., Podder, S., Sengupta, S., Dubash, N.: Towards accurate duplicate bug retrieval using deep learning techniques. In: 2017 IEEE International Conference on Software Maintenance and Evolution (ICSME), pp. 115–124 (2017). IEEE
2017
Earlier work this paper cites.

Similar

Fu, T.-y., Lee, W.-C., Lei, Z.: Hin2vec: Explore meta-paths in heterogeneous information networks for representation learning. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 1797–1806 (2017)
2017
Cited alongside, same era.
Riazi, M.S., Rouani, B.D., Koushanfar, F.: Deep learning on private data. IEEE Security & Privacy 17
2019
Cited alongside, same era.
Joshi, S.D., Chimalakonda, S.: Rapidrelease-a dataset of projects and issues on github with rapid releases. In: 2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR), pp. 587–591 (2019). IEEE
2019
Cited alongside, same era.
Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of NAACL-HLT, pp. 4171–4186 (2019)
2019
Cited alongside, same era.
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al
2019
Cited alongside, same era.
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019)
Original
2019
Cited alongside, same era.
Rodrigues, I.M., Aloise, D., Fernandes, E.R., Dagenais, M.: A soft alignment model for bug deduplication. In: Proceedings of the 17th International Conference on Mining Software Repositories, pp. 43–53 (2020)
2020
Cited alongside, same era.
He, J., Xu, L., Yan, M., Xia, X., Lei, Y.: Duplicate bug report detection using dual-channel convolutional neural networks. In: Proceedings of the 28th International Conference on Program Comprehension, pp. 117–127 (2020)
2020
Cited alongside, same era.
Xiao, G., Du, X., Sui, Y., Yue, T.: Hindbr: Heterogeneous information network based duplicate bug report prediction. In: 2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE), pp. 195–206 (2020). IEEE
2020
Cited alongside, same era.
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al
2020
Cited alongside, same era.
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., Davison, J., Shleifer, S., Platen, P., Ma, C., Jernite, Y., Plu, J., Xu, C., Scao, T.L., Gugger, S., Drame, M., Lhoest, Q., Rush, A.M.: Transformers: State-of-the-art natural language processing. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pp. 38–45. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.6
2020
Cited alongside, same era.
Campos, R., Mangaravite, V., Pasquali, A., Jorge, A., Nunes, C., Jatowt, A.: Yake! keyword extraction from single documents using multiple local features. Information Sciences 509
2020
Cited alongside, same era.

Then

Zhang, T., Irsan, I.C., Thung, F., Lo, D.: Revisiting sentiment analysis for software engineering in the era of large language models. arXiv preprint arXiv:2310.11113 (2023)
Original
2023
Closest in time.
Zhang, W., Deng, Y., Liu, B., Pan, S.J., Bing, L.: Sentiment analysis in the era of large language models: A reality check. arXiv preprint arXiv:2305.15005 (2023)
Original
2023
Closest in time.
ChatGPT — Release Notes — OpenAI Help Center. https://help.openai.com/en/articles/6825453-chatgpt-release-notes . (Accessed on 05/05/2024) (2023)
2023
Closest in time.
Bugzilla. https://www.bugzilla.org/ . (Accessed on 05/05/2024) (2024)
2024
Closest in time.
Jira — Issue & Project Tracking Software — Atlassian. https://www.atlassian.com/software/jira . (Accessed on 05/05/2024) (2024)
2024
Closest in time.
GitHub. https://github.com/ . (Accessed on 05/05/2024) (2024)
2024
Closest in time.
axios/axios: Promise based HTTP client for the browser and node.js. https://github.com/axios/axios . (Accessed on 05/05/2024) (2024)
2024
Closest in time.
vuejs/vue: This is the repo for Vue 2. For Vue 3, go to https://github.com/vuejs/core. https://github.com/vuejs/vue . (Accessed on 05/05/2024) (2024)
2024
Closest in time.
Significant-Gravitas/Auto-GPT. https://github.com/Significant-Gravitas/Auto-GPT . (Accessed on 05/05/2024) (2024)
2024
Closest in time.
AI@Meta: Llama 3 model card (2024)
2024
Closest in time.
Introducing Phi-3: Redefining what’s possible with SLMs — Microsoft Azure Blog. https://azure.microsoft.com/en-us/blog/introducing-phi-3-redefining-whats-possible-with-slms/ . (Accessed on 05/13/2024) (2024)
2024
Closest in time.
Abdin, M., Jacobs, S.A., Awan, A.A., Aneja, J., Awadallah, A., Awadalla, H., Bach, N., Bahree, A., Bakhtiari, A., Behl, H., et al.: Phi-3 technical report: A highly capable language model locally on your phone. arXiv preprint arXiv:2404.14219 (2024)
Original
2024
Closest in time.
Wang, G., Cheng, S., Zhan, X., Li, X., Song, S., Liu, Y.: Openchat: Advancing open-source language models with mixed-quality data. In: The Twelfth International Conference on Learning Representations (2024)
2024
Closest in time.
Introducing ChatGPT. https://openai.com/blog/chatgpt . (Accessed on 05/05/2024) (2024)
2024
Closest in time.
LMSys Chatbot Arena Leaderboard - a Hugging Face Space by lmsys. https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard . (Accessed on 05/05/2024) (2024)
2024
Closest in time.
[HADOOP-17091] [JDK11] Fix Javadoc errors - ASF JIRA. https://issues.apache.org/jira/browse/HADOOP-17091 . (Accessed on 05/05/2024) (2024)
2024
Closest in time.
[HADOOP-16648] HDFS Native Client does not build correctly - ASF JIRA. https://issues.apache.org/jira/browse/HADOOP-16648 . (Accessed on 05/05/2024) (2024)
2024
Closest in time.
[HADOOP-16862] [JDK11] Support JavaDoc - ASF JIRA. https://issues.apache.org/jira/browse/HADOOP-16862 . (Accessed on 05/05/2024) (2024)
2024
Closest in time.
[SPARK-31519] Cast in having aggregate expressions returns the wrong result. https://issues.apache.org/jira/browse/SPARK-31519 . (Accessed on 05/05/2024) (2024)
2024
Closest in time.
[SPARK-31334] Use agg column in Having clause behave different with column type. https://issues.apache.org/jira/browse/SPARK-31334 . (Accessed on 05/05/2024) (2024)
2024
Closest in time.
[Discover] ”Reset search” is broken · Issue #66249 · elastic/kibana. https://github.com/elastic/kibana/issues/66249 . (Accessed on 05/05/2024) (2024)
2024
Closest in time.
FireFox Filters Bug · Issue #43282 · elastic/kibana. https://github.com/elastic/kibana/issues/43282 . (Accessed on 05/05/2024)
2024
Closest in time.
[SPARK-30412] Eliminate warnings in Java tests regarding to deprecated API - ASF JIRA. https://issues.apache.org/jira/browse/SPARK-30412 . (Accessed on 05/05/2024) (2024)
2024
Closest in time.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Reading the bibliography…