Skip to main content
SpringerLink
Log in

Analyzing Techniques for Duplicate Question Detection on Q&A Websites for Game Developers

  • Published:
Empirical Software Engineering Aims and scope Submit manuscript

Abstract

Game development is currently the largest industry in the entertainment segment and has a high demand for skilled game developers that can produce high-quality games. To satiate this demand, game developers need resources that can provide them with the knowledge they need to learn and improve their skills. Question and Answer (Q&A) websites are one of such resources that provide a valuable source of knowledge about game development practices. However, the presence of duplicate questions on Q&A websites hinders their ability to effectively provide information for their users. While several researchers created and analyzed techniques for duplicate question detection on websites such as Stack Overflow, so far no studies have explored how well those techniques work on Q&A websites for game development. With that in mind, in this paper we analyze how we can use pre-trained and unsupervised techniques to detect duplicate questions on Q&A websites focused on game development using data extracted from the Game Development Stack Exchange and Stack Overflow. We also explore how we can leverage a small set of labelled data to improve the performance of those techniques. The pre-trained technique based on MPNet achieved the highest results in identifying duplicate questions about game development, and we could achieve a better performance when combining multiple unsupervised techniques into a single supervised model. Furthermore, the supervised models could identify duplicate questions on websites different from those they were trained on with little to no decrease in performance. Our results lay the groundwork for building better duplicate question detection systems in Q&A websites for game developers and ultimately providing game developers with a more effective Q&A community.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data Availability

The datasets generated and analysed during the current study are available as part of our replication package on Zenodo on https://zenodo.org/record/5500268#.Y0iVjtLMJhF.

Notes

  1. https://answers.unity.com accessed September 6th, 2021.

  2. https://answers.unrealengine.com, accessed September 6th, 2021.

  3. https://gamedev.stackexchange.com, accessed September 6th, 2021.

  4. https://www.quora.com accessed September 6th, 2021.

  5. https://cooking.stackexchange.com/, accessed September 6th, 2021.

  6. https://photo.stackexchange.com/, accessed September 6th, 2021.

  7. https://www.researchgate.net/topics, accessed September 6th, 2021.

  8. https://stackexchange.com/, acessed July 4, 2021.

  9. Our replication package is available online at https://github.com/asgaardlab/done-21-arthur-duplicate_gamedev_questions-code.

  10. https://archive.org/details/stackexchange, accessed September 6th, 2021.

  11. Although duplicate questions usually point to only one main question, some duplicates point to several others. For example, question 10661714 links to five main questions: https://stackoverflow.com/questions/10661714. However, over 95% of duplicate questions have only one main question in our datasets.

    Table 1 Summary of the three datasets used in our methodology
    Full size table

  12. We define pre-trained techniques as any technique that requires a training step prior to being used for our task of detecting duplicate questions. This training step is usually unsupervised and does not use any additional information about our data aside from its vocabulary. While we pre-trained some of the techniques ourselves using the data we described in Section 3.1, we also used techniques that were pre-trained by their authors using a separate set of data.

  13. We performed all of our experiments on a laptop with an 8th generation Intel Core i7 processor and 16GB of RAM. We also used the Tesla P100 GPUs provided by Google Colab Pro, which are available for a small monthly fee. Using this setup, the most costly techniques took 11 minutes to run.

  14. https://stackoverflow.com/help/duplicates, accessed September 6th, 2021.

  15. https://github.com/RaRe-Technologies/gensim/blob/3.8.3/gensim/summarization/bm25.py, accessed September 6th, 2021.

  16. BERTOverflow was originally created for code and named entity recognition, but its word embeddings can be used for many natural language processing tasks.

  17. https://huggingface.co, accessed September 6th, 2021.

  18. https://www.sbert.net, accessed September 6th, 2021.

  19. https://www.sbert.net/docs/pretrained_models.html, accessed September 6th, 2021.

  20. The BM25 similarities scored at most 3 percent points more than MPNet for the game development dataset from Stack Overflow when using answers for the comparison. Despite having a lower performance, we still consider MPNet a more robust approach for detecting duplicates, as it has surpassed the second best approach in every other experiment we performed, with margins of up to 10 percent points.

  21. https://gamedev.stackexchange.com/questions/86517, accessed September 6th, 2021.

  22. https://gamedev.stackexchange.com/questions/86326, accessed September 6th, 2021.

  23. While the vectors produced by TF-IDF have as many dimensions as the size of the vocabulary in the corpus, these vectors are sparse and can only have as many non-zero values as the number of unique words in a document. In our datasets, the median number of unique words in each question is around 30. Meanwhile, MPNet produces dense vectors with a fixed size of 768 dimensions.

  24. https://gamedev.stackexchange.com/questions/116755, accessed September 6th, 2021.

  25. https://gamedev.stackexchange.com/questions/1653, September 6th, 2021.

  26. https://gamedev.stackexchange.com/questions/69119, accessed September 6th, 2021.

  27. https://stackoverflow.com/questions/3390396, accessed September 6th, 2021.

  28. https://stackoverflow.com/questions/1139762, accessed September 6th, 2021.

  29. https://stackoverflow.com/questions/5137497, accessed September 6th, 2021.

  30. The dataset is available at http://2015.msrconf.org/challenge.php, accessed June 1st, 2022.

  31. The current number of duplicate question pairs on Stack Overflow is available by running the following query on the Stack Exchange data explorer website https://data.stackexchange.com/stackoverflow/query/1440749/number-of-duplicate-questions-on-stack-overflow.

  32. https://www.elastic.co/elasticsearch/, accessed September 6th, 2021.

  33. Despite being trained using Stack Overflow data, BERTOverflow was created for code and named entity recognition and not was not previously used for duplicate question detection.

References

  • Abric D, Clark OE, Caminiti M, Gallaba K, McIntosh S (2019) Can duplicate questions on Stack Overflow benefit the software development community?. In: 2019 IEEE/ACM 16th international conference on mining software repositories (MSR). IEEE, pp 230–234

  • Ahasanuzzaman M, Asaduzzaman M, Roy CK, Schneider KA (2016) Mining duplicate questions of Stack Overflow. In: 2016 IEEE/ACM 13th working conference on mining software repositories (MSR). IEEE, pp 402–412

  • Ahmad A, Feng C, Ge S, Yousif A (2018) A survey on mining Stack Overflow: question and answering (Q&A) community. Data Technologies and Applications

  • Barua A, Thomas SW, Hassan AE (2014) What are developers talking about? An analysis of topics and trends in Stack Overflow. Empir Softw Eng 19 (3):619–654

    Article  Google Scholar 

  • Bazelli B, Hindle A, Stroulia E (2013) On the personality traits of StackOverflow users. In: 2013 IEEE International conference on software maintenance. IEEE, pp 460–463

  • Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3:993–1022

    MATH  Google Scholar 

  • Chen L, Baird A, Straub D (2019) Why do participants continue to contribute? Evaluation of usefulness voting and commenting motivational affordances within an online knowledge community. Decis Support Syst 118:21–32

    Article  Google Scholar 

  • Chowdhury A, Frieder O, Grossman D, McCabe MC (2002) Collection statistics for fast duplicate document detection. ACM Trans Inf Syst (TOIS) 20 (2):171–191

    Article  Google Scholar 

  • Dalip DH, Gonçalves MA, Cristo M, Calado P (2013) Exploiting user feedback to learn to rank answers in Q&A forums: a case study with Stack Overflow. In: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval, pp 543–552

  • Deng S, Tong J, Lin Y, Li H, Liu Y (2019) Motivating scholars’ responses in academic social networking sites: an empirical study on ResearchGate Q&A behavior. Inf Process Manag 56(6):102082

    Article  Google Scholar 

  • Devlin J, Chang MW, Lee K, Toutanova K (2018) BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805

  • Ellmann M (2019) Same-same but different: on understanding duplicates in Stack Overflow. Inform Spektrum 42(4):266–286

    Article  Google Scholar 

  • Fang C, Zhang J (2019) Users’ continued participation behavior in social Q&A communities: a motivation perspective. Comput Hum Behav 92:87–109

    Article  Google Scholar 

  • Fu H, Oh S (2019) Quality assessment of answers with user-identified criteria and data-driven features in social Q&A. Inf Process Manag 56(1):14–28

    Article  Google Scholar 

  • Guan T, Wang L, Jin J, Song X (2018) Knowledge contribution behavior in online Q&A communities: an empirical investigation. Comput Hum Behav 81:137–147

    Article  Google Scholar 

  • Hindle A, Onuczko C (2019) Preventing duplicate bug reports by continuously querying bug reports. Empir Softw Eng 24(2):902–936

    Article  Google Scholar 

  • Hindle A, Alipour A, Stroulia E (2016) A contextual approach towards more accurate duplicate bug report detection and ranking. Empir Softw Eng 21 (2):368–410

    Article  Google Scholar 

  • Homma Y, Sy S, Yeh C (2016) Detecting duplicate questions with deep learning. In: Proceedings of the international conference on neural information processing systems (NIPS)

  • Hong Z, Deng Z, Evans R, Wu H (2020) Patient questions and physician responses in a Chinese health Q&A website: content analysis. J Med Internet Res 22(4):e13071

    Article  Google Scholar 

  • Hoogeveen D, Bennett A, Li Y, Verspoor KM, Baldwin T (2018) Detecting misflagged duplicate questions in community question-answering archives. In: Twelfth international AAAI conference on web and social media

  • Imtiaz Z, Umer M, Ahmad M, Ullah S, Choi GS, Mehmood A (2020) Duplicate questions pair detection using siamese maLSTM. IEEE Access 8:21932–21942

    Article  Google Scholar 

  • Jaccard P (1912) The distribution of the flora in the alpine zone. 1. New Phytol 11(2):37–50

    Article  Google Scholar 

  • Jin J, Li Y, Zhong X, Zhai L (2015) Why users contribute knowledge to online communities: an empirical study of an online social Q&A community. Inf Manag 52(7):840–849

    Article  Google Scholar 

  • Kamath A, Gupta S, Carvalho V (2019) Reversing gradients in adversarial domain adaptation for question deduplication and textual entailment tasks. In: Proceedings of the 57th annual meeting of the association for computational linguistics, pp 5545–5550

  • Kamienski A, Bezemer CP (2021) An empirical study of Q&A websites for game developers. Empir Softw Eng

  • Le Q, Mikolov T (2014) Distributed representations of sentences and documents. In: International conference on machine learning, pp 1188–1196

  • Li Z, Yin G, Yu Y, Wang T, Wang H (2017) Detecting duplicate pull-requests in GitHub. In: Proceedings of the 9th Asia-Pacific symposium on internetware, pp 1–6

  • Li Z, Yu Y, Zhou M, Wang T, Yin G, Lan L, Wang H (2020) Redundancy, context, and preference: an empirical study of duplicate pull requests in OSS projects, IEEE Trans Softw Eng

  • Liang D, Zhang F, Zhang W, Zhang Q, Fu J, Peng M, Gui T, Huang X (2019) Adaptive multi-attention network incorporating answer information for duplicate question detection. In: Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval, pp 95–104

  • Lopresti DP (1999) Models and algorithms for duplicate document detection. In: Proceedings of the fifth international conference on document analysis and recognition. ICDAR’99 (Cat. No. PR00318). IEEE, pp 297–300

  • Mikolov T, Chen K, Corrado G, Dean J (2013a) Efficient estimation of word representations in vector space. arXiv:1301.3781

  • Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013b) Distributed representations of words and phrases and their compositionality. In: Advances in neural information processing systems, pp 3111–3119

  • Mizobuchi Y, Takayama K (2017) Two improvements to detect duplicates in Stack Overflow. In: 2017 IEEE 24th international conference on software analysis, evolution and reengineering (SANER). IEEE, pp 563–564

  • Nasehi SM, Sillito J, Maurer F, Burns C (2012) What makes a good code example?: a study of programming Q&A in StackOverflow. In: 2012 28th IEEE international conference on software maintenance (ICSM). IEEE, pp 25–34

  • Niwattanakul S, Singthongchai J, Naenudorn E, Wanapu S (2013) Using of Jaccard coefficient for keywords similarity. In: Proceedings of the international multiconference of engineers and computer scientists, vol 6, pp 380–384

  • Omondiagbe OP, Licorish SA, MacDonell SG (2019) Features that predict the acceptability of Java and JavaScript answers on Stack Overflow. In: Proceedings of the evaluation and assessment on software engineering, pp 101–110

  • Overflow S (2021) About Stack Overflow. https://stackoverflow.com/company. Accessed 25 July 2021

  • Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830

    MathSciNet  MATH  Google Scholar 

  • Poerner N, Schütze H (2019) Multi-view domain adapted sentence embeddings for low-resource unsupervised duplicate question detection. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP), pp 1630–1641

  • Porter MF, et al. (1980) An algorithm for suffix stripping. Program 14(3):130–137

    Article  Google Scholar 

  • Prabowo DA, Herwanto GB (2019) Duplicate question detection in question answer website using convolutional neural network. In: 2019 5th International conference on science and technology (ICST), vol 1. IEEE, pp 1–6

  • Procaci TB, Nunes BP, Nurmikko-Fuller T, Siqueira SW (2016) Finding topical experts in question & answer communities. In: 2016 IEEE 16th international conference on advanced learning technologies (ICALT). IEEE, pp 407–411

  • Procaci TB, Siqueira SW, Nunes BP, Nurmikko-Fuller T (2017) Modelling experts behaviour in Q&A communities to predict worthy discussions. In: 2017 IEEE 17th international conference on advanced learning technologies (ICALT). IEEE, pp 291–295

  • Rahman MM, Roy CK (2015) An insight into the unresolved questions at Stack Overflow. In: 2015 IEEE/ACM 12th working conference on mining software repositories. IEEE, pp 426–429

  • Rakha MS, Bezemer CP, Hassan AE (2017) Revisiting the performance evaluation of automated approaches for the retrieval of duplicate issue reports. IEEE Trans Softw Eng 44(12):1245–1268

    Article  Google Scholar 

  • Rakha MS, Bezemer CP, Hassan AE (2018) Revisiting the performance of automated approaches for the retrieval of duplicate reports in issue tracking systems that perform just-in-time duplicate retrieval. Empir Softw Eng 23(5):2597–2621

    Article  Google Scholar 

  • Ramos J, et al. (2003) Using tf-idf to determine word relevance in document queries. In: Proceedings of the first instructional conference on machine learning, vol 242. Citeseer, pp 29–48

  • Řehůřek R (2021) Gensim: topic modelling for humans. https://radimrehurek.com/gensim. Accessed 5 Sept 2021

  • Reimers N, Gurevych I (2019) Sentence-BERT: sentence embeddings using siamese BERT-networks. In: Proceedings of the 2019 conference on empirical methods in natural language processing. arXiv:1908.10084. Association for Computational Linguistics

  • Richardson L (2020) Beautiful soup. https://www.crummy.com/software/BeautifulSoup. Accessed 5 Sep 2021

  • Rochette A, Yaghoobzadeh Y, Hazen TJ (2019) Unsupervised domain adaptation of contextual embeddings for low-resource duplicate question detection. arXiv:1911.02645

  • Rodrigues J, Saedi C, Maraev V, Silva J, Branco A (2017) Ways of asking and replying in duplicate question detection. In: Proceedings of the 6th joint conference on lexical and computational semantics (SEM), pp 262–270

  • Rücklé A, Moosavi NS, Gurevych I (2019) Neural duplicate question detection without labeled training data. arXiv:1911.05594

  • Runeson P, Alexandersson M, Nyholm O (2007) Detection of duplicate defect reports using natural language processing. In: 29th International conference on software engineering (ICSE’07). IEEE, pp 499–510

  • Saedi C, Rodrigues J, Silva J, Branco A, Maraev V (2017) Learning profiles in duplicate question detection. In: 2017 IEEE International conference on information reuse and integration (IRI). IEEE, pp 544– 550

  • Santos T, Burghardt K, Lerman K, Helic D (2020) Can badges foster a more welcoming culture on Q&A boards?. In: Proceedings of the international AAAI conference on Web and social media, vol 14, pp 969–973

  • Shah DJ, Lei T, Moschitti A, Romeo S, Nakov P (2018) Adversarial domain adaptation for duplicate question detection. arXiv:1809.02255

  • Shen X, Jia AL, Shen S, Dou Y (2020) Helping the ineloquent farmers: finding experts for questions with limited text in agricultural Q&A communities. IEEE Access 8:62238–62247

    Article  Google Scholar 

  • Silva RF, Paixão K, de Almeida Maia M (2018) Duplicate question detection in Stack Overflow: a reproducibility study. In: 2018 IEEE 25th international conference on software analysis, evolution and reengineering (SANER). IEEE, pp 572–581

  • Song K, Tan X, Qin T, Lu J, Liu TY (2020) MPNet: masked and permuted pre-training for language understanding. arXiv:2004.09297

  • Sun C, Lo D, Wang X, Jiang J, Khoo SC (2010) A discriminative model approach for accurate duplicate bug report retrieval. In: Proceedings of the 32nd ACM/IEEE international conference on software engineering, vol 1, pp 45–54

  • Sun C, Lo D, Khoo SC, Jiang J (2011) Towards more accurate retrieval of duplicate bug reports. In: 2011 26th IEEE/ACM international conference on automated software engineering (ASE 2011). IEEE, pp 253–262

  • Tabassum J, Maddela M, Xu W, Ritter A (2020) Code and named entity recognition in StackOverflow. In: Proceedings of the 58th annual meeting of the association for computational linguistics (ACL). https://www.aclweb.org/anthology/2020.acl-main.443/

  • Viggiato M, Lin D, Hindle A, Bezemer CP (2021) What causes wrong sentiment classifications of game reviews. IEEE Trans Games

  • Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, Burovski E, Peterson P, Weckesser W, Bright J, van der Walt SJ, Brett M, Wilson J, Millman KJ, Mayorov N, Nelson ARJ, Jones E, Kern R, Larson E, Carey CJ, Polat İ, Feng Y, Moore EW, VanderPlas J, Laxalde D, Perktold J, Cimrman R, Henriksen I, Quintero EA, Harris CR, Archibald AM, Ribeiro AH, Pedregosa F, van Mulbregt P (2020) Scipy 1.0 contributors: SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods 17:261–272. https://doi.org/10.1038/s41592-019-0686-2

    Article  Google Scholar 

  • Wang Y (2020) The price of being polite: politeness, social status, and their joint impacts on community Q&A efficiency. J Comput Social Sci 1–22

  • Wang L, Zhang L, Jiang J (2019a) Detecting duplicate questions in Stack Overflow via deep learning approaches. In: 2019 26th Asia-Pacific software engineering conference (APSEC). IEEE, pp 506– 513

  • Wang Q, Xu B, Xia X, Wang T, Li S (2019b) Duplicate pull request detection: when time matters. In: Proceedings of the 11th Asia-Pacific symposium on internetware, pp 1–10

  • Wang L, Zhang L, Jiang J (2020) Duplicate question detection with deep learning in Stack Overflow. IEEE Access 8:25964–25975

    Article  Google Scholar 

  • Witkowski W (2020) Videogames are a bigger industry than movies and north american sports combined, thanks to the pandemic. https://www.marketwatch.com/story/videogames-are-a-bigger-industry-than-sports-and-movies-combined-thanks-to-the-pandemic-11608654990https://www.marketwatch.com/story/videogames-are-a-bigger-industry-than-sports-and-movies-combined-thanks-to-the-pandemic-11608654990. Accessed 4 July 2021

  • Wolf T, Debut L, Sanh V, Chaumond J, Delangue C, Moi A, Cistac P, Rault T, Louf R, Funtowicz M, Davison J, Shleifer S, von Platen P, Ma C, Jernite Y, Plu J, Xu C, Scao TL, Gugger S, Drame M, Lhoest Q, Rush AM (2020) Transformers: state-of-the-art natural language processing. In: Proceedings of the 2020 conference on empirical methods in natural language processing: system demonstrations. Online. https://www.aclweb.org/anthology/2020.emnlp-demos.6. Association for Computational Linguistics, pp 38–45

  • Wu Y, Wang S, Bezemer CP, Inoue K (2019) How do developers utilize source code from Stack Overflow? Empir Softw Eng 24(2):637–673

    Article  Google Scholar 

  • Xu Z, Yuan H (2020) Forum duplicate question detection by domain adaptive semantic matching. IEEE Access 8:56029–56038

    Article  Google Scholar 

  • Xu B, Hoang T, Sharma A, Yang C, Xia X, Lo D (2021) Post2vec: learning distributed representations of Stack Overflow posts. IEEE Trans Softw Eng

  • Yang XL, Lo D, Xia X, Wan ZY, Sun JL (2016) What security questions do developers ask? A large-scale study of Stack Overflow posts. J Comput Sci Technol 31(5):910–924

    Article  Google Scholar 

  • Ying ATT (2015) Mining challenge 2015: comparing and combining different information sources on the Stack Overflow data set. In: The 12th working conference on mining software repositories

  • Zhang Y, Lo D, Xia X, Sun JL (2015) Multi-factor duplicate question detection in Stack Overflow. J Comput Sci Technol 30(5):981–997

    Article  Google Scholar 

  • Zhang WE, Sheng QZ, Lau JH, Abebe E (2017a) Detecting duplicate posts in programming QA communities via latent semantics and association rules. In: Proceedings of the 26th international conference on World Wide Web, pp 1221–1229

  • Zhang WE, Sheng QZ, Shu Y, Nguyen VK (2017b) Feature analysis for duplicate detection in programming QA communities. In: International conference on advanced data mining and applications. Springer, pp 623–638

  • Zhang X, Liu S, Chen X, et al. (2017c) Social capital, motivations, and knowledge sharing intention in health Q&A communities. Manag Decis

  • Zhang WE, Sheng QZ, Lau JH, Abebe E, Ruan W (2018a) Duplicate detection in programming question answering communities. ACM Trans Internet Technol (TOIT) 18(3):1–21

    Google Scholar 

  • Zhang WE, Sheng QZ, Tang Z, Ruan W (2018b) Related or duplicate: distinguishing similar CQA questions via convolutional neural networks. In: The 41st international ACM SIGIR conference on research & development in information retrieval, pp 1153–1156

  • Zhang Y, Lu T, Phang CW, Zhang C (2019) Scientific knowledge communication in online Q&A communities: linguistic devices as a tool to increase the popularity and perceived professionalism of knowledge contribution. J Assoc Inf Syst 20(8):3

    Google Scholar 

  • Zhou Q, Liu X, Wang Q (2021) Interpretable duplicate question detection models based on attention mechanism. Inf Sci 543:259–272

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Alberta, Edmonton, AB, Canada

    Arthur Kamienski, Abram Hindle & Cor-Paul Bezemer

Authors
  1. Arthur Kamienski
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abram Hindle
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Cor-Paul Bezemer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Arthur Kamienski.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Additional information

Communicated by: Sebastian Baltes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kamienski, A., Hindle, A. & Bezemer, CP. Analyzing Techniques for Duplicate Question Detection on Q&A Websites for Game Developers. Empir Software Eng 28, 17 (2023). https://doi.org/10.1007/s10664-022-10256-w

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10664-022-10256-w

Keywords

Search

Navigation