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EnTagRec ++: An enhanced tag recommendation system for software information sites

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Abstract

Software engineers share experiences with modern technologies using software information sites, such as Stack Overflow. These sites allow developers to label posted content, referred to as software objects, with short descriptions, known as tags. Tags help to improve the organization of questions and simplify the browsing of questions for users. However, tags assigned to objects tend to be noisy and some objects are not well tagged. For instance, 14.7% of the questions that were posted in 2015 on Stack Overflow needed tag re-editing after the initial assignment. To improve the quality of tags in software information sites, we propose EnTagRec ++, which is an advanced version of our prior work EnTagRec. Different from EnTagRec, EnTagRec ++ does not only integrate the historical tag assignments to software objects, but also leverages the information of users, and an initial set of tags that a user may provide for tag recommendation. We evaluate its performance on five software information sites, Stack Overflow, Ask Ubuntu, Ask Different, Super User, and Freecode. We observe that even without considering an initial set of tags that a user provides, it achieves Recall@5 scores of 0.821, 0.822, 0.891, 0.818 and 0.651, and Recall@10 scores of 0.873, 0.886, 0.956, 0.887 and 0.761, on Stack Overflow, Ask Ubuntu, Ask Different, Super User, and Freecode, respectively. In terms of Recall@5 and Recall@10, averaging across the 5 datasets, it improves upon TagCombine, which is the prior state-of-the-art approach, by 29.3% and 14.5% respectively. Moreover, the performance of our approach is further boosted if users provide some initial tags that our approach can leverage to infer additional tags: when an initial set of tags is given, Recall@5 is improved by 10%.

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Notes

  1. http://meta.stackexchange.com/questions/tagged/tags

  2. http://meta.stackexchange.com/questions/206907/how-are-suggested-tags-chosen

  3. Since the implementation of Stack Overflow’s proprietary system is, to the best of our knowledge, not documented publicly, a meaningful comparison was not possible.

  4. http://sourceforge.net/

  5. http://marketplace.eclipse.org/

  6. http://snipplr.com/

  7. http://stackoverflow.com/q/5550896

  8. http://stackoverflow.com/q/2058138

  9. https://data.stackexchange.com/stackoverflow/queries

  10. http://stackoverflow.com/users/137369/thirler?tab=tags

  11. Based on http://www.textfixer.com/resources/common-english-words.txt

  12. http://nlp.stanford.edu/software/tagger.shtml

  13. Our experiments show that the effectiveness of UIC substantially degrades if it takes into consideration all tags.

  14. By construction, γ is an extra weight given to some of the tags in \(T_{\text {\small {\textsl {BIC}}} \cup \text {\small {\textsl {FIC}}}}\).

  15. Since EnTagRec ++ o (t) is itself a probability score, it could also be expressed as a function of only three coefficients α′, β′, and γ′, with the fourth being automatically 1 − α′ − β′ − γ′. We chose the four-coefficient expression to better reflect the four components of EnTagRec ++.

  16. https://sites.google.com/site/wswshaoweiwang/projects/entagrec

References

  • Al-Kofahi JM, Tamrawi A, Nguyen TT, Nguyen HA, Nguyen TN (2010) Fuzzy set approach for automatic tagging in evolving software ICSM, pp 1–10

    Google Scholar 

  • Antoniol G, Canfora G, Casazza G, De Lucia A, Merlo E (2002) Recovering traceability links between code and documentation. IEEE Trans Softw Eng 28(10):970–983

    Article  Google Scholar 

  • Asuncion HU, Asuncion AU, Taylor RN (2010) Software traceability with topic modeling ICSE, pp 95–104

    Google Scholar 

  • Baldi P, Lopes CV, Linstead E, Bajracharya SK (2008) A theory of aspects as latent topics OOPSLA, pp 543–562

    Google Scholar 

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

  • Benjamini Y, Yekutieli D (2001) The control of the false discovery rate in multiple testing under dependency. Ann Stat 29:1165–1188

    Article  MathSciNet  MATH  Google Scholar 

  • Bergstra J, Bengio Y (2012) Random search for hyper-parameter optimization. JMLR 13:281–305

    MathSciNet  MATH  Google Scholar 

  • Bindelli S, Criscione C, Curino C, Drago ML, Eynard D, Orsi G (2008) Improving search and navigation by combining ontologies and social tags. In: On the move to meaningful internet systems, OTM 2008 Workshops, OTM confederated international workshops and posters, ADI, AWeSoMe, COMBEK, EI2N, IWSSA, MONET, OnToContent + QSI, ORM, PerSys, RDDS, SEMELS, and SWWS 2008, Monterrey, Mexico, November 9-14, 2008. Proceedings, pp 76–85

  • Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. JMLR, 993–1022

  • Brandt J, Guo PJ, Lewenstein J, Dontcheva M, Klemmer SR (2009) Two studies of opportunistic programming: interleaving web foraging, learning, and writing code CHI. ACM, pp 1589–1598

  • Cabot J, Izquierdo JLC, Cosentino V, Rolandi B (2015) Exploring the use of labels to categorize issues in open-source software projects. In: 22nd IEEE international conference on software analysis, evolution, and reengineering, SANER 2015. Montreal, QC, Canada, March 2-6, 2015, pp 550–554

  • Capobianco G, Lucia AD, Oliveto R, Panichella A, Panichella S (2013) Improving IR-based traceability recovery via noun-based indexing of software artifacts. J Softw Evol Process 25(7):743–762

    Article  Google Scholar 

  • Cress U, Held C, Kimmerle J (2013) The collective knowledge of social tags: direct and indirect influences on navigation, learning, and information processing. Comput Educ 60(1):59–73

    Article  Google Scholar 

  • Crestani F (1997) Application of spreading activation techniques in information retrieval. Artif Intell Rev 11(6):453–482

    Article  Google Scholar 

  • Gelman A, Carlin J, Stern H, Rubin D (2003) Bayesian data analysis. CRC Press

  • Ghamrawi N, McCallum A (2005) Collective multi-label classification CIKM, pp 195–200

    Google Scholar 

  • Golder SA, Huberman BA (2006) Usage patterns of collaborative tagging systems. J Inf Sci 32(2):198–206

    Article  Google Scholar 

  • Grissom RJ, Kim JJ (2005) Effect sizes for research. A broad practical approach

  • Han J, Kamber M, Pei J (2011) Data mining: concepts and techniques. Morgan Kaufmann Publishers Inc

  • Held C, Kimmerle J, Cress U (2012) Learning by foraging: the impact of individual knowledge and social tags on web navigation processes. Comput Hum Behav 28(1):34–40

    Article  Google Scholar 

  • Hong L, Davison BD (2010) Empirical study of topic modeling in twitter. In: Proceedings of the first workshop on social media analytics, SOMA ’10, pp 80–88

  • Jäschke R, Marinho LB, Hotho A, Schmidt-Thieme L, Stumme G (2007) Tag recommendations in folksonomies PKDD

  • Jmac (2013) Select and display ‘suggested tags’ for all posts based on related questions (or other logic). http://meta.stackexchange.com/q/196702/182512

  • Joorabchi A, English M, Mahdi AE (2015) Automatic mapping of user tags to wikipedia concepts: the case of a q&a website âĂŞ stackoverflow. J Inf Sci 41 (5):570–583

    Article  Google Scholar 

  • Her J (2011) Tag recommendations for stack overflow. http://meta.stackexchange.com/q/88611/182512

  • Lukins SK, Kraft NA, Etzkorn LH (2010) Bug localization using latent dirichlet allocation. Inf Softw Technol 52(9):972–990

    Article  Google Scholar 

  • Panichella A, Dit B, Oliveto R, Di Penta M, Poshyvanyk D, Lucia AD (2013) How to effectively use topic models for software engineering tasks? An approach based on genetic algorithms ICSE, pp 522–531

    Google Scholar 

  • Pletea D, Vasilescu B, Serebrenik A (2014) Security and emotion: Sentiment analysis of security discussions on github. In: Proceedings of the 11th working conference on mining software repositories, MSR 2014. ACM, New York, pp 348–351

  • Porter MF (1997) An algorithm for suffix stripping Readings in information retrieval. Morgan Kaufmann, pp 313–316

  • Puurula A (2011) Mixture models for multi-label text classification. In: 10th New Zealand computer science research student conference

  • Ramage D, Hall D, Nallapati R, Manning CD (2009) Labeled lda: a supervised topic model for credit attribution in multi-labeled corpora. In: EMNLP ’09, pp 248–256

  • Rebouças M, Pinto G, Ebert F, Torres W, Serebrenik A, Castor F (2016) An empirical study on the usage of the swift programming language. In: 2016 IEEE 23rd international conference on software analysis, evolution, and reengineering (SANER), pp 634–638

  • Samaniego FI (2010) A comparison of the bayesian and frequentist approaches to estimation. Series in Statistics, Springer

  • Shokripour R, Anvik J, Kasirun ZM, Zamani S (2013) Why so complicated? Simple term filtering and weighting for location-based bug report assignment recommendation MSR

  • Sigurbjörnsson B, van Zwol R (2008) Flickr tag recommendation based on collective knowledge WWW ’08, pp 327–336

  • Storey M-A, Ryall J, Singer J, Myers D, Cheng L-T, Muller M (2009) How software developers use tagging to support reminding and refinding. IEEE Trans Softw Eng 35(undefined):470–483

    Article  Google Scholar 

  • Storey M-A, Treude C, van Deursen A, Cheng L-T (2010) The impact of social media on software engineering practices and tools. In: FoSER ’10, pp 359–364

  • Thung F, Lo D, Jiang L (2012) Detecting similar applications with collaborative tagging. In: ICSM, pp 600–603

  • Toutanova K, Klein D, Manning CD, Singer Y (2003) Feature-rich part-of-speech tagging with a cyclic dependency network. In: HLT-NAACL

  • Treude C, Storey M-A (2009) How tagging helps bridge the gap between social and technical aspects in software development. In: ICSE ’09, pp 12–22

  • Treude C, Storey M-A (2012) Work item tagging: communicating concerns in collaborative software development. IEEE Trans Softw Eng 38(1):19–34

    Article  Google Scholar 

  • Vasilescu B, Serebrenik A, Devanbu PT, Filkov V (2014) How social Q&A sites are changing knowledge sharing in open source software communities. In: CSCW, pp 342–354

  • Vasilescu B, Serebrenik A, van den Brand MGJ (2013) The babel of software development: linguistic diversity in open source. In: Jatowt A, Lim E-P, Ding Y, Miura A, Tezuka T, Dias G, Tanaka K, Flanagin A, Dai BT (eds) Proceedings of the social informatics: 5th international conference, SocInfo 2013, Kyoto, Japan, November 25-27, 2013. Springer International Publishing, pp 391–404

  • Vogt CC, Cottrell GW (1999) Fusion via a linear combination of scores. Inf Retr 1(3):151–173

    Article  Google Scholar 

  • Wang S, Lo D, Jiang L (2012) Inferring semantically related software terms and their taxonomy by leveraging collaborative tagging. In: ICSM, pp 604–607

  • Wang S, Lo D, Vasilescu B, Serebrenik A (2014) EnTagRec: an enhanced tag recommendation system for software information sites. In: 30th IEEE international conference on software maintenance and evolution, Victoria, BC, Canada, September 29 - October 3, 2014. IEEE Computer Society, pp 291– 300

  • Wang W, Niu N, Liu H, Wu Y (2015) Tagging in assisted tracing. In: 2015 IEEE/ACM 8th international symposium on software and systems traceability, pp 8–14

  • Wang X-Y, Xia X, Lo D (2015) Tagcombine: recommending tags to contents in software information sites. J Comput Sci Technol 30(5):1017–1035

    Article  Google Scholar 

  • Warbox D (2009) Auto-tagging. http://meta.stackoverflow.com/questions/1377/auto-tagging

  • Wilcoxon F (1945) Individual comparisons by ranking methods. Biom Bull 1 (4):80–83

    Article  Google Scholar 

  • Xia X, Lo D, Wang X, Zhou B (2013) Tag recommendation in software information sites. In: MSR ’13, pp 287–296

  • Zangerle E, Gassler W, Specht G (2011) Using tag recommendations to homogenize folksonomies in microblogging environments. In: SocInfo’11, pp 113–126

  • Zubiaga A (2012) Enhancing navigation on wikipedia with social tags. CoRR, arXiv:1202.5469

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Authors and Affiliations

  1. SAIL, Queen’s University, Kingston, Canada

    Shaowei Wang

  2. School of Information Systems, Singapore Management University, Singapore, Singapore

    David Lo

  3. School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA

    Bogdan Vasilescu

  4. Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands

    Alexander Serebrenik

Authors
  1. Shaowei Wang
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  2. David Lo
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  3. Bogdan Vasilescu
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  4. Alexander Serebrenik
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Correspondence to Shaowei Wang.

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Communicated by: Romain Robbes

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Wang, S., Lo, D., Vasilescu, B. et al. EnTagRec ++: An enhanced tag recommendation system for software information sites. Empir Software Eng 23, 800–832 (2018). https://doi.org/10.1007/s10664-017-9533-1

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