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Semantically-enhanced topic recommendation systems for software projects

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Abstract

Software-related platforms such as GitHub and Stack Overflow, have enabled their users to collaboratively label software entities with a form of metadata called topics. Tagging software repositories with relevant topics can be exploited for facilitating various downstream tasks. For instance, a correct and complete set of topics assigned to a repository can increase its visibility. Consequently, this improves the outcome of tasks such as browsing, searching, navigation, and organization of repositories. Unfortunately, assigned topics are usually highly noisy, and some repositories do not have well-assigned topics. Thus, there have been efforts on recommending topics for software projects, however, the semantic relationships among these topics have not been exploited so far. In this work, we propose two recommender models for tagging software projects that incorporate the semantic relationship among topics. Our approach has two main phases; (1) we first take a collaborative approach to curate a dataset of quality topics specifically for the domain of software engineering and development. We also enrich this data with the semantic relationships among these topics and encapsulate them in a knowledge graph we call SED-KGraph. Then, (2) we build two recommender systems; The first one operates only based on the list of original topics assigned to a repository and the relationships specified in our knowledge graph. The second predictive model, however, assumes there are no topics available for a repository, hence it proceeds to predict the relevant topics based on both textual information of a software project (such as its README file), and SED-KGraph. We built SED-KGraph in a crowd-sourced project with 170 contributors from both academia and industry. Through their contributions, we constructed SED-KGraph with 2,234 carefully evaluated relationships among 863 community-curated topics. Regarding the recommenders’ performance, the experiment results indicate that our solutions outperform baselines that neglect the semantic relationships among topics by at least 25% and 23% in terms of Average Success Rate and Mean Average Precision metrics, respectively. We share SED-KGraph, as a rich form of knowledge for the community to re-use and build upon. We also release the source code of our two recommender models, KGRec and KGRec+ (https://github.com/mahtab-nejati/KGRec).

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Data Availability

The dataset generated during the current study is available in the authors’ public GitHub repository.Footnote 11 The dataset used for training the ML-based components and comparing approaches is also available in the baseline paper’s public GitHub repository.Footnote 12

Notes

  1. January 2022, https://github.com/search

  2. https://angular.io/

  3. https://github.com/github/explore

  4. https://github.com/mahtab-nejati/KGRec

  5. https://github.com/MalihehIzadi/SoftwareTagRecommender

  6. https://github.com/MalihehIzadi/SoftwareTagRecommender

  7. To access the platform, please refer to our public GitHub repository at https://github.com/mahtab-nejati/KGRec.

  8. https://tedboy.github.io/nlps/generated/generated/nltk.edit_distance.html

  9. For more samples please refer to Appendix B

  10. https://github.com/mahtab-nejati/KGRec

  11. https://github.com/mahtab-nejati/KGRec

  12. https://github.com/MalihehIzadi/SoftwareTagRecommender

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Acknowledgements

We would like to thank all the participants for helping us with constructing and evaluating our KG, as well as for assessing our recommender model.

Author information

Authors and Affiliations

  1. TU Delft, Delft, Netherlands

    Maliheh Izadi

  2. University of Waterloo, Waterloo, Ontario, Canada

    Mahtab Nejati

  3. Bowling Green State University, Bowling Green, Ohio, USA

    Abbas Heydarnoori

Authors
  1. Maliheh Izadi
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  2. Mahtab Nejati
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  3. Abbas Heydarnoori
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Corresponding authors

Correspondence to Maliheh Izadi or Abbas Heydarnoori.

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Communicated by: Sousuke Amasaki, Xin Xia, Shane McIntosh

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This article belongs to the Topical Collection: Predictive Models and Data Analytics in Software Engineering (PROMISE)

Appendices

Appendix A: Platform Screenshots

Figures 6 and 7 present multiple screenshots of our online platform for both the construction and maintenance phases.

Fig. 6
figure 6

Platform dashboard and review panel for KG construction phase

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Fig. 7
figure 7

KG entities in the maintenance phase

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Appendix B: Samples

Table 9 presents several samples per relation type from SED-KGraph.

Table 9 Samples per relation types
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Izadi, M., Nejati, M. & Heydarnoori, A. Semantically-enhanced topic recommendation systems for software projects. Empir Software Eng 28, 50 (2023). https://doi.org/10.1007/s10664-022-10272-w

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