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A survey on the use of topic models when mining software repositories

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Abstract

Researchers in software engineering have attempted to improve software development by mining and analyzing software repositories. Since the majority of the software engineering data is unstructured, researchers have applied Information Retrieval (IR) techniques to help software development. The recent advances of IR, especially statistical topic models, have helped make sense of unstructured data in software repositories even more. However, even though there are hundreds of studies on applying topic models to software repositories, there is no study that shows how the models are used in the software engineering research community, and which software engineering tasks are being supported through topic models. Moreover, since the performance of these topic models is directly related to the model parameters and usage, knowing how researchers use the topic models may also help future studies make optimal use of such models. Thus, we surveyed 167 articles from the software engineering literature that make use of topic models. We find that i) most studies centre around a limited number of software engineering tasks; ii) most studies use only basic topic models; iii) and researchers usually treat topic models as black boxes without fully exploring their underlying assumptions and parameter values. Our paper provides a starting point for new researchers who are interested in using topic models, and may help new researchers and practitioners determine how to best apply topic models to a particular software engineering task.

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Notes

  1. The creators of LSI call these reduced dimensions “concepts”, not “topics”. However, to be consistent with other topic modeling approaches, we will use the term “topics”.

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  1. Software Analysis and Intelligence Lab (SAIL), Queen’s University, Kingston, ON, Canada

    Tse-Hsun Chen, Stephen W. Thomas & Ahmed E. Hassan

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Appendices

Appendix A: Article Selection Process

In this paper, we are interested in locating articles that use topic modeling techniques to solve SE tasks. We focus our attention on articles written between December 1999 and December 2014, more than a full decade of research results. We consider in our search a wide range of journals and conferences in an effort to cover as many relevant articles as possible.

To select our list of articles, we use first compile a list of highly relevant venues to search. Then, we perform a series of keyword searches at each venue, producing a list of candidate articles. For each of the candidate articles, we read the abstract (and, in some cases, the introduction) of the article to determine if the article is indeed relevant to our interests. This yields an initial set of related articles. For each of the articles in the initial set, we consider the citations that are contained in the article for additional relevant articles. Then, we reach our final set of articles.

1.1 A.1 Considered Venues

Table 4 lists the journals and conference venues that we included in our initial search for articles.

Table 4 The fifteen venues that we considered in our initial article selection process
Full size table

1.2 A.2 Keyword Searches and Filtering

We collected the initial set of articles by performing keyword searches at the publisher websites for each of our considered venues. We also searched using aggregate search engines, such as the ACM Digital Library and IEEE Xplore. The keywords and search queries that we use are listed below.

1.3 IEEE Xplore

("topic models" OR "topic model" OR "lsi" OR "lda" OR "plsi" OR "latent dirichlet allocation" OR "latent semantic") AND ("Publication Title":"Source Code Analysis and Manipulation" OR "Publication Title":"Software Engineering, IEEE Transactions on" OR "Publication Title":"Reverse Engineering" OR "Publication Title":"Software Maintenance" OR "Publication Title":"Software Engineering" OR "Publication Title":"Program Comprehension" OR "Publication Title":"Mining Software Repositories")

1.4 Software Practice and Experience

lsi or lda or "topic model" or "topic models" or "latent dirichlet allocation" or "latent semantic" AND publication title="Software Practice and Experience"

1.5 Journal of Software Maintenance and Evolution

lsi or lda or "topic model" or "topic models" or "latent dirichlet allocation" or "latent semantic" AND publication title="Software Maintenance and Evolution"

1.6 Empirical Software Engineering

lsi or lda or "topic model" or "topic models" or latent dirichlet allocation" or "latent semantic"

In general, we found that keyword searches resulted in many irrelevant results. We manually filtered the search results by reading the article’s abstract (and sometimes introduction) to determine if the article solved an SE task by employing one or more topic modeling technique. The articles that were determined to be relevant were added to our initial set.

1.7 A.3 Reference Checking

For each article in the initial set, we followed its citations to obtain another list of potentially relevant articles. Again, we filtered this list by reading the abstract and introduction. The articles that were determined to relevant were added to our final set of articles.

1.8 A.4 Article Selection Results

We finally arrive at 167 articles published between 1999 and 2014. Figure 7 shows the distribution of venues and years for the articles.

Fig. 7
figure 7

Article selection results

Full size image

Appendix B: Article Characterization Results for Facets 1–6

Table 5 Article characterization results of facets 1–4
Full size table
Table 6 Article characterization results of facets 5 and 6
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Chen, TH., Thomas, S.W. & Hassan, A.E. A survey on the use of topic models when mining software repositories. Empir Software Eng 21, 1843–1919 (2016). https://doi.org/10.1007/s10664-015-9402-8

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