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Labeling source code with information retrieval methods: an empirical study

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Abstract

To support program comprehension, software artifacts can be labeled—for example within software visualization tools—with a set of representative words, hereby referred to as labels. Such labels can be obtained using various approaches, including Information Retrieval (IR) methods or other simple heuristics. They provide a bird-eye’s view of the source code, allowing developers to look over software components fast and make more informed decisions on which parts of the source code they need to analyze in detail. However, few empirical studies have been conducted to verify whether the extracted labels make sense to software developers. This paper investigates (i) to what extent various IR techniques and other simple heuristics overlap with (and differ from) labeling performed by humans; (ii) what kinds of source code terms do humans use when labeling software artifacts; and (iii) what factors—in particular what characteristics of the artifacts to be labeled—influence the performance of automatic labeling techniques. We conducted two experiments in which we asked a group of students (38 in total) to label 20 classes from two Java software systems, JHotDraw and eXVantage. Then, we analyzed to what extent the words identified with an automated technique—including Vector Space Models, Latent Semantic Indexing (LSI), latent Dirichlet allocation (LDA), as well as customized heuristics extracting words from specific source code elements—overlap with those identified by humans. Results indicate that, in most cases, simpler automatic labeling techniques—based on the use of words extracted from class and method names as well as from class comments—better reflect human-based labeling. Indeed, clustering-based approaches (LSI and LDA) are more worthwhile to be used for source code artifacts having a high verbosity, as well as for artifacts requiring more effort to be manually labeled. The obtained results help to define guidelines on how to build effective automatic labeling techniques, and provide some insights on the actual usefulness of automatic labeling techniques during program comprehension tasks.

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Notes

  1. http://www.jhotdraw.org/

  2. http://www.research.avayalabs.com/

  3. http://distat.unimol.it/reports/labeling/

  4. http://www.jhotdraw.org

  5. http://www.research.avayalabs.com

  6. The number of unique terms ranges from 26 to 186, while the number of documents, i.e., methods, from 4 to 37.

  7. Note that both LSI and LDA were used in the same way by other authors to support different software engineering tasks. For instance, both the techniques have been applied at class level when computing class cohesion/coupling exhibiting good results (Liu et al. 2009; Marcus and Poshyvanyk 2005; Poshyvanyk and Marcus 2006).

  8. Note that in our case the asymmetric Jaccard overlap coincides with the precision measure (Baeza-Yates and Ribeiro-Neto 1999). Assuming that K(C i ) represents the set of “correct” keywords, the overlap measures the number of identified keywords that are actually correct, i.e., precision.

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Acknowledgements

We would like to thank all the students that participated in our study. We would also like to thank anonymous reviewers for their careful reading of our manuscript and high-quality feedback. Their detailed comments have helped us to improve the original version of this paper.

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Authors and Affiliations

  1. Software Engineering Lab, University of Salerno, Via Ponte don Melillo, 84084, Fisciano (SA), Italy

    Andrea De Lucia & Annibale Panichella

  2. RCOST, University of Sannio, Palazzo ex Poste, Viale Traiano, 82100, Benevento, Italy

    Massimiliano Di Penta & Sebastiano Panichella

  3. Department of Bioscience and Territory, University of Molise, C.da Fonte Lappone, 86090, Pesche (IS), Italy

    Rocco Oliveto

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  1. Andrea De Lucia
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  5. Sebastiano Panichella
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Correspondence to Rocco Oliveto.

Additional information

Communicated By: Michael Godfrey and Arie van Deursen

This paper is an extension of the work “Using IR Methods for Labeling Source Code Artifacts: Is It Worthwhile?” appeared in the Proceedings of the 20th IEEE International Conference on Program Comprehension, Passau, Bavaria, Germany, pp. 193–202, 2012. IEEE Press.

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De Lucia, A., Di Penta, M., Oliveto, R. et al. Labeling source code with information retrieval methods: an empirical study. Empir Software Eng 19, 1383–1420 (2014). https://doi.org/10.1007/s10664-013-9285-5

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