The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Automatic Source Code Summarization of Context for Java Methods
Abstract
Source code summarization is the task of creating readable summaries that describe the functionality of software. Source code summarization is a critical component of documentation generation, for example as Javadocs formed from short paragraphs attached to each method in a Java program. At present, a majority of source code summarization is manual, in that the paragraphs are written by human experts. However, new automated technologies are becoming feasible. These automated techniques have been shown to be effective in select situations, though a key weakness is that they do not explain the source code's context. That is, they can describe the behavior of a Java method, but not why the method exists or what role it plays in the software. In this paper, we propose a source code summarization technique that writes English descriptions of Java methods by analyzing how those methods are invoked. We then performed two user studies to evaluate our approach. First, we compared our generated summaries to summaries written manually by experts. Then, we compared our summaries to summaries written by a state-of-the-art automatic summarization tool. We found that while our approach does not reach the quality of human-written summaries, we do improve over the state-of-the-art summarization tool in several dimensions by a statistically-significant margin.
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Cites
Introduction to information retrieval
(2009) · Choice Reviews Online · doi:10.5860/choice.46-2715 · Cited by 12,278
Introduction to Information Retrieval
Manning, C. D., Raghavan, P., & Schütze, H. (2008) · doi:10.1017/cbo9780511809071 · Cited by 11,288
Google's PageRank and Beyond: The Science of Search Engine Rankings
Langville, A. N., & Meyer, C. D. (2006) · doi:10.1515/9781400830329 · Cited by 814
Towards automatically generating summary comments for Java methods
Sridhara, G., Hill, E., Muppaneni, D., Pollock, L., & Vijay-Shanker, K. (2010) · Proceedings of the IEEE/ACM international conference on Automated software engineering · doi:10.1145/1858996.1859006 · Cited by 514
The relevance of software documentation, tools and technologies: a survey
Forward, A., & Lethbridge, T. C. (2002) · Proceedings of the 2002 ACM symposium on Document engineering · doi:10.1145/585058.585065 · Cited by 268
Automatically documenting program changes
Buse, R. P. L., & Weimer, W. R. (2010) · Proceedings of the IEEE/ACM international conference on Automated software engineering · doi:10.1145/1858996.1859005 · Cited by 219
Stacksplorer: call graph navigation helps increasing code maintenance efficiency
Karrer, T., Krämer, J.-P., Diehl, J., Hartmann, B., & Borchers, J. (2011) · Proceedings of the 24th annual ACM symposium on User interface software and technology · doi:10.1145/2047196.2047225 · Cited by 48
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Self-citationA Neural Model for Generating Natural Language Summaries of Program Subroutines
LeClair, A., Jiang, S., & McMillan, C. (2019) · 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) · doi:10.1109/icse.2019.00087 · Cited by 294
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Aghajani, E., Nagy, C., Vega-Marquez, O. L., Linares-Vasquez, M., Moreno, L., Bavota, G., & Lanza, M. (2019) · 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) · doi:10.1109/icse.2019.00122 · Cited by 195
Semantic similarity metrics for evaluating source code summarization
Haque, S., Eberhart, Z., Bansal, A., & McMillan, C. (2022) · Proceedings of the 30th IEEE/ACM International Conference on Program Comprehension · doi:10.1145/3524610.3527909 · Cited by 116
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Metadata
- Authors
- McBurney, P. W., & McMillan, C.
- Venue
- IEEE Transactions on Software Engineering
- Published
- 2016-02-01
- Volume
- 42
- Issue
- 2
- Pages
- 103-119
- ISSN
- 0098-5589 (ISSN-L)
- 1939-3520
- OpenAlex
- W2294980783
- Cited by
- 187
- References
- 7
- FWCI
- 23.42
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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