The DOI matches its Crossref record.
Using structural context to recommend source code examples
Open accessAbstract
When coding to a framework, developers often become stuck, unsure of which class to subclass, which objects to instantiate and which methods to call. Example code that demonstrates the use of the framework can help developers make progress on their task. In this paper, we describe an approach for locating relevant code in an example repository that is based on heuristically matching the structure of the code under development to the example code. Our tool improves on existing approaches in two ways. First, the structural context needed to query the repository is extracted automatically from the code, freeing the developer from learning a query language or from writing their code in a particular style. Second, the repository can be generated easily from existing applications. We demonstrate the utility of this approach by reporting on a case study involving two subjects completing four programming tasks within the Eclipse integrated development environment framework.
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Cited by
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Brandt, J., Dontcheva, M., Weskamp, M., & Klemmer, S. R. (2010) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/1753326.1753402 · Cited by 324
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de Alwis, B., & Murphy, G. C. (2008) · Proceedings of the 30th international conference on Software engineering · doi:10.1145/1368088.1368092 · Cited by 63
Self-citationEvaluating how developers use general-purpose web-search for code retrieval
Rahman, M. M., Barson, J., Paul, S., Kayani, J., Lois, F. A., Quezada, S. F., Parnin, C., Stolee, K. T., & Ray, B. (2018) · Proceedings of the 15th International Conference on Mining Software Repositories · doi:10.1145/3196398.3196425 · Cited by 62
Significant Productivity Gains through Programming with Large Language Models
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“…and industry have explored different mechanisms how these suggestions and code completions can be generated, from semantic analysis of the source code [3, 22, 28, 48, 49] to machine learning approaches [43, 57] Programming with Large Language Models 256:3 and different combinations [7, 47].”
Supporting Software Developers with a Holistic Recommender System
Ponzanelli, L., Scalabrino, S., Bavota, G., Mocci, A., Oliveto, R., Di Penta, M., & Lanza, M. (2017) · 2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE) · doi:10.1109/icse.2017.17 · Cited by 41
Active Inductive Logic Programming for Code Search
Sivaraman, A., Zhang, T., Van den Broeck, G., & Kim, M. (2019) · 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) · doi:10.1109/icse.2019.00044 · Cited by 34
Evaluating the evaluations of code recommender systems: a reality check
Proksch, S., Amann, S., Nadi, S., & Mezini, M. (2016) · Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering · doi:10.1145/2970276.2970330 · Cited by 26
Towards Understanding Successful Novice Example Use in Blocks-Based Programming
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Metadata
- Authors
- Holmes, R., & Murphy, G. C.
- Venue
- Proceedings of the 27th international conference on Software engineering - ICSE '05
- Publisher
- ACM Press
- Published
- 2005
- Pages
- 117
- ISSN
- 0270-5257 (ISSN-L)
- OpenAlex
- W2169952536
- Cited by
- 379
- References
- 0
- Type
- conference abstract
- Language
- en
- Open access
- green
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