The DOI matches its Crossref record.
Recommendation Systems for Software Engineering
Abstract
Software development can be challenging because of the large information spaces that developers must navigate. Without assistance, developers can become bogged down and spend a disproportionate amount of their time seeking information at the expense of other value-producing tasks. Recommendation systems for software engineering (RSSEs) are software tools that can assist developers with a wide range of activities, from reusing code to writing effective bug reports. The authors provide an overview of recommendation systems for software engineering: what they are, what they can do for developers, and what they might do in the future.
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Citations
Cited by
An empirical evaluation of GitHub copilot's code suggestions
Nguyen, N., & Nadi, S. (2022) · Proceedings of the 19th International Conference on Mining Software Repositories · doi:10.1145/3524842.3528470 · Cited by 313
Mining StackOverflow to turn the IDE into a self-confident programming prompter
Ponzanelli, L., Bavota, G., Di Penta, M., Oliveto, R., & Lanza, M. (2014) · Proceedings of the 11th Working Conference on Mining Software Repositories · doi:10.1145/2597073.2597077 · Cited by 240
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Meyer, A. N., Barr, E. T., Bird, C., & Zimmermann, T. (2021) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2019.2904957 · Cited by 123
Self-citationSeahawk: Stack Overflow in the IDE
Ponzanelli, L., Bacchelli, A., & Lanza, M. (2013) · 2013 35th International Conference on Software Engineering (ICSE) · doi:10.1109/icse.2013.6606701 · Cited by 95
A Survey of Machine Learning for Big Code and Naturalness
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An Information Foraging Theory Perspective on Tools for Debugging, Refactoring, and Reuse Tasks
Fleming, S. D., Scaffidi, C., Piorkowski, D., Burnett, M., Bellamy, R., Lawrance, J., & Kwan, I. (2013) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/2430545.2430551 · Cited by 79
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Harnessing Stack Overflow for the IDE
Bacchelli, A., Ponzanelli, L., & Lanza, M. (2012) · 2012 Third International Workshop on Recommendation Systems for Software Engineering (RSSE) · doi:10.1109/rsse.2012.6233404 · Cited by 60
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
Unveiling ChatGPT’s usage in open source projects: A mining-based study
Tufano, R., Mastropaolo, A., Pepe, F., Dabic, O., Di Penta, M., & Bavota, G. (2024) · Proceedings of the 21st International Conference on Mining Software Repositories · doi:10.1145/3643991.3644918 · Cited by 38
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Terra, R., Valente, M. T., Czarnecki, K., & Bigonha, R. S. (2015) · Software: Practice and Experience · doi:10.1002/spe.2228 · Cited by 37
Zhou, X., Liang, P., Zhang, B., Li, Z., Ahmad, A., Shahin, M., & Waseem, M. (2025) · Journal of Systems and Software · doi:10.1016/j.jss.2024.112204 · Cited by 30
“Several studies (e.g., Luan et al. (2019), Robillard et al. (2010)) and software products (e.g., Eclipse Code Recommenders (Eclipse, 2019)) have been dedicated to improving the efficiency of developers through the development of systems that can recommend and generate code.”
BackgroundPolitowski, C., Fontoura, L. M., Petrillo, F., & Guéhéneuc, Y.-G. (2018) · Information and Software Technology · doi:10.1016/j.infsof.2018.04.003 · Cited by 17
Metadata
- Authors
- Robillard, M., Walker, R., & Zimmermann, T.
- Venue
- IEEE Software
- Published
- 2010-07
- Volume
- 27
- Issue
- 4
- Pages
- 80-86
- ISSN
- 0740-7459 (ISSN-L)
- OpenAlex
- W2156448859
- Cited by
- 388
- References
- 0
- FWCI
- 43.25
- Type
- journal article
- Language
- en
- Open access
- closed
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