The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Boa: A language and infrastructure for analyzing ultra-large-scale software repositories
Abstract
In today's software-centric world, ultra-large-scale software repositories, e.g. SourceForge (350,000+ projects), GitHub (250,000+ projects), and Google Code (250,000+ projects) are the new library of Alexandria. They contain an enormous corpus of software and information about software. Scientists and engineers alike are interested in analyzing this wealth of information both for curiosity as well as for testing important hypotheses. However, systematic extraction of relevant data from these repositories and analysis of such data for testing hypotheses is hard, and best left for mining software repository (MSR) experts! The goal of Boa, a domain-specific language and infrastructure described here, is to ease testing MSR-related hypotheses. We have implemented Boa and provide a web-based interface to Boa's infrastructure. Our evaluation demonstrates that Boa substantially reduces programming efforts, thus lowering the barrier to entry. We also see drastic improvements in scalability. Last but not least, reproducing an experiment conducted using Boa is just a matter of re-running small Boa programs provided by previous researchers.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Awareness and coordination in shared workspaces
Dourish, P., & Bellotti, V. (1992) · Proceedings of the 1992 ACM conference on Computer-supported cooperative work - CSCW '92 · doi:10.1145/143457.143468 · Cited by 2,550 · cited 1×
“Scientists and engineers alike are interested in analyzing this wealth of information both for curiosity as well as for testing such important hypotheses as: “how people perceive and consider the potential impacts of their own and others’ edits as they write together?”
Background
Cited by
ATOM: Commit Message Generation Based on Abstract Syntax Tree and Hybrid Ranking
Liu, S., Gao, C., Chen, S., Nie, L. Y., & Liu, Y. (2022) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2020.3038681 · Cited by 93
CCT5: A Code-Change-Oriented Pre-trained Model
Lin, B., Wang, S., Liu, Z., Liu, Y., Xia, X., & Mao, X. (2023) · Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering · doi:10.1145/3611643.3616339 · Cited by 69
FIRA: fi ne-grained g ra ph-based code change representation for automated commit message generation
Dong, J., Lou, Y., Zhu, Q., Sun, Z., Li, Z., Zhang, W., & Hao, D. (2022) · Proceedings of the 44th International Conference on Software Engineering · doi:10.1145/3510003.3510069 · Cited by 61
Ma, Y., Dey, T., Bogart, C., Amreen, S., Valiev, M., Tutko, A., Kennard, D., Zaretzki, R., & Mockus, A. (2021) · Empirical Software Engineering · doi:10.1007/s10664-020-09905-9 · Cited by 49
RACE: Retrieval-augmented Commit Message Generation
Shi, E., Wang, Y., Tao, W., Du, L., Zhang, H., Han, S., Zhang, D., & Sun, H. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.372 · Cited by 42
Software provenance tracking at the scale of public source code
Rousseau, G., Di Cosmo, R., & Zacchiroli, S. (2020) · Empirical Software Engineering · doi:10.1007/s10664-020-09828-5 · Cited by 37
GrimoireLab: A toolset for software development analytics
Dueñas, S., Cosentino, V., Gonzalez-Barahona, J. M., del Castillo San Felix, A., Izquierdo-Cortazar, D., Cañas-Díaz, L., & Pérez García-Plaza, A. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.601 · Cited by 33
The Software Heritage Open Science Ecosystem
Cosmo, R. D., & Zacchiroli, S. (2023) · Software Ecosystems · doi:10.1007/978-3-031-36060-2_2 · Cited by 5
JEMMA: An extensible Java dataset for ML4Code applications
Karmakar, A., Allamanis, M., & Robbes, R. (2023) · Empirical Software Engineering · doi:10.1007/s10664-022-10275-7 · Cited by 4
Metadata
- Authors
- Dyer, R., Nguyen, H. A., Rajan, H., & Nguyen, T. N.
- Venue
- 2013 35th International Conference on Software Engineering (ICSE)
- Publisher
- IEEE
- Published
- 2013-05
- Pages
- 422-431
- ISSN
- 0270-5257 (ISSN-L)
- OpenAlex
- W4232691406
- Cited by
- 255
- References
- 1
- FWCI
- 11.57
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
