The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
A practical guide for using statistical tests to assess randomized algorithms in software engineering
Abstract
Randomized algorithms have been used to successfully address many different types of software engineering problems. This type of algorithms employ a degree of randomness as part of their logic. Randomized algorithms are useful for difficult problems where a precise solution cannot be derived in a deterministic way within reasonable time. However, randomized algorithms produce different results on every run when applied to the same problem instance. It is hence important to assess the effectiveness of randomized algorithms by collecting data from a large enough number of runs. The use of rigorous statistical tests is then essential to provide support to the conclusions derived by analyzing such data. In this paper, we provide a systematic review of the use of randomized algorithms in selected software engineering venues in 2009. Its goal is not to perform a complete survey but to get a representative snapshot of current practice in software engineering research. We show that randomized algorithms are used in a significant percentage of papers but that, in most cases, randomness is not properly accounted for.
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
What's wrong with Bonferroni adjustments
Perneger, T. V. (1998) · BMJ · doi:10.1136/bmj.316.7139.1236 · Cited by 5,900
Systematic Literature Reviews in Software Engineering—A Systematic Literature Review
Kitchenham, B., Pearl Brereton, O., Budgen, D., Turner, M., Bailey, J., & Linkman, S. (2009) · Information and Software Technology · doi:10.1016/j.infsof.2008.09.009 · Cited by 4,537
Ruxton, G. D. (2006) · Behavioral Ecology · doi:10.1093/beheco/ark016 · Cited by 1,988
Fundamentals of Modern Statistical Methods: Substantially Improving Power and Accuracy
Wilcox, R. R. (2010) · doi:10.1007/978-1-4419-5525-8 · Cited by 404
Cited by
Large language models for software engineering: survey and open problems
Fan, A., Gokkaya, B., Harman, M., Lyubarskiy, M., Sengupta, S., Yoo, S., & Zhang, J. M. (2023) · 2023 IEEE/ACM International Conference on Software Engineering: Future of Software Engineering (ICSE-FoSE) · doi:10.1109/icse-fose59343.2023.00008 · Cited by 650
Fraser, G., & Arcuri, A. (2013) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2012.14 · Cited by 586
Self-citationAn Empirical Comparison of Model Validation Techniques for Defect Prediction Models
Tantithamthavorn, C., McIntosh, S., Hassan, A. E., & Matsumoto, K. (2017) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2016.2584050 · Cited by 574
Large Language Models for Software Engineering: Survey and Open Problems
Fan, A., Gokkaya, B., Harman, M., Lyubarskiy, M., Sengupta, S., Yoo, S., & Zhang, J. M. (2023) · arXiv · doi:10.48550/arxiv.2310.03533 · Cited by 523
Fairness testing: testing software for discrimination
Galhotra, S., Brun, Y., & Meliou, A. (2017) · Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering · doi:10.1145/3106237.3106277 · Cited by 405
“Apparent discrimination captures this notion, applying group or causal discrimination score measurement to a subset of the input domain, which can be described by an operational profile [7, 58].”
BackgroundMethodInfluential
Metadata
- Authors
- Arcuri, A., & Briand, L.
- Venue
- Proceedings of the 33rd International Conference on Software Engineering
- Publisher
- ACM
- Published
- 2011-05-21
- Pages
- 1-10
- ISSN
- 0270-5257 (ISSN-L)
- OpenAlex
- W2138428785
- Cited by
- 968
- References
- 4
- FWCI
- 37.02
- 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.
