Reproduce Statistical Analyses and Meta-Analyses

Includes data analysis and meta-analysis functions (e.g., to calculate effect sizes and 95% Confidence Intervals (CI) on Standardised Effect Sizes (d) for AB/BA cross-over repeated-measures experimental designs), data presentation functions (e.g., density curve overlaid on histogram),and the data sets analyzed in different research papers in software engineering (e.g., related to software defect prediction or multi- site experiment concerning the extent to which structured abstracts were clearer and more complete than conventional abstracts) to streamline reproducible research in software engineering.

reproducer

The R package reproducer is aimed to support reproducible research in software engineering. See the package homepage for details and examples.

Installation

One may install the stable version from CRAN:

install.packages('reproducer', dependencies = TRUE)

You can use devtools to install the development version from my web site:

install.packages("devtools", dependencies = T, repos = "https://cran.r-project.org/")
library(devtools)
devtools::install_url("https://madeyski.e-informatyka.pl/download/R/reproducer_0.7.0.tar.gz")
library(reproducer)

Motivation

The motivation is to support using robust statistical methods and reproducible research in software engineering via sharing data sets and code behind the published or just submitted papers.

Status

Citations

If you use reproducer, please cite it:

Lech Madeyski, Barbara Kitchenham, Tomasz Lewowski (2026). reproducer: Reproduce Statistical Analyses and Meta-Analyses. R package version 0.7.0. https://cran.r-project.org/package=reproducer

@Manual{reproducer,
title = {reproducer: Reproduce Statistical Analyses and Meta-Analyses},
author = {Lech Madeyski},
year = {2026},
note = {R package version 0.7.0},
url = {https://cran.r-project.org/package=reproducer} }

Lech Madeyski, Marian Jureczko (2015). Which process metrics can significantly improve defect prediction models? An empirical study. Software Quality Journal, vol. 23, no. 3, pp. 393-422 DOI: 10.1007/s11219-014-9241-7 Online: https://dx.doi.org/10.1007/s11219-014-9241-7

@Article{Madeyski15SQJ,
title = {Which process metrics can significantly improve defect prediction models? An empirical study},
author = {Lech Madeyski and Marian Jureczko},
journal = {Software Quality Journal},
year = {2015},
volume = {23},
number = {3},
pages = {393–422},
doi = {10.1007/s11219-014-9241-7},
url = {https://dx.doi.org/10.1007/s11219-014-9241-7} }

Marian Jureczko, Lech Madeyski (2015). Cross-project defect prediction with respect to code ownership model: An empirical study. e-Informatica Software Engineering Journal, vol. 9, no. 1, pp. 21-35 DOI: 10.5277/e-Inf150102 Online: https://dx.doi.org/10.5277/e-Inf150102

@Article{Jureczko15eInf,
title = {Cross-project defect prediction with respect to code ownership model: An empirical study},
author = {Marian Jureczko and Lech Madeyski},
journal = {e-Informatica Software Engineering Journal},
year = {2015},
volume = {9},
number = {1},
pages = {21–35},
doi = {10.5277/e-Inf150102},
url = {https://dx.doi.org/10.5277/e-Inf150102} }

Barbara A. Kitchenham, Lech Madeyski, David Budgen, Jacky Keung, Pearl Brereton, Stuart Charters, Shirley Gibbs and Amnart Pohthong (2017). Robust Statistical Methods for Empirical Software Engineering. Empirical Software Engineering, vol. 22, no.2, p. 579-630 DOI: 10.1007/s10664-016-9437-5 Online: https://dx.doi.org/10.1007/s10664-016-9437-5

@Article{Kitchenham17ESE,
title = {Robust Statistical Methods for Empirical Software Engineering},
author = {Barbara Kitchenham and Lech Madeyski and David Budgen and Jacky Keung and Pearl Brereton and Stuart Charters and Shirley Gibbs and Amnart Pohthong},
journal = {Empirical Software Engineering},
year = {2017},
volume = {22},
number = {2},
pages = {579–630},
doi = {10.1007/s10664-016-9437-5},
url = {https://dx.doi.org/10.1007/s10664-016-9437-5} }

Lech Madeyski and Barbara Kitchenham (2018) Effect Sizes and their Variance for AB/BA Crossover Design Studies. Empirical Software Engineering, vol. 23, no.4, p. 1982-2017 DOI: 10.1007/s10664-017-9574-5 Online: https://dx.doi.org/10.1007/s10664-017-9574-5

@Article{Madeyski18ESE,
title = {Effect Sizes and their Variance for AB/BA Crossover Design Studies},
author = {Lech Madeyski and Barbara Kitchenham},
journal = {Empirical Software Engineering},
year = {2018},
volume = {23},
number = {4},
pages = {1982–2017},
doi = {10.1007/s10664-017-9574-5},
url = {https://doi.org/10.1007/s10664-017-9574-5} }

Barbara Kitchenham, Lech Madeyski and Pearl Brereton (2020) Meta-analysis for families of experiments in software engineering: a systematic review and reproducibility and validity assessment. Empirical Software Engineering, vol. 25, no.1, p. 353-401 DOI: 10.1007/s10664-019-09747-0 Online: https://dx.doi.org/10.1007/s10664-019-09747-0

@Article{Kitchenham20ESE,
title = {Meta-analysis for families of experiments in software engineering: a systematic review and reproducibility and validity assessment},
author = {Barbara Kitchenham and Lech Madeyski and Pearl Pearl},
journal = {Empirical Software Engineering},
year = {2020},
volume = {25},
number = {1},
pages = {353–401},
doi = {10.1007/s10664-019-09747-0},
url = {https://doi.org/10.1007/s10664-019-09747-0} }

Tomasz Lewowski and Lech Madeyski (2020) Creating Evolving Project Data Sets in Software Engineering vol.851 of Studies in Computational Intelligence, p.1-14, Springer DOI: 10.1007/s10664-019-09747-0 Online: https://dx.doi.org/10.1007/s10664-019-09747-0

@InBook{Lewowski20SCI,
title = {Creating Evolving Project Data Sets in Software Engineering},
booktitle = {Integrating Research and Practice in Software Engineering},
chapter = {Creating Evolving Project Data Sets in Software Engineering},
author = {Tomasz Lewowski and Lech Madeyski},
editor = {Stanislaw Jarzabek and Aneta Poniszewska-Mara{’{n}}da and Lech Madeyski},
year = {2020},
volume = {851},
series = {Studies in Computational Intelligence},
pages = {1–14},
publisher = {Springer},
doi = {10.1007/978-3-030-26574-8_1},
url = {https://doi.org/10.1007/978-3-030-26574-8_1} }

Barbara Kitchenham, Lech Madeyski, Giuseppe Scanniello, and Carmine Gravino (2022) The importance of the correlation in crossover experiments IEEE Transactions on Software Engineering, vol. 48, no.8, p.2802-2813 DOI: 10.1109/TSE.2021.3070480 Online: https://doi.org/10.1109/TSE.2021.3070480

@Article{Kitchenham22TSE,
title = {The importance of the correlation in crossover experiments},
author = {Barbara Kitchenham and Lech Madeyski and Giuseppe Scanniello and Carmine Gravino},
journal = {IEEE Transactions on Software Engineering},
year = {2022},
volume = {48},
number = {8},
pages = {2802–2813},
doi = {10.1109/TSE.2021.3070480},
url = {https://doi.org/10.1109/TSE.2021.3070480} }