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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_0.4.0.tar.gz")
library(reproducer)

Motivation

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

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Version

Install

install.packages('reproducer')

Monthly Downloads

223

Version

0.4.0

License

GPL (>= 2)

Maintainer

Lech Madeyski

Last Published

November 4th, 2020

Functions in reproducer (0.4.0)

CalculateLevel2ExperimentRData

CalculateLevel2ExperimentRData

Ciolkowski09ESEM.MetaAnalysis.PBRvsCBRorAR

Ciolkowski09ESEM.MetaAnalysis.PBRvsCBRorAR data form a set of primary studies on reading methods for software inspections. They were reported and analysed by M. Ciolkowski ("What do we know about perspective-based reading? an approach for quantitative aggregation in software engineering", in Proceedings of the 3rd International Symposium on Empirical Software Engineering and Measurement, ESEM'09, pp. 133-144, IEEE Computer Society, 2009), corrected and re-analysed by Madeyski and Kitchenham ("How variations in experimental designs impact the construction of comparable effect sizes for meta-analysis" (to be submitted)).

ExtractSummaryStatisticsRandomizedExp

ExtractSummaryStatisticsRandomizedExp

ExtractExperimentData

ExtractExperimentData

ExtractMAStatistics

ExtractMAStatistics

ExtractGroupSizeData

ExtractGroupSizeData

CalculateRLevel1

CalculateRLevel1

ConstructLevel1ExperimentRData

ConstructLevel1ExperimentRData

KitchenhamEtAl.CorrelationsAmongParticipants.Abrahao13TSE

KitchenhamEtAl.CorrelationsAmongParticipants.Abrahao13TSE data illustrate correlations between results from individual participants in a family of five cross-over experiments conducted by Abrahao et al: [1] S. Abrahao, C. Gravino, E. Insfran Pelozo, G. Scanniello, and G. Tortora, "Assessing the effectiveness of sequence diagrams in the comprehension of functional requirements: Results from a family of five experiments," IEEE Transactions on Software Engineering, vol. 39, no. 3, pp. 327<U+2013>342, March 2013 The five experiments assess whether the comprehensibility of function requirements improve when software models include UML sequence diagrams. If you use this data set please cite: [1] S. Abrahao, C. Gravino, E. Insfran Pelozo, G. Scanniello, and G. Tortora, "Assessing the effectiveness of sequence diagrams in the comprehension of functional requirements: Results from a family of five experiments," IEEE Transactions on Software Engineering, vol. 39, no. 3, pp. 327<U+2013>342, March 2013 [2] Barbara Kitchenham, Lech Madeyski, Giuseppe Scanniello and Carmine Gravino, "The importance of the Correlation between Results from Individual Participants in Crossover Experiments" (to be submitted as of 2020).

KitchenhamEtAl.CorrelationsAmongParticipants.Gravino15JVLC

KitchenhamEtAl.CorrelationsAmongParticipants.Gravino15JVLC data illustrate correlations between results from individual participants in a family of 2 cross-over experiments conducted by Gravino et al.: [1] C. Gravino, G. Scanniello, and G. Tortora, "Source-code comprehension tasks supported by UML design models: Results from a controlled experiment and a differentiated replication," Journal of Visual Languages and Computing, vol. 28, pp. 23<U+2013>38, 2015. The experiments assess whether the comprehension of object oriented source-code increases used with UML class and sequence diagrams produced in the software design phase. If you use this data set please cite: [1] C. Gravino, G. Scanniello, and G. Tortora, "Source-code comprehension tasks supported by UML design models: Results from a controlled experiment and a differentiated replication," Journal of Visual Languages and Computing, vol. 28, pp. 23<U+2013>38, 2015. [2] Barbara Kitchenham, Lech Madeyski, Giuseppe Scanniello and Carmine Gravino, "The importance of the Correlation between Results from Individual Participants in Crossover Experiments" (to be submitted as of 2020).

KitchenhamEtAl.CorrelationsAmongParticipants.Scanniello14JVLC

KitchenhamEtAl.CorrelationsAmongParticipants.Scanniello14JVLC data illustrate correlations between results from individual participants in a cross-over experiment conducted by Scanniello and Erra: [1] G. Scanniello and U. Erra, "Distributed modeling of use case diagrams with a method based on think-pair-square: Results from two controlled experiments", Journal of Visual Languages and Computing, vol. 25, no. 4, pp. 494<U+2013>517, 2014. The experiment investigated whether a new method based on think-pair-square and its implementation in a integrated communication/modeling environment (TPS approach) is as effective as traditional face-to-face (F2F approach) for requirements elicitation. The experiment was performed in two stages using different software systems. If you use this data set please cite: [1] G. Scanniello and U. Erra, "Distributed modeling of use case diagrams with a method based on think-pair-square: Results from two controlled experiments,<U+201D> Journal of Visual Languages and Computing, vol. 25, no. 4, pp. 494<U+2013>517, 2014. [2] Barbara Kitchenham, Lech Madeyski, Giuseppe Scanniello and Carmine Gravino, "The Importance of the Correlation between Results from Individual Participants in Crossover Experiments" (to be submitted as of 2020).

KitchenhamEtAl.CorrelationsAmongParticipants.Ricca14TOSEM

KitchenhamEtAl.CorrelationsAmongParticipants.Ricca14TOSEM data illustrate correlations between results from individual participants in a family of three of four cross-over experiments conducted by Ricca et al: [1] F. Ricca, G. Scanniello, M. Torchiano, G. Reggio, and E. Astesiano, "Assessing the effect of screen mockups on the comprehension of functional requirements," ACM Transactions on Software Engineering and Methodology, vol. 24, no. 1, pp. 1:1<U+2013>1:38, Oct. 2014. The goal of the study was to assess whether stakeholders benefit from the presence of screen mock-ups in the comprehension of functional requirements represented with use cases. [2] Barbara Kitchenham, Lech Madeyski, Giuseppe Scanniello and Carmine Gravino, "The importance of the Correlation between Results from Individual Participants in Crossover Experiments" (to be submitted as of 2020).

KitchenhamEtAl.CorrelationsAmongParticipants.Ricca10TSE

KitchenhamEtAl.CorrelationsAmongParticipants.Ricca10TSE data illustrate correlations between results from individual participants in a family of four cross-over experiments conducted by Ricca et al.: [1] F. Ricca, M. D. Penta, M. Torchiano, P. Tonella, and M. Ceccato "How developers<U+2019> experience and ability influence web application comprehension tasks supported by uml stereotypes: A series of four experiments", IEEE Transactions on Software Engineering, vol. 36, no. 1, pp. 96-118, 2010. Although we present the full data set, only the first two experiments were used in the correlation study, because many of the observations in the final two studies were unpaired. The experiments assess whether participants performance comprehension tasks better when using source code complemented by standard UML diagrams (UML) or by diagrams stereotyped using the Conallen notation (Conallen). If you use this data set please cite: [1] F. Ricca, M. D. Penta, M. Torchiano, P. Tonella, and M. Ceccato "How developers<U+2019> experience and ability influence web application comprehension tasks supported by uml stereotypes: A series of four experiments", IEEE Transactions on Software Engineering, vol. 36, no. 1, pp. 96<U+2014>118, 2010. [2] Barbara Kitchenham, Lech Madeyski, Giuseppe Scanniello and Carmine Gravino, "The Importance of the Correlation between Results from Individual Participants in Crossover Experiments" (to be submitted as of 2020).

KitchenhamEtAl.CorrelationsAmongParticipants.Madeyski10

KitchenhamEtAl.CorrelationsAmongParticipants.Madeyski10 data illustrate correlations between results from individual participants in cross-over experiment P2007 (Smell\&Library) conducted by Madeyski, see: [1] Lech Madeyski, Test-Driven Development: An Empirical Evaluation of Agile Practice. (Heidelberg, London, New York): Springer, 2010. Foreword by Prof. Claes Wohlin. If you use this data set please cite: [1] Lech Madeyski, Test-Driven Development: An Empirical Evaluation of Agile Practice. (Heidelberg, London, New York): Springer, 2010. Foreword by Prof. Claes Wohlin. [2] Barbara Kitchenham, Lech Madeyski, Giuseppe Scanniello and Carmine Gravino, "The importance of the Correlation between Results from Individual Participants in Crossover Experiments" (to be submitted as of 2020).

KitchenhamEtAl.CorrelationsAmongParticipants.Reggio15SSM

KitchenhamEtAl.CorrelationsAmongParticipants.Reggio15SSM data illustrate correlations between results from individual participants in a family of two cross-over experiments conducted by Reggio et al: [1] G. Reggio, F. Ricca, G. Scanniello, F. D. Cerbo, and G. Dodero,"On the comprehension of workflows modeled with a precise style: results from a family of controlled experiments". Software and Systems Modeling, vol. 14, pp. 1481<U+2013>1504, 2015. The experiments assess whether the level of formality/precision in workflow model influences comprehension. If you use this data set please cite: [1] G. Reggio, F. Ricca, G. Scanniello, F. D. Cerbo, and G. Dodero, "On the comprehension of workflows modeled with a precise style: results from a family of controlled experiments". Software and Systems Modeling, vol. 14, pp. 1481<U+2013>1504, 2015. [2] Barbara Kitchenham, Lech Madeyski, Giuseppe Scanniello and Carmine Gravino, "The Importance of the Correlation between Results from Individual Participants in Crossover Experiments" (to be submitted as of 2020).

KitchenhamMadeyskiBrereton.ABBAMetaAnalysisReportedResults

KitchenhamMadeyskiBrereton.ABBAMetaAnalysisReportedResults data

KitchenhamMadeyskiBrereton.DocData

KitchenhamMadeyskiBrereton.DocData data

KitchenhamEtAl.CorrelationsAmongParticipants.Romano18ESEM

KitchenhamEtAl.CorrelationsAmongParticipants.Romano18ESEM data illustrate correlations between results from individual participants in a cross-over experiment conducted by Romano et al.: [1] S. Romano, G. Scanniello, D. Fucci, N. Juristo, and B. Turhan, "The effect of noise on software engineers<U+2019> performance", in Proceedings of the 12th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, ser. ESEM'18, 2018. The experiments assess whether noise has an impact on the performance of software engineers. If you use this data set please cite: [1] S. Romano, G. Scanniello, D. Fucci, N. Juristo, and B. Turhan, "The effect of noise on software engineers<U+2019> performance", in Proceedings of the 12th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, ser. ESEM'18, 2018. [2] Barbara Kitchenham, Lech Madeyski, Giuseppe Scanniello and Carmine Gravino, "The Importance of the Correlation between Results from Individual Participants in Crossover Experiments" (to be submitted as of 2020). The experiment had two parts but Kitchenham et al. only use the data from the first part of the experiment.

KitchenhamEtAl.CorrelationsAmongParticipants.Scanniello14EASE

KitchenhamEtAl.CorrelationsAmongParticipants.Scanniello14EASE data illustrate correlations between results from individual participants in a family of two cross-over experiments conducted by Scanniello et al: [1] G. Scanniello, M. Staron, H. Burden, and R. Heldal, "On the effect of using SysML requirement diagrams to comprehend requirements: results from two controlled experiments," in Proceedings of the 18th International Conference on Evaluation and Assessment in Software Engineering, EASE. ACM, 2014. The two experiments investigate whether requirements specified as SysML requirement diagrams improve the comprehensibility of requirements. If you use this data set please cite: [1] G. Scanniello, M. Staron, H. Burden, and R. Heldal, "On the effect of using SysML requirement diagrams to comprehend requirements: results from two controlled experiments", in Proceedings of the 18th International Conference on Evaluation and Assessment in Software Engineering, EASE. ACM, 2014. [2] Barbara Kitchenham, Lech Madeyski, Giuseppe Scanniello and Carmine Gravino, "The importance of the Correlation between Results from Individual Participants in Crossover Experiments" (to be submitted as of 2020).

KitchenhamMadeyskiBrereton.ExpData

KitchenhamMadeyskiBrereton.ExpData data

KitchenhamMadeyskiBrereton.MetaAnalysisReportedResults

KitchenhamMadeyskiBrereton.MetaAnalysisReportedResults data

KitchenhamEtAl.CorrelationsAmongParticipants.Scanniello14TOSEM

KitchenhamEtAl.CorrelationsAmongParticipants.Scanniello14TOSEM data illustrate correlations between results from individual participants in a family of four cross-over experiments conducted by Scanniello et al: [1] G. Scanniello, C. Gravino, M. Genero, J.A. Cruz-Lemus, and G. Tortora, "On the Impact of UML Analysis Models on Source-Code Comprehensibility and Modifiability", ACM Transactions on Software Engineering and Methodlogy, vol. 23, no. 2, pp. 13:1-13:26, 2014 The family of experiments investigated whether the availability of analysis models in addition to the source code made the code easier to understand and modify. If you use this data set please cite: [1] G. G. Scanniello, C. Gravino, M. Genero, J.A. Cruz-Lemus, and G. Tortora, "On the Impact of UML Analysis Models on Source-Code Comprehensibility and Modifiability", ACM Transactions on Software Engineering and Methodology, vol. 23, no. 2, pp. 13:1-13:26, 2014 [2] Barbara Kitchenham, Lech Madeyski, Giuseppe Scanniello and Carmine Gravino, "The importance of the Correlation between Results from Individual Participants in Crossover Experiments" (to be submitted as of 2020).

KitchenhamMadeyskiBudgen16.FINNISH

KitchenhamMadeyskiBudgen16.FINNISH data

Madeyski15EISEJ.OpenProjects

Madeyski15EISEJ.OpenProjects data

KitchenhamMadeyskiBudgen16.DiffInDiffData

KitchenhamMadeyskiBudgen16.DiffInDiffData data

KitchenhamMadeyskiBudgen16.COCOMO

KitchenhamMadeyskiBudgen16.COCOMO data

KitchenhamEtAl.CorrelationsAmongParticipants.Scanniello15EMSE

KitchenhamEtAl.CorrelationsAmongParticipants.Scanniello15EMSE data illustrate correlations between results from individual participants in cross-over experiment usb2 conducted by Scanniello et al: [1] G. Scanniello, A. Marcus, and D. Pascale, "Link analysis algorithms for static concept location: an empirical assessment", Empirical Software Engineering, vol. 20, no. 6, pp. 1666<U+2013>1720, 2015. The goal of the experiment is to assess whether a new technique (implemented as an Eclipse plug-in) for static concept location (proposed by the authors) supports users in identifying the places in the code where changes are to be made.

KitchenhamMadeyskiBrereton.ReportedEffectSizes

KitchenhamMadeyskiBrereton.ReportedEffectSizes data

boxplotAndDensityCurveOnHistogram

boxplotAndDensityCurveOnHistogram

Madeyski15EISEJ.PropProjects

Madeyski15EISEJ.PropProjects data

PrepareForMetaAnalysisGtoR

PrepareForMetaAnalysisGtoR

aggregateIndividualDocumentStatistics

aggregateIndividualDocumentStatistics

KitchenhamMadeyskiBudgen16.PolishData

KitchenhamMadeyskiBudgen16.PolishData data

KitchenhamEtAl.CorrelationsAmongParticipants.Torchiano17JVLC

KitchenhamEtAl.CorrelationsAmongParticipants.Torchiano17JVLC data illustrate correlations between results from individual participants in a family of three cross-over experiments conducted by Torchiano et al: [1] M. Torchiano, G. Scanniello, F. Ricca, G. Reggio, and M. Leotta, "Do UML object diagrams affect design comprehensibility? Results from a family of four controlled experiments." Journal of Visual Languages and Computing, vol. 41, pp. 10<U+2013>21, 2017. Although the paper reports four experiment, we only have data from three of those experiments. The experiments assess whether the comprehensibility of UML specifications improve when the software documents include UML object diagrams as well as the standard UML class diagrams. If you use this data set please cite: [1] M. Torchiano, G. Scanniello, F. Ricca, G. Reggio, and M. Leotta, "Do UML object diagrams affect design comprehensibility? Results from a family of four controlled experiments." Journal of Visual Languages and Computing, vol. 41, pp. 10<U+2013>21, 2017. [2] Barbara Kitchenham, Lech Madeyski, Giuseppe Scanniello and Carmine Gravino, "The importance of the Correlation between Results from Individual Participants in Crossover Experiments" (to be submitted as of 2020).

MadeyskiKitchenham.EUBASdata

MadeyskiKitchenham.EUBASdata data

KitchenhamEtAl.CorrelationsAmongParticipants.Scanniello17TOSEM

KitchenhamEtAl.CorrelationsAmongParticipants.Scanniello17TOSEM data illustrate correlations between results from individual participants in a family of four cross-over experiments conducted by Scanniello et al.: [1] G. Scanniello, M. Risi, P. Tramontana, and S. Romano, "Fixing faults in C and Java source code: Abbreviated vs. full-word identifier names", ACM Transactions on Software Engineering Methodology, vol. 26, no. 2, 2017. The experiments assess whether whether the use of abbreviated identifier names (ABBR), impacts the effectiveness of fault fixing in C and Java source code in comparison with full-word identifier names (FULL). If you use this data set please cite: [1] G. Scanniello, M. Risi, P. Tramontana, and S. Romano, <U+201C>Fixing faults in C and Java source code: Abbreviated vs. full-word identifier names", ACM Transactions on Software Engineering Methodology, vol. 26, no. 2, 2017. [2] Barbara Kitchenham, Lech Madeyski, Giuseppe Scanniello and Carmine Gravino, "On the Importance of the Correlation between Results from Individual Participants in Crossover Experiments" (to be submitted as of 2020).

KitchenhamMadeyskiBrereton.ABBAReportedEffectSizes

KitchenhamMadeyskiBrereton.ABBAReportedEffectSizes data

KitchenhamMadeyski.SimulatedCrossoverDataSets

KitchenhamMadeyski.SimulatedCrossoverDataSets data

proportionOfSignificantTValuesUsingIncorrectAnalysis

proportionOfSignificantTValuesUsingIncorrectAnalysis

KitchenhamMadeyskiBudgen16.PolishSubjects

KitchenhamMadeyskiBudgen16.PolishSubjects data

MadeyskiLewowski.IndustryRelevantGitHubJavaProjects20191022

MadeyskiLewowski.IndustryRelevantGitHubJavaProjects20191022 data

Madeyski15EISEJ.StudProjects

Madeyski15EISEJ.StudProjects data

MadeyskiLewowski.IndustryRelevantGitHubJavaProjects20190324

MadeyskiLewowski.IndustryRelevantGitHubJavaProjects20190324 data

percentageInaccuracyOfLargeSampleVarianceApproximation

percentageInaccuracyOfLargeSampleVarianceApproximation

calculateGroupSummaryStatistics

calculateGroupSummaryStatistics

calculateBasicStatistics

calculateBasicStatistics

MadeyskiKitchenham.MetaAnalysis.PBRvsCBRorAR

MadeyskiKitchenham.MetaAnalysis.PBRvsCBRorAR data form a set of primary studies on reading methods for software inspections. They were analysed by Lech Madeyski and Barbara Kitchenham, "How variations in experimental designs impact the construction of comparable effect sizes for meta-analysis", 2015.

Madeyski15SQJ.NDC

Madeyski15SQJ.NDC data

proportionOfSignificantTValuesUsingCorrectAnalysis

proportionOfSignificantTValuesUsingCorrectAnalysis

KitchenhamMadeyskiBudgen16.SubjectData

KitchenhamMadeyskiBudgen16.SubjectData

plotOutcomesForIndividualsInEachSequenceGroup

plotOutcomesForIndividualsInEachSequenceGroup

getEffectSizesABBAIgnoringPeriodEffect

getEffectSizesABBAIgnoringPeriodEffect

getEffectSizesABBA

getEffectSizesABBA

searchForIndustryRelevantGitHubProjects

searchForIndustryRelevantGitHubProjects

reproduceTableWithPossibleModeratingFactors

reproduceTableWithPossibleModeratingFactors()

reproduceMixedEffectsAnalysisWithEstimatedVarianceAndExperimentalDesignModerator

reproduceMixedEffectsAnalysisWithEstimatedVarianceAndExperimentalDesignModerator()

reproduceTableWithEffectSizesBasedOnMeanDifferences

reproduceTableWithEffectSizesBasedOnMeanDifferences()

transformZrtoHgapprox

transformZrtoHgapprox

reproduceMixedEffectsAnalysisWithExperimentalDesignModerator

reproduceMixedEffectsAnalysisWithExperimentalDesignModerator()

transformHgtoZr

transformHgtoZr

getSimulationData

getSimulationData

constructEffectSizes

constructEffectSizes

densityCurveOnHistogram

densityCurveOnHistogram

reproduceMixedEffectsForestPlotWithExperimentalDesignModerator

reproduceMixedEffectsForestPlotWithExperimentalDesignModerator()

calculateSmallSampleSizeAdjustment

calculateSmallSampleSizeAdjustment

getTheoreticalEffectSizeVariancesABBA

getTheoreticalEffectSizeVariancesABBA

reproduceSimulationResultsBasedOn500Reps1000Obs

reproduceSimulationResultsBasedOn500Reps1000Obs

reproduceTableWithSourceDataByCiolkowski

reproduceTableWithSourceDataByCiolkowski

reproduceTablesOfPaperMetaAnalysisForFamiliesOfExperiments

reproduceTablesOfPaperMetaAnalysisForFamiliesOfExperiments

reproduceForestPlotRandomEffects

reproduceForestPlotRandomEffects()

transformZrtoHg

transformZrtoHg