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Meta-analysis for families of experiments in software engineering: a systematic review and reproducibility and validity assessment.

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Title: Meta-analysis for families of experiments in software engineering: a systematic review and reproducibility and validity assessment.
Authors: Kitchenham, Barbara1, Madeyski, Lech2 Lech.Madeyski@pwr.edu.pl, Brereton, Pearl1
Source: Empirical Software Engineering. Jan2020, Vol. 25 Issue 1, p353-401. 49p.
Subjects: Meta-analysis, Software engineering, Reproducible research, Aggregation (Statistics), Descriptive statistics
Abstract: Context: Previous studies have raised concerns about the analysis and meta-analysis of crossover experiments and we were aware of several families of experiments that used crossover designs and meta-analysis. Objective: To identify families of experiments that used meta-analysis, to investigate their methods for effect size construction and aggregation, and to assess the reproducibility and validity of their results. Method: We performed a systematic review (SR) of papers reporting families of experiments in high quality software engineering journals, that attempted to apply meta-analysis. We attempted to reproduce the reported meta-analysis results using the descriptive statistics and also investigated the validity of the meta-analysis process. Results: Out of 13 identified primary studies, we reproduced only five. Seven studies could not be reproduced. One study which was correctly analyzed could not be reproduced due to rounding errors. When we were unable to reproduce results, we provide revised meta-analysis results. To support reproducibility of analyses presented in our paper, it is complemented by the reproducer R package. Conclusions: Meta-analysis is not well understood by software engineering researchers. To support novice researchers, we present recommendations for reporting and meta-analyzing families of experiments and a detailed example of how to analyze a family of 4-group crossover experiments. [ABSTRACT FROM AUTHOR]
Copyright of Empirical Software Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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PubType: Academic Journal
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  Data: Meta-analysis for families of experiments in software engineering: a systematic review and reproducibility and validity assessment.
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  Data: <searchLink fieldCode="AR" term="%22Kitchenham%2C+Barbara%22">Kitchenham, Barbara</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Madeyski%2C+Lech%22">Madeyski, Lech</searchLink><relatesTo>2</relatesTo><i> Lech.Madeyski@pwr.edu.pl</i><br /><searchLink fieldCode="AR" term="%22Brereton%2C+Pearl%22">Brereton, Pearl</searchLink><relatesTo>1</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Empirical+Software+Engineering%22">Empirical Software Engineering</searchLink>. Jan2020, Vol. 25 Issue 1, p353-401. 49p.
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  Data: <searchLink fieldCode="DE" term="%22Meta-analysis%22">Meta-analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Software+engineering%22">Software engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Reproducible+research%22">Reproducible research</searchLink><br /><searchLink fieldCode="DE" term="%22Aggregation+%28Statistics%29%22">Aggregation (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Descriptive+statistics%22">Descriptive statistics</searchLink>
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  Label: Abstract
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  Data: Context: Previous studies have raised concerns about the analysis and meta-analysis of crossover experiments and we were aware of several families of experiments that used crossover designs and meta-analysis. Objective: To identify families of experiments that used meta-analysis, to investigate their methods for effect size construction and aggregation, and to assess the reproducibility and validity of their results. Method: We performed a systematic review (SR) of papers reporting families of experiments in high quality software engineering journals, that attempted to apply meta-analysis. We attempted to reproduce the reported meta-analysis results using the descriptive statistics and also investigated the validity of the meta-analysis process. Results: Out of 13 identified primary studies, we reproduced only five. Seven studies could not be reproduced. One study which was correctly analyzed could not be reproduced due to rounding errors. When we were unable to reproduce results, we provide revised meta-analysis results. To support reproducibility of analyses presented in our paper, it is complemented by the reproducer R package. Conclusions: Meta-analysis is not well understood by software engineering researchers. To support novice researchers, we present recommendations for reporting and meta-analyzing families of experiments and a detailed example of how to analyze a family of 4-group crossover experiments. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Empirical Software Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s10664-019-09747-0
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              Text: Jan2020
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