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This replication package contains the data of the paper "Replicability Study: Corpora For Understanding Simulink Models & Projects" by Sohil Lal Shrestha, Shafiul Azam Chowdhury and Christoph Csallner in Proc. 17th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM), 2023.
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Replication Package for 'An Empirical Study of Community-prescribed Security Configurations in Kubernetes'
The replication package contains data and scripts used to generate results reported in the paper. The replication package does not contain any data from Company-A to abide by the non-disclose agreement signed between the authors and Company-A.
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- PAR ID:
- 10636009
- Publisher / Repository:
- figshare
- Date Published:
- Subject(s) / Keyword(s):
- Empirical software engineering Software quality, processes and metrics
- Format(s):
- Medium: X Size: 8797708 Bytes
- Size(s):
- 8797708 Bytes
- Location:
- Ottawa, ON, Canada
- Right(s):
- MIT License
- Institution:
- Auburn University
- Sponsoring Org:
- National Science Foundation
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Aim In contrast to studies of defects found during code review, we aim to clarify whether code review measures can explain the prevalence of post-release defects. Method We replicate McIntosh et al.’s (Empirical Softw. Engg. 21(5): 2146–2189, 2016) study that uses additive regression to model the relationship between defects and code reviews. To increase external validity, we apply the same methodology on a new software project. We discuss our findings with the first author of the original study, McIntosh. We then investigate how to reduce the impact of correlated predictors in the variable selection process and how to increase understanding of the inter-relationships among the predictors by employing Bayesian Network (BN) models. Context As in the original study, we use the same measures authors obtained for Qt project in the original study. We mine data from version control and issue tracker of Google Chrome and operationalize measures that are close analogs to the large collection of code, process, and code review measures used in the replicated the study. Results Both the data from the original study and the Chrome data showed high instability of the influence of code review measures on defects with the results being highly sensitive to variable selection procedure. Models without code review predictors had as good or better fit than those with review predictors. Replication, however, confirms with the bulk of prior work showing that prior defects, module size, and authorship have the strongest relationship to post-release defects. The application of BN models helped explain the observed instability by demonstrating that the review-related predictors do not affect post-release defects directly and showed indirect effects. For example, changes that have no review discussion tend to be associated with files that have had many prior defects which in turn increase the number of post-release defects. We hope that similar analyses of other software engineering techniques may also yield a more nuanced view of their impact. Our replication package including our data and scripts is publicly available (Replication package 2018).more » « less
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