Note: Please find the dockerized version of this replication package in the following link: https://figshare.com/articles/dataset/Replication_Package_of_the_study_Exploring_the_Notion_of_Risk_in_Reviewer_Recommendation_/20673255 This repository contains the necessary data for replicating the necessary information to replicate the study of "Exploring the Notion of Risk in Reviewer Recommendation." This code extends the RelationalGit package (https://github.com/CESEL/RelationalGit) from the study of E. Mirsaeedi and P. C. Rigby [1] and adds some functionality that is needed to incorporate the concept of the fix-inducing likelihood of a project. In addition to our dataset, this repository also have the supporting materials for our study. The supporting materials are in the "ICSME_online_materials_ICSME.pdf" and contains the following items: Table 1 contains the detail of the dataset and some related statistics for each of the studied projects. Table 2 have risk measures that were used in our defect prediction model. We use Commit Guru Tool to extracts the data from the GitHub repositories and then use this data to train our defect prediction model. Figure 1 illustrates the distribution of predicted defect probability of different projects. This distribution shows how defect probability of different periods are similar to the adjacent periods. References: [1] E. Mirsaeedi and P. C. Rigby, ‘Mitigating turnover with code review recommendation: Balancing expertise, workload, and knowledge distribution’, στο Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering, 2020.

Note: Please find the dockerized version of this replication package in the following link: https://figshare.com/articles/dataset/Replication_Package_of_the_study_Exploring_the_Notion_of_Risk_in_Reviewer_Recommendation_/20673255 This repository contains the necessary data for replicating the necessary information to replicate the study of "Exploring the Notion of Risk in Reviewer Recommendation." This code extends the RelationalGit package (https://github.com/CESEL/RelationalGit) from the study of E. Mirsaeedi and P. C. Rigby [1] and adds some functionality that is needed to incorporate the concept of the fix-inducing likelihood of a project. In addition to our dataset, this repository also have the supporting materials for our study. The supporting materials are in the "ICSME_online_materials_ICSME.pdf" and contains the following items: Table 1 contains the detail of the dataset and some related statistics for each of the studied projects. Table 2 have risk measures that were used in our defect prediction model. We use Commit Guru Tool to extracts the data from the GitHub repositories and then use this data to train our defect prediction model. Figure 1 illustrates the distribution of predicted defect probability of different projects. This distribution shows how defect probability of different periods are similar to the adjacent periods. References: [1] E. Mirsaeedi and P. C. Rigby, ‘Mitigating turnover with code review recommendation: Balancing expertise, workload, and knowledge distribution’, στο Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering, 2020.

Note: Please find the dockerized version of this replication package in the following link: https://figshare.com/articles/dataset/Replication_Package_of_the_study_Exploring_the_Notion_of_Risk_in_Reviewer_Recommendation_/20673255 This repository contains the necessary data for replicating the necessary information to replicate the study of "Exploring the Notion of Risk in Reviewer Recommendation." This code extends the RelationalGit package (https://github.com/CESEL/RelationalGit) from the study of E. Mirsaeedi and P. C. Rigby [1] and adds some functionality that is needed to incorporate the concept of the fix-inducing likelihood of a project. In addition to our dataset, this repository also have the supporting materials for our study. The supporting materials are in the "ICSME_online_materials_ICSME.pdf" and contains the following items: Table 1 contains the detail of the dataset and some related statistics for each of the studied projects. Table 2 have risk measures that were used in our defect prediction model. We use Commit Guru Tool to extracts the data from the GitHub repositories and then use this data to train our defect prediction model. Figure 1 illustrates the distribution of predicted defect probability of different projects. This distribution shows how defect probability of different periods are similar to the adjacent periods. References: [1] E. Mirsaeedi and P. C. Rigby, ‘Mitigating turnover with code review recommendation: Balancing expertise, workload, and knowledge distribution’, στο Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering, 2020.

Note: Please find the dockerized version of this replication package in the following link: https://figshare.com/articles/dataset/Replication_Package_of_the_study_Exploring_the_Notion_of_Risk_in_Reviewer_Recommendation_/20673255 This repository contains the necessary data for replicating the necessary information to replicate the study of "Exploring the Notion of Risk in Reviewer Recommendation." This code extends the RelationalGit package (https://github.com/CESEL/RelationalGit) from the study of E. Mirsaeedi and P. C. Rigby [1] and adds some functionality that is needed to incorporate the concept of the fix-inducing likelihood of a project. In addition to our dataset, this repository also have the supporting materials for our study. The supporting materials are in the "ICSME_online_materials_ICSME.pdf" and contains the following items: Table 1 contains the detail of the dataset and some related statistics for each of the studied projects. Table 2 have risk measures that were used in our defect prediction model. We use Commit Guru Tool to extracts the data from the GitHub repositories and then use this data to train our defect prediction model. Figure 1 illustrates the distribution of predicted defect probability of different projects. This distribution shows how defect probability of different periods are similar to the adjacent periods. References: [1] E. Mirsaeedi and P. C. Rigby, ‘Mitigating turnover with code review recommendation: Balancing expertise, workload, and knowledge distribution’, στο Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering, 2020.

Note: Please find the dockerized version of this replication package in the following link: https://figshare.com/articles/dataset/Replication_Package_of_the_study_Exploring_the_Notion_of_Risk_in_Reviewer_Recommendation_/20673255 This repository contains the necessary data for replicating the necessary information to replicate the study of "Exploring the Notion of Risk in Reviewer Recommendation." This code extends the RelationalGit package (https://github.com/CESEL/RelationalGit) from the study of E. Mirsaeedi and P. C. Rigby [1] and adds some functionality that is needed to incorporate the concept of the fix-inducing likelihood of a project. In addition to our dataset, this repository also have the supporting materials for our study. The supporting materials are in the "ICSME_online_materials_ICSME.pdf" and contains the following items: Table 1 contains the detail of the dataset and some related statistics for each of the studied projects. Table 2 have risk measures that were used in our defect prediction model. We use Commit Guru Tool to extracts the data from the GitHub repositories and then use this data to train our defect prediction model. Figure 1 illustrates the distribution of predicted defect probability of different projects. This distribution shows how defect probability of different periods are similar to the adjacent periods. References: [1] E. Mirsaeedi and P. C. Rigby, ‘Mitigating turnover with code review recommendation: Balancing expertise, workload, and knowledge distribution’, στο Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering, 2020.

This is the replication material related to the following paper submitted for TSE. Faizan Khan, Boqi Chen, Daniel Varro, and Shane McIntosh. An Empirical Study ofType-Related Defects in Python Projects.IEEE Transactions on Software Engineering,2021(under review)

This is the replication material related to the following paper submitted for TSE. Faizan Khan, Boqi Chen, Daniel Varro, and Shane McIntosh. An Empirical Study ofType-Related Defects in Python Projects.IEEE Transactions on Software Engineering,2021(under review)

This is the replication material related to the following paper submitted for TSE. Faizan Khan, Boqi Chen, Daniel Varro, and Shane McIntosh. An Empirical Study ofType-Related Defects in Python Projects.IEEE Transactions on Software Engineering,2021(under review)

This is the replication material related to the following paper submitted for TSE. Faizan Khan, Boqi Chen, Daniel Varro, and Shane McIntosh. An Empirical Study ofType-Related Defects in Python Projects.IEEE Transactions on Software Engineering,2021(under review)