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The materials of "How do Developers Fix Cross-project Correlated Bugs?"
This project forked from njuap/icse2017
The materials of "How do Developers Fix Cross-project Correlated Bugs?"
This repository contains the materials of our paper "How do Developers Fix Cross-project Correlated Bugs? A case study on the GitHub scientific Python ecosystem", including: 1. script * the Python script used to collect bugs in GitHub repository. 2. method & data * Manual Analysis: the detailed description of our manual inspection * Survey Analysis: the detailed description of the online survey and a summary of the survey responses * cross-project bugs: the 271 pairs of investigated cross-project correlated bugs * Statistical Comparision: the results of statistical tests for comparing the difficulty of fixing cross-project bugs and other bugs in terms of fixing time, number of participants, and number of comments * numpy, scipy, astropy, ipython, scikit-learn, matplotlib, pandas: the bug data of numpy, scipy, astropy, ipython, scikit-learn, matplotlib, pandas collected by the script "getBugs.py" 3. figures * Dependency graph (Figure 3): Figure 3 in the paper which shows part of the inter-dependencies among projects in the scientific Python ecosystem * Distribution of hidden duration (Figure 4): Figure 4 in RQ1 which shows the distribution of hidden duration A version-dependent workaround (Figure 6): Figure 6 in RQ3 which shows an example of a version-dependent workaround * The identities of reporters and restorers (Figure 7): Figure 7 in RQ3 which shows the identities of reporters and restorers of the upstream bugs in the dowsntream projects
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