Revisiting the Effect of Branch Handling Strategies on Change Recommendation

Keisuke Isemoto, Takashi Kobayashi, Shinpei Hayashi
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Abstract

Although literature has noted the effects of branch handling strate-gies on change recommendation based on evolutionary coupling, they have been tested in a limited experimental setting. Additionally, the branches characteristics that lead to these effects have not been investigated. In this study, we revisited the investigation conducted by Kovalenko et al. on the effect to change recommendation using two different branch handling strategies: including changesets from commits on a branch and excluding them. In addition to the setting by Kovalenko et al., we introduced another setting to compare: ex-tracting a changeset for a branch from a merge commit at once. We compared the change recommendation results and the similarity of the extracted co-changes to those in the future obtained using two strategies through 30 open-source software systems. The results show that handling commits on a branch separately is often more appropriate in change recommendation, although the comparison in an additional setting resulted in a balanced performance among the branch handling strategies. Additionally, we found that the merge commit size and the branch length positively influence the change recommendation results.
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再论分行处理策略对变更建议的影响
虽然文献已经注意到分支处理策略对基于进化耦合的变更推荐的影响,但它们已经在有限的实验环境中进行了测试。此外,导致这些影响的分支特性尚未得到研究。在这项研究中,我们重新审视了Kovalenko等人对使用两种不同的分支处理策略对更改建议的影响的调查:包括分支上提交的更改集和排除它们。除了Kovalenko等人的设置之外,我们还引入了另一种设置来进行比较:一次从合并提交中提取分支的变更集。我们通过30个开源软件系统,比较了采用两种策略获得的变更推荐结果以及提取的共同变更与未来获得的变更的相似度。结果表明,在变更建议中单独处理分支上的提交通常更合适,尽管在另一个设置中的比较导致了分支处理策略之间的平衡性能。此外,我们发现合并提交大小和分支长度对变更推荐结果有积极影响。
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