Scalable Software Merging Studies with MERGANSER

Owhadi-Kareshk, Moein; Nadi, Sarah

doi:10.1109/msr.2019.00084

Cited by 1 publication

(1 citation statement)

References 21 publications

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“…The input for this stage is a list of Git repositories to be analyzed. After cloning all repositories, we use MER-GANSER, an open-source toolchain we developed for extracting merge-scenario data [36], to analyze their histories.…”

Section: A Collecting Merge Scenariosmentioning

confidence: 99%

Predicting Merge Conflicts in Collaborative Software Development

Owhadi-Kareshk

Nadi

Rubin

2019

2019 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)

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Background. During collaborative software development, developers often use branches to add features or fix bugs. When merging changes from two branches, conflicts may occur if the changes are inconsistent. Developers need to resolve these conflicts before completing the merge, which is an error-prone and time-consuming process. Early detection of merge conflicts, which warns developers about resolving conflicts before they become large and complicated, is among the ways of dealing with this problem. Existing techniques do this by continuously pulling and merging all combinations of branches in the background to notify developers as soon as a conflict occurs, which is a computationally expensive process. One potential way for reducing this cost is to use a machine-learning based conflict predictor that filters out the merge scenarios that are not likely to have conflicts, i.e. safe merge scenarios.Aims. In this paper, we assess if conflict prediction is feasible.Method. We design a classifier for predicting merge conflicts, based on 9 light-weight Git feature sets. To evaluate our predictor, we perform a large-scale study on 267, 657 merge scenarios from 744 GitHub repositories in seven programming languages.Results. Our results show that we achieve high f1-scores, varying from 0.95 to 0.97 for different programming languages, when predicting safe merge scenarios. The f1-score is between 0.57 and 0.68 for the conflicting merge scenarios.Conclusions. Predicting merge conflicts is feasible in practice, especially in the context of predicting safe merge scenarios as a pre-filtering step for speculative merging.

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Section: A Collecting Merge Scenariosmentioning

confidence: 99%