Indicators for merge conflicts in the wild: survey and empirical study

Leßenich, Olaf; Siegmund, Janet; Apel, Sven; Kästner, Christian; Hunsen, Claus

doi:10.1007/s10515-017-0227-0

Cited by 37 publications

(40 citation statements)

References 32 publications

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“…Despite confirming the lack of significant correlation found by previous work [13], our prediction results show that lack of strong correlation does not necessarily mean that a machine learning classifier would perform poorly. Our results show that a Random Forest classifier using all our feature sets predicts conflicting merge scenarios with a precision of 0.48 to 0.63 and a recall from 0.68 to 0.83 across the different programming languages.…”

Section: Introductionsupporting

confidence: 78%

“…In this paper, we investigate merge-conflict prediction by creating a list of nine feature sets that can potentially impact conflicts. Our list is based on previous work in the areas of software merging and code review [5], [9], [13], [34], [35]. Our work is different from all the above in that we use statistical machine learning to create a classifier, for each programming language, that can predict conflicts in unseen merge scenarios.…”

Section: Proactive Conflict Detectionmentioning

confidence: 99%

“…There are several techniques in the field of machine learning that have been designed to overcome this problem [38], including data resampling, tree-based models, an ensemble learning approach, or alternative cost functions, Resampling methods, such as Synthetic Minority Over-sampling Technique (SMOTE) [39], are computationally expensive which is why we opt for ensemble learning techniques that combine multiple simpler classification models, allowing them to handle No. of simultaneously changed files in two branches [5], [13] The increase in simultaneously changed files increases the chance of conflicts. If the value of this feature is zero, no conflict can occur.…”

Section: Predictionmentioning

confidence: 99%

“…To the best of our knowledge, there have been two attempts at predicting merge conflicts in the past [13], [14]. The first study [13] looked for correlations between various features and merge conflicts and found that none of the features have a strong correlation with merge conflicts. The authors concluded that merge conflict prediction may not be possible.…”

Section: Introductionmentioning

confidence: 99%

“…To the best of our knowledge, this work presents the largest mergeconflict prediction study to date. We have almost 13 times the number of merge scenarios used by recent work [13], and our work is the first that is evaluated with repositories written in several programming languages. We publish all our code and evaluation data in an online artifact page [18].…”

Section: Introductionmentioning

confidence: 99%

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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: Introductionsupporting

confidence: 78%

Section: Proactive Conflict Detectionmentioning

confidence: 99%

Section: Predictionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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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Build conflicts in the wild

Silva

Borba

Pires

2022

J Software Evolu Process

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When collaborating, developers often create and change software artifacts without being fully aware of team members' work. While such independence is essential for increasing development productivity, it might also result in conflicts when integrating developers' contributions. To better understand the conflicts revealed by failures when building integrated code, we investigate their frequency and structure and adopted resolution patterns in 451 open-source Java projects. To detect such build conflicts, we select merge scenarios from git repositories, parse their Travis build logs, and check whether the build error messages are related to the merged changes. We find and classify 239 build conflicts and their resolution patterns. Most build conflicts are caused by missing declarations removed or renamed by one developer but referenced by another developer. Conflicts caused by renaming are often resolved by updating the missing reference, whereas removed declarations are often reintroduced. Most fix commits are authored by one of the merge scenario contributors. Based on our catalogue of conflict causes, awareness tools could alert developers about the risk of conflict situations. Program repair tools could benefit from our catalogue of resolution patterns to automatically fix conflicts; we illustrate that with a proof of concept implementation of a tool that fixes conflicts.

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Early prediction of merged code changes to prioritize reviewing tasks

Fan

Xia

et al. 2018

Empir Software Eng

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Indicators for merge conflicts in the wild: survey and empirical study

Cited by 37 publications

References 32 publications

Predicting Merge Conflicts in Collaborative Software Development

Predicting Merge Conflicts in Collaborative Software Development

Build conflicts in the wild

Early prediction of merged code changes to prioritize reviewing tasks

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