Analyzing conflict predictors in open-source Java projects

Accioly, Paola; Borba, Paulo; Silva, Leuson; Cavalcanti, Guilherme

doi:10.1145/3196398.3196437

Cited by 17 publications

(6 citation statements)

References 27 publications

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“…The model's performance is also expected to deteriorate when some of the features in the feature set are not available for a given pull request. Other techniques [6,30] explored the idea of predicting conflicts. None of them actually built a prediction model or corresponding tools.…”

Section: Related Workmentioning

confidence: 99%

ConE: A Concurrent Edit Detection Tool for Large Scale Software Development

Maddila,

Nagappan,

Bird

et al. 2021

Preprint

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Developers from different teams or organizations, co-located or distributed, making changes to the same source code files or areas, through pull requests that are active in the same time period, is an essential part of developing complex software systems. With such a dynamically changing environment spanning several boundaries, geographic and organizational, there is little awareness about the changes that are flowing in through other active pull requests in the system leading to complex merge conflicts, hard-to-detect logical bugs or duplication of work and wasted developer productivity. In order to address this problem, we studied changes produced in eight very large repositories, in Microsoft to understand the extent of concurrent edits and their relation to subsequent bugs and bug fixes. Motivated by our findings, we developed a system called ConE (Concurrent Edit Detector) that proactively detects concurrent edits to help mitigate the problems caused by them. We present the results of ConE's deployment through early intervention techniques such as pull request notifications, by which ConE facilitates better communication among all the stakeholders participating in collaborative software development, helping avoid future problems.

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Section: Related Workmentioning

confidence: 99%

ConE: A Concurrent Edit Detection Tool for Large Scale Software Development

Maddila,

Nagappan,

Bird

et al. 2021

Preprint

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“…Accioly et al [14] investigate whether the occurrence of events such as edits to the same method and edits to directly dependent methods can be used to predict conflicts. However, they do not actually build a prediction model.…”

Section: Proactive Conflict Detectionmentioning

confidence: 99%

“…• Popularity: Intuitively, more active and useful repositories attract more attention, reflected in the number of stars, issues, forks, etc. Similar to previous studies [4], [14], we use the number of stars as a filtering criterion. • Quality: Even though the number of stars represents some measure of quality, not all popular repositories are suitable for our study.…”

Section: Data Collection Processmentioning

confidence: 99%

“…However, we argue that lacking correlation does not necessarily preclude a successful classifier, especially since the study did not consider the fact that the frequency of conflicts is low in practice and most of the standard form of statistics and machine learning techniques cannot handle imbalanced data well. The second study [14] investigates the relationship between two types of code changes, edits to the same method and edits to dependent methods, and merge conflicts. The authors report recall of 82.67% and precision of 57.99% based on counting how often a merge scenario that had the given change was conflicting.…”

Section: Introductionmentioning

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.…”

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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