RevRec: A two-layer reviewer recommendation algorithm in pull-based development model

Yang, Cheng; Zhang, Xunhui; Zeng, Lingbin; Fan, Qiang; Wang, Tao; Yu, Yue; Yin, Gang; Wang, Huaimin

doi:10.1007/s11771-018-3812-x

Cited by 26 publications

(9 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We also searched for publicly available datasets for further comparison. Lipcak and Rossi [12] review different approaches on code reviewer recommendation and create a dataset publicly available [12] Naive-Bayes --Code RevFinder [22] File path similarity + -Code Correct [18] Developer experience -+ Code RevRec [26] Hybrid of information retrieval and file location --Code Jeong et al [6] Bayesian network -+ Code cHRev [29] Expertise model --Code Developer-Source code graph [11] Random walk algorithm --Code TIE [24] Text and File Location Analyses --Code CoreDevRec [7] Support vector machine --Code RSTrace Know-about metric + + Any for comparison with other approaches. The dataset includes code review information about 51 projects (37 from GitHub and 14 from Gerrit) and it is available on GitHub.…”

Section: Resultsmentioning

confidence: 99%

“…Yu et al propose an approach by combining comment networks with traditional approaches to find suitable developers for pull-requests in GitHub projects [28]. Cheng et al propose a method called RevRec to recommend reviewers [26]. Rahman et al predict code reviewers by considering cross-project technology experience of developers [18].…”

Section: Reviewer Suggestionmentioning

confidence: 99%

See 1 more Smart Citation

Reviewer Recommendation using Software Artifact Traceability Graphs

Sülün

Tüzün

Doğrusöz

2019

Proceedings of the Fifteenth International Conference on Predictive Models and Data Analytics in Software Engineering

View full text Add to dashboard Cite

Various types of artifacts (requirements, source code, test cases, documents, etc.) are produced throughout the lifecycle of a software. These artifacts are often related with each other via traceability links that are stored in modern application lifecycle management repositories. Throughout the lifecycle of a software, various types of changes can arise in any one of these artifacts. It is important to review such changes to minimize their potential negative impacts. To maximize benefits of the review process, the reviewer(s) should be chosen appropriately. In this study, we reformulate the reviewer suggestion problem using software artifact traceability graphs. We introduce a novel approach, named RSTrace, to automatically recommend reviewers that are best suited based on their familiarity with a given artifact. The proposed approach, in theory, could be applied to all types of artifacts. For the purpose of this study, we focused on the source code artifact and conducted an experiment on finding the appropriate code reviewer(s). We initially tested RSTrace on an open source project and achieved top-3 recall of 0.85 with an MRR (mean reciprocal ranking) of 0.73. In a further empirical evaluation of 37 open source projects, we confirmed that the proposed reviewer recommendation approach yields promising top-k and MRR scores on the average compared to the existing reviewer recommendation approaches. CCS CONCEPTS • Software and its engineering → Software maintenance tools; Collaboration in software development.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Reviewer Suggestionmentioning

confidence: 99%

Reviewer Recommendation using Software Artifact Traceability Graphs

Sülün

Tüzün

Doğrusöz

2019

Proceedings of the Fifteenth International Conference on Predictive Models and Data Analytics in Software Engineering

View full text Add to dashboard Cite

show abstract

“…Each recommender system in this category uses a different combination of algorithms. These systems include the following work: [10,12,43,44]. For example, Xia et al [12] proposed a recommender system that combines neighborhood methods and latent factor models to capture implicit relations among contributors in CN.…”

Section: Hybrid Techniquesmentioning

confidence: 99%

Automatic Identification of Similar Pull-Requests in GitHub’s Repositories Using Machine Learning

2022

View full text Add to dashboard Cite

Context: In a social coding platform such as GitHub, a pull-request mechanism is frequently used by contributors to submit their code changes to reviewers of a given repository. In general, these code changes are either to add a new feature or to fix an existing bug. However, this mechanism is distributed and allows different contributors to submit unintentionally similar pull-requests that perform similar development activities. Similar pull-requests may be submitted to review in parallel time by different reviewers. This will cause redundant reviewing time and efforts. Moreover, it will complicate the collaboration process. Objective: Therefore, it is useful to assign similar pull-requests to the same reviewer to be able to decide which pull-request to choose in effective time and effort. In this article, we propose to group similar pull-requests together into clusters so that each cluster is assigned to the same reviewer or the same reviewing team. This proposal allows saving reviewing efforts and time. Method: To do so, we first extract descriptive textual information from pull-requests content to link similar pull-requests together. Then, we employ the extracted information to find similarities among pull-requests. Finally, machine learning algorithms (K-Means clustering and agglomeration hierarchical clustering algorithms) are used to group similar pull-requests together. Results: To validate our proposal, we have applied it to twenty popular repositories from public dataset. The experimental results show that the proposed approach achieved promising results according to the well-known metrics in this subject: precision and recall. Furthermore, it helps to save the reviewer time and effort. Conclusion: According to the obtained results, the K-Means algorithm achieves 94% and 91% average precision and recall values over all considered repositories, respectively, while agglomeration hierarchical clustering performs 93% and 98% average precision and recall values over all considered repositories, respectively. Moreover, the proposed approach saves reviewing time and effort on average between (67% and 91%) by K-Means algorithm and between (67% and 83%) by agglomeration hierarchical clustering algorithm.

show abstract

“…Social network-based approaches identify various relationships among contributors to suggest reviewers [60], [61], [62]. Hybrid approaches rely on various combinations of techniques [63], [64], [65], [66].…”

Section: Related Workmentioning

confidence: 99%

Expanding the Number of Reviewers in Open-Source Projects by Recommending Appropriate Developers

Chueshev

Lawall

Bendraou

et al. 2020

2020 IEEE International Conference on Software Maintenance and Evolution (ICSME)

View full text Add to dashboard Cite

Code review is an important part of the development of any software project. Recently, many open source projects have begun practicing lightweight and tool-based code review (a.k.a modern code review) to make the process simpler and more efficient. However, those practices still require reviewers, of which there may not be sufficiently many to ensure timely decisions. In this paper, we propose a recommender-based approach to be used by open-source projects to increase the number of reviewers from among the appropriate developers. We first motivate our approach by an exploratory study of nine projects hosted on GitHub and Gerrit. Secondly, we build the recommender system itself, which, given a code change, initially searches for relevant reviewers based on similarities between the reviewing history and the files affected by the change, and then augments this set with developers who have a similar development history as these reviewers but have little or no relevant reviewing experience. To make these recommendations, we rely on collaborative filtering, and more precisely, on matrix factorization. Our evaluation shows that all nine projects could benefit from our system by using it both to get recommendations of previous reviewers and to expand their number from among the appropriate developers.

show abstract

RevRec: A two-layer reviewer recommendation algorithm in pull-based development model

Cited by 26 publications

References 25 publications

Reviewer Recommendation using Software Artifact Traceability Graphs

Reviewer Recommendation using Software Artifact Traceability Graphs

Automatic Identification of Similar Pull-Requests in GitHub’s Repositories Using Machine Learning

Expanding the Number of Reviewers in Open-Source Projects by Recommending Appropriate Developers

Contact Info

Product

Resources

About