Code review is an important process to reduce code defects and improve software quality. In social coding communities like GitHub, as everyone can submit Pull-Requests, code review plays a more important role than eve...Code review is an important process to reduce code defects and improve software quality. In social coding communities like GitHub, as everyone can submit Pull-Requests, code review plays a more important role than ever before, and the process is quite time-consuming. Therefore, finding and recommending proper reviewers for the emerging Pull-Requests becomes a vital task. However, most of the current studies mainly focus on recommending reviewers by checking whether they will participate or not without differentiating the participation types. In this paper, we develop a two-layer reviewer recommendation model to recommend reviewers for Pull-Requests （PRs） in GitHub projects from the technical and managerial perspectives. For the first layer, we recommend suitable developers to review the target PRs based on a hybrid recommendation method. For the second layer, after getting the recommendation results from the first layer, we specify whether the target developer will technically or managerially participate in the reviewing process. We conducted experiments on two popular projects in GitHub, and tested the approach using PRs created between February 2016 and February 2017. The results show that the first layer of our recommendation model performs better than the previous work, and the second layer can effectively differentiate the types of participation.展开更多
Code reviews in pull-based model are open to community users on GitHub. Various participants are taking part in the review discussions and the review topics are not only about the improvement of code contributions but...Code reviews in pull-based model are open to community users on GitHub. Various participants are taking part in the review discussions and the review topics are not only about the improvement of code contributions but also about project evolution and social interaction. A comprehensive understanding of the review topics in pull-based model would be useful to better organize the code review process and optimize review tasks such as reviewer recommendation and pull-request prioritization. In this paper, we first conduct a qualitative study on three popular open-source software projects hosted on GitHub and construct a fine-grained two-level taxonomy covering four level-1 categories （code correctness, pull- request decision-making, project management, and social interaction） and 11 level-2 subcategories （e.g., defect detecting, reviewer assigning, contribution encouraging）. Second, we conduct preliminary quantitative analysis on a large set of review comments that were labeled by TSHC （a two-stage hybrid classification algorithm）, which is able to automatically classify review comments by combining rule-based and machine-learning techniques. Through the quantitative study, we explore the typical review patterns. We find that the three projects present similar comments distribution on each subeategory. Pull-requests submitted by inexperienced contributors tend to contain potential issues even though they have passed the tests. Furthermore, external contributors are more likely to break project conventions in their early contributions.展开更多
Communication and coordination between OSS developers who do not work physically in the same location have always been the challenging issues.The pull-based development model,as the state-of-art collaborative developm...Communication and coordination between OSS developers who do not work physically in the same location have always been the challenging issues.The pull-based development model,as the state-of-art collaborative development mechanism,provides high openness and transparency to improve the visibility of contributors'work.However,duplicate contributions may still be submitted by more than one contributors to solve the same problem due to the parallel and uncoordinated nature of this model.If not detected in time,duplicate pull-requests can cause contributors and reviewers to waste time and energy on redundant work.In this paper,we propose an approach combining textual and change similarities to automatically detect duplicate contributions in pull-based model at submission time.For a new-arriving contribution,we first compute textual similarity and change similarity between it and other existing contributions.And then our method returns a list of candidate duplicate contributions that are most similar with the new contribution in terms of the combined textual and change similarity.The evaluation shows that 83.4%of the duplicates can be found in average when we use the combined textual and change similarity compared to 54.8%using only textual similarity and 78.2%using only change similarity.展开更多
基金Project(2016-YFB1000805)supported by the National Grand R&D Plan,ChinaProjects(61502512,61432020,61472430,61532004)supported by the National Natural Science Foundation of China
文摘Code review is an important process to reduce code defects and improve software quality. In social coding communities like GitHub, as everyone can submit Pull-Requests, code review plays a more important role than ever before, and the process is quite time-consuming. Therefore, finding and recommending proper reviewers for the emerging Pull-Requests becomes a vital task. However, most of the current studies mainly focus on recommending reviewers by checking whether they will participate or not without differentiating the participation types. In this paper, we develop a two-layer reviewer recommendation model to recommend reviewers for Pull-Requests （PRs） in GitHub projects from the technical and managerial perspectives. For the first layer, we recommend suitable developers to review the target PRs based on a hybrid recommendation method. For the second layer, after getting the recommendation results from the first layer, we specify whether the target developer will technically or managerially participate in the reviewing process. We conducted experiments on two popular projects in GitHub, and tested the approach using PRs created between February 2016 and February 2017. The results show that the first layer of our recommendation model performs better than the previous work, and the second layer can effectively differentiate the types of participation.
基金This work was supported by the National Key Research and Development Program of China under Grant No. 2016YFB1000805 and the National Natural Science Foundation of China under Grant Nos. 61432020, 61303064, 61472430 and 61502512.
文摘Code reviews in pull-based model are open to community users on GitHub. Various participants are taking part in the review discussions and the review topics are not only about the improvement of code contributions but also about project evolution and social interaction. A comprehensive understanding of the review topics in pull-based model would be useful to better organize the code review process and optimize review tasks such as reviewer recommendation and pull-request prioritization. In this paper, we first conduct a qualitative study on three popular open-source software projects hosted on GitHub and construct a fine-grained two-level taxonomy covering four level-1 categories （code correctness, pull- request decision-making, project management, and social interaction） and 11 level-2 subcategories （e.g., defect detecting, reviewer assigning, contribution encouraging）. Second, we conduct preliminary quantitative analysis on a large set of review comments that were labeled by TSHC （a two-stage hybrid classification algorithm）, which is able to automatically classify review comments by combining rule-based and machine-learning techniques. Through the quantitative study, we explore the typical review patterns. We find that the three projects present similar comments distribution on each subeategory. Pull-requests submitted by inexperienced contributors tend to contain potential issues even though they have passed the tests. Furthermore, external contributors are more likely to break project conventions in their early contributions.
基金This work was supported by the National Key Research and Development Program of China under Grant No. 2018YFB1004202the National Natural Science Foundation of China under Grant No. 61702534.
文摘Communication and coordination between OSS developers who do not work physically in the same location have always been the challenging issues.The pull-based development model,as the state-of-art collaborative development mechanism,provides high openness and transparency to improve the visibility of contributors'work.However,duplicate contributions may still be submitted by more than one contributors to solve the same problem due to the parallel and uncoordinated nature of this model.If not detected in time,duplicate pull-requests can cause contributors and reviewers to waste time and energy on redundant work.In this paper,we propose an approach combining textual and change similarities to automatically detect duplicate contributions in pull-based model at submission time.For a new-arriving contribution,we first compute textual similarity and change similarity between it and other existing contributions.And then our method returns a list of candidate duplicate contributions that are most similar with the new contribution in terms of the combined textual and change similarity.The evaluation shows that 83.4%of the duplicates can be found in average when we use the combined textual and change similarity compared to 54.8%using only textual similarity and 78.2%using only change similarity.
