Fast code recommendation via approximate sub-tree matching

Software developers often write code that has similar functionality to existing code segments.A code recommendation tool that helps developers reuse these code fragments can significantly improve their efficiency.Several methods have been proposed in recent years.Some use sequence matching algorithms to find the related recommendations.Most of these methods are time-consuming and can leverage only low-level textual information from code.Others extract features from code and obtain similarity using numerical feature vectors.However,the similarity of feature vectors is often not equivalent to the original code’s similarity.Structural information is lost during the process of transforming abstract syntax trees into vectors.We propose an approximate sub-tree matching based method to solve this problem.Unlike existing tree-based approaches that match feature vectors,it retains the tree structure of the query code in the matching process to find code fragments that best match the current query.It uses a fast approximation sub-tree matching algorithm by transforming the sub-tree matching problem into the match between the tree and the list.In this way,the structural information can be used for code recommendation tasks that have high time requirements.We have constructed several real-world code databases covering different languages and granularities to evaluate the effectiveness of our method.The results show that our method outperforms two compared methods,SENSORY and Aroma,in terms of the recall value on all the datasets,and can be applied to large datasets.

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

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.