Previous studies showed that replying to a user review usually has a positive effect on the rating that is given by the user to the app. For example, Hassan et al. found that responding to a review increases the chances of a user updating their given rating by up to six times compared to not responding. To alleviate the labor burden in replying to the bulk of user reviews, developers usually adopt a template-based strategy where the templates can express appreciation for using the app or contain the company email for users to react. However, reading large numbers of user reviews every day is not an easy task for developers. The available review-response pairs provide us chances to learn the knowledge relations between reviews and responses.
Although there exists research on studying the popular review patterns (e.g., reviews with longer content and lower rating) that developers tend to respond, approaches to automate the review response process have never been proposed. Inspired by the RNN encoder-decoder model in the natural language processing field, we propose a response generation framework, named RRGen. RRGen explicitly incorporates review attributes, such as user rating and review length, and learns the relations between reviews and corresponding responses in a supervised way from the available training data. Experiments on 58 apps and 309,246 review-response pairs highlight that RRGen outperforms several baselines by 67.4% to 4.5 times in terms of BLEU (an accuracy measure that is widely used to evaluate generation systems). Qualitative analysis also confirms the effectiveness of RRGen in generating the relevant and accurate response.
Tue 12 NovDisplayed time zone: Tijuana, Baja California change
13:40 - 15:20
|Discovering, Explaining and Summarizing Controversial Discussions in Community Q&A Sites|
Xiaoxue Ren Zhejiang University, Zhenchang Xing Australia National University, Xin Xia Monash University, Guoqiang Li Shanghai Jiao Tong University, Jianling Sun Zhejiang UniversityPre-print
|Automating App Review Response Generation|
Cuiyun Gao Nanyang Technological University, Singapore, Jichuan Zeng The Chinese University of Hong Kong, Xin Xia Monash University, David Lo Singapore Management University, Michael Lyu The Chinese University of Hong Kong, Irwin King The Chinese University of Hong KongPre-print
|Automatic Generation of Pull Request DescriptionsACM SIGSOFT Distinguished Paper Award|
Zhongxin Liu Zhejiang University, Xin Xia Monash University, Christoph Treude The University of Adelaide, David Lo Singapore Management University, Shanping Li Zhejiang UniversityPre-print
|Recommending Who to Follow in the Software Engineering Twitter Space|
Journal First Presentations
Abhishek Sharma Singapore Management University, Singapore, Yuan Tian Queens University, Kingston, Canada, Agus Sulistya School of Information Systems, Singapore Management University, Dinusha Wijedasa School of Information Systems, Singapore Management University, David Lo Singapore Management UniversityPre-print
|Developer Reputation Estimator (DRE)|
|CocoQa: Question Answering for Coding Conventions over Knowledge Graphs|
Tianjiao Du Shanghai JiaoTong University, Junming Cao Shanghai JiaoTong University, Qinyue Wu Shanghai JiaoTong University, Wei Li Shanghai JiaoTong University, Beijun Shen School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Yuting Chen Shanghai Jiao Tong University