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Register a new userAutomating App Review Response Generation Based on Contextual Knowledge
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Added by
Zhou Wenjie
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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User experience of mobile apps is an essential ingredient that can influence the audience volumes and app revenue. To ensure good user experience and assist app development, several prior studies resort to analysis of app reviews, a type of app repository that directly reflects user opinions about the apps. Accurately responding to the app reviews is one of the ways to relieve user concerns and thus improve user experience. However, the response quality of the existing method relies on the pre-extracted features from other tools, including manually-labelled keywords and predicted review sentiment, which may hinder the generalizability and flexibility of the method. In this paper, we propose a novel end-to-end neural network approach, named CoRe, with the contextual knowledge naturally incorporated and without involving external tools. Specifically, CoRe integrates two types of contextual knowledge in the training corpus, including official app descriptions from app store and responses of the retrieved semantically similar reviews, for enhancing the relevance and accuracy of the generated review responses. Experiments on practical review data show that CoRe can outperform the state-of-the-art method by 11.53% in terms of BLEU-4, an accuracy metric that is widely used to evaluate text generation systems.
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Code reviews are popular in both industrial and open source projects. The benefits of code reviews are widely recognized and include better code quality and lower likelihood of introducing bugs. However, since code review is a manual activity it comes at the cost of spending developers time on reviewing their teammates code. Our goal is to make the first step towards partially automating the code review process, thus, possibly reducing the manual costs associated with it. We focus on both the contributor and the reviewer sides of the process, by training two different Deep Learning architectures. The first one learns code changes performed by developers during real code review activities, thus providing the contributor with a revised version of her code implementing code transformations usually recommended during code review before the code is even submitted for review. The second one automatically provides the reviewer commenting on a submitted code with the revised code implementing her comments expressed in natural language. The empirical evaluation of the two models shows that, on the contributor side, the trained model succeeds in replicating the code transformations applied during code reviews in up to 16% of cases. On the reviewer side, the model can correctly implement a comment provided in natural language in up to 31% of cases. While these results are encouraging, more research is needed to make these models usable by developers.
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