Because of rapid growth in mobile application markets, competition between companies that provide similar applications has become fierce. To improve user satisfaction for keeping existing users and attracting new users, application developers need to quickly respond to customer feedback regarding functionality and performance defects. In software engineering, specifying an accurate evolution plan according to user feedback is useful but quite difficult. Hence, we propose an approach for predicting and recommending evolution plans to application developers that includes: (1) when a new version of an App should be released; (2) which features should be updated in the next version and (3) if a new version is released, to what degree users would like or dislike it. This approach is based on an elaborate text analysis of massive numbers of user reviews and App update histories. A collocation-based mRAKE method is presented to extract requested and updated features from user reviews and update logs, and the intensity and sentiment scores of each feature are calculated to quantitatively represent time-series histories of App updates and user requests. Machine learning algorithms including linear support vector, Gaussian naïve Bayes and logistic regression are employed to discover the underlying correlation between user opinions embedded in their reviews and the App update behaviors of developers, and rich experiments were conducted on real data to validate the effectiveness of the proposed approach. Overall, our approach can achieve an average accuracy of 72.8% and 93.7% in release time recommendation and content updates of successive versions, respectively, and it can predict user reactions to a planned version with an average accuracy of above 89.0%.

中文翻译：

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从用户评论和更新日志预测移动服务演进的方法

由于移动应用市场的快速增长，提供类似应用的公司之间的竞争变得激烈。为了提高用户对保留现有用户和吸引新用户的满意度，应用程序开发人员需要快速响应客户关于功能和性能缺陷的反馈。在软件工程中，根据用户反馈指定一个准确的进化计划是有用的，但相当困难。因此，我们提出了一种向应用程序开发人员预测和推荐进化计划的方法，包括：（1）何时发布应用程序的新版本；（2）下一个版本应该更新哪些功能；（3）如果发布了新版本，用户喜欢或不喜欢它的程度。这种方法基于对大量用户评论和应用程序更新历史的详尽文本分析。提出了一种基于搭配的 mRAKE 方法，从用户评论和更新日志中提取请求和更新的特征，并计算每个特征的强度和情感得分，以定量表示 App 更新和用户请求的时间序列历史。采用线性支持向量、高斯朴素贝叶斯和逻辑回归等机器学习算法来发现评论中嵌入的用户意见与开发者的应用更新行为之间的潜在相关性，并在真实数据上进行了丰富的实验以验证其有效性建议的方法。总体而言，我们的方法可以达到 72.8% 和 93 的平均准确率。