Mobile applications have become an inseparable part of people's daily life. Nonetheless, the market competition is extremely fierce, and apps lacking recognition among most users are susceptible to market elimination. To this end, developers must swiftly and accurately apprehend the requirements of the wider user base to effectively strategize and promote their apps' orderly and healthy evolution. The rate at which general user requirements are adopted by developers, or user contribution, is a very valuable metric that can be an important tool for app developers or software engineering researchers to measure or gain insight into the evolution of app requirements and predict the evolution of app software. Regrettably, the landscape lacks refined quantitative analysis approaches and tools for this pivotal indicator. To address this problem, this paper exploratively proposes a quantitative analysis approach based on the temporal correlation perception that exists in the app update log and user reviews, which provides a feasible solution for quantitatively obtaining the user contribution. The main idea of this scheme is to consider valid user reviews as user requirements and app update logs as developer responses, and to mine and analyze the pairwise and chronological relationships existing between the two by text computing, thus constructing a feasible approach for quantitatively calculating user contribution. To demonstrate the feasibility of the approach, this paper collects data from four Chinese apps in the App Store in mainland China and one English app in the U.S. region, including 2,178 update logs and 4,236,417 user reviews, and from the results of the experiment, it was found that 16.6%-43.2% of the feature of these apps would be related to the drive from the online popular user requirements.

翻译：移动应用已成为人们日常生活中不可或缺的一部分。然而，市场竞争极为激烈，缺乏多数用户认可的应用极易被市场淘汰。为此，开发者必须快速准确地把握更广泛用户群体的需求，以有效制定策略，促进其应用有序且健康地演进。一般用户需求被开发者采纳的比率，即用户贡献度，是一项极具价值的指标，可为应用开发者或软件工程研究人员提供重要工具，用于衡量或洞察应用需求的演进过程，并预测应用软件的演变。遗憾的是，当前领域缺乏针对这一关键指标的精细化定量分析方法与工具。为应对此问题，本文探索性地提出了一种基于应用更新日志与用户评论中存在的时序关联感知的定量分析方法，为定量获取用户贡献度提供了可行方案。该方案的核心思想是将有效用户评论视为用户需求，将应用更新日志视为开发者响应，通过文本计算挖掘并分析二者之间的成对与时序关系，从而构建一种可行的用户贡献度定量计算方法。为论证该方法的可行性，本文收集了来自中国大陆地区 App Store 中四款中文应用及美国地区一款英文应用的数据，包括 2,178 条更新日志与 4,236,417 条用户评论。实验结果表明，这些应用 16.6% 至 43.2% 的功能特征与在线热门用户需求的驱动相关。