  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.
