Abstract
App reviews found in app stores can provide critically valuable information to help software engineers understand user requirements and to design, debug, and evolve software products. Over the last ten years, a vast amount of research has been produced to study what useful information might be found in app reviews, and how to mine and organise such information as efficiently as possible. This paper presents a comprehensive survey of this research, covering 182 papers published between 2012 and 2020. This survey classifies app review analysis not only in terms of mined information and applied data mining techniques but also, and most importantly, in terms of supported software engineering activities. The survey also reports on the quality and results of empirical evaluation of existing techniques and identifies important avenues for further research. This survey can be of interest to researchers and commercial organisations developing app review analysis techniques and to software engineers considering to use app review analysis.
Highlights
App stores have become important platforms for the distribution of software products
We presented a systematic literature review of the research on analysing app reviews for software engineering
Research on analysing app reviews are published in the main software engineering conferences and journals e.g., International Conference on Software Engineering (ICSE), TSE or Empirical Software Engineering Journal (EMSE) and the number of publications has tripled in the last four years
Summary
App stores have become important platforms for the distribution of software products. In 2020, Google Play Store and Apple Store host over 5 million apps and are widely used for the discovery, purchase and updates of software products (Clement 2020) The emergence of these App Stores have had important effects on software engineering practices, notably by bridging the gap between developers and users, by increasing market transparency and by affecting release management (AlSubaihin et al 2019). Most reviews have length up to 675 characters (Pagano and Maalej 2013); and convey information on variety of topics such as feature requests, bug reports or user opinions (Martin et al 2017; Al-Hawari 2020) Analysing these reviews can benefit a range of software engineering activities. – identify and classify the range of app review analysis proposed in the literature; – identify the range of natural language processing and data mining techniques that support such analysis; – identify the range of software engineering activities that app review analysis can support; Page 3 of 63 43
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