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Furthermore, through readily available, downloadable toolkits, users can write their own applications to make use of a smart device's hardware.

They can subsequently publish their software in the central app store for users to download (and possibly pay for).

We define the following App Store Analysis subfields, based on the literature gathered through the process explained in Section 2: “API Analysis”, which is discussed in Section 6; “Feature Analysis”, which is discussed in Section 7; “Release Engineering”, which is discussed in Section 8; “Review Analysis”, which is discussed in Section 9; “Security”, which is discussed in Section 10; “Store Ecosystem”, which is discussed in Section 11; and “Size and Effort Prediction”, which is discussed in Section 12.

Closely related work is discussed in Section 13; guidelines and recommendations for future app store analysis authors are outlines in Section 14; we identify potential future directions in Section 15, and conclude our findings in Section 16.

It is our aim to encompass this evolution as best we can through the stated definitions, in the hope that future surveys will be able to build upon this work and the App Store Analysis literature to come.

These attributes can be both non-technical and technical, depending on how they are obtained.

It is the user-submitted content that fundamentally distinguishes app stores from the ad-hoc commercially available applications that existed beforehand.

As a result, software engineering researchers have access to large numbers of software applications with customer feedback and commercial performance data, unavailable in previous software deployment mechanisms.

In this section, we describe the process used to find literature, including our scope, search terms and repositories and lessons learned for future app store analysis surveyors.

App Store Analysis literature encompasses studies that perform analysis on a collection of apps mined from an App Store.

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