Madusa: mobile application demo generation based on usage scenarios
- Authors
- Lee, Jaehyung; Cho, Hangyeol; Lee, Woosuk
- Issue Date
- May-2023
- Publisher
- Kluwer Academic Publishers
- Keywords
- Demo generation; Mobile applications; Integer linear programming; Android; Static analysis for android
- Citation
- Automated Software Engineering, v.30, no.1, pp 1 - 25
- Pages
- 25
- Indexed
- SCIE
SCOPUS
- Journal Title
- Automated Software Engineering
- Volume
- 30
- Number
- 1
- Start Page
- 1
- End Page
- 25
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/112776
- DOI
- 10.1007/s10515-022-00372-8
- ISSN
- 0928-8910
1573-7535
- Abstract
- Mobile applications have grown rapidly in size. This dramatic increases in size and complexity make mobile applications less accessible to a broader scope of users. The prevailing approach for better accessibility of mobile applications is to manually reimplement slimmed versions with a small but representative portion of a regular original app. Unfortunately, this approach imposes significant burden on developers. We propose a system called Madusa to enable developers to effectively customize and reduce their mobile applications for Android. Madusa takes as input an original app, an upper bound on the size of a reduced version, and usage scenarios as a high-level specification of its desired core functionality. The output is a reduced version of the app that is still correct with respect to the specification while not exceeding the size limit. Madusa constructs a graph representing dependencies among methods and resources and identifies a sub-part of the graph using integer linear programming to generate a reduced version that exhibits behaviors as similar as possible to the original app. Our experimental evaluation on a suite of 19 Android apps available on Google Play Store. Madusa effectively converges to the desired simplified apps by reducing the app size by 40% on average (maximally by 60%). We conclude our approach effectively removes redundant code and resources with respect to given usage scenarios.
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