Search-Based Test Generation Targeting Non-Functional Quality Attributes of Android Apps
Paper in proceeding, 2023

Mobile apps form a major proportion of the software marketplace and it is crucial to ensure that they meet both functional and nonfunctional quality thresholds. Automated test input generation can reduce the cost of the testing process. However, existing Android test generation approaches are focused on code coverage and cannot be customized to a tester's diverse goals---in particular, quality attributes such as resource use.

We propose a flexible multi-objective search-based test generation framework for interface testing of Android apps---STGFA-SMOG. This framework allows testers to target a variety of fitness functions, corresponding to different software quality attributes, code coverage, and other test case properties. We find that STGFA-SMOG outperforms random test generation in exposing potential quality issues and triggering crashes. Our study also offers insights on how different combinations of fitness functions can affect test generation for Android apps.

Software Quality

Search-Based Software Engineering

Automated Test Generation

Search-Based Test Generation

Quality Attributes


Teklit Berihu Gereziher

Student at Chalmers

Selam Welu Gebrekrstos

Student at Chalmers

Gregory Gay

University of Gothenburg

GECCO '23: Proceedings of the Genetic and Evolutionary Computation Conference

9798400701191 (ISBN)

GECCO '23: Proceedings of the Genetic and Evolutionary Computation Conference
Porto, Portugal,

Context-Infused Automated Software Test Generation

Swedish Research Council (VR) (2019-05275), 2020-01-01 -- 2023-12-31.

Subject Categories

Software Engineering

Information Science

Computer Science





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