GUI에 기반한 모바일 앱 사용상태 구분
GUI-based Detection of Usage-state Changes in Mobile Apps
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초록
모바일 앱의 신뢰성 향상과 개발 환경 변화라는 제약 조건을 모두 만족시키려면 모바일 앱의 동작을 자동으로 검증할 필요가 있다. 모바일 앱의 동작 검증 과정에서 다양한 이슈가 발생하나, 사용 상태 변화 탐지도 중요한 이슈 중 하나이다. 본논문에서는 모바일 앱의 사용 상태 변화 탐지를 위하여 딥뉴럴넷을 이용하여 모바일 앱 GUI의 UI 위젯을 인식한 후 인식된위젯간의 관계를 그래프로 변환하고, 변환된 그래프의 그래프 엔트로피를 계산하여 사용 상태 변화를 감지하는 방법을 제안한다. 제안 방법은 SIFT(Scale-Invariant Feature Transform)에 기반한 감지 방법과 비교되었으며 20개의 실제 모바일 앱의동작 데이터를 통해 검증한 결과 대부분의 경우 제안 방법이 우수하나, 엔트로피 계산이 어려울 때는 제안 방법의 성능이 저하됨을 확인하였다.
Abstract
Under the conflicting objectives of maximum user satisfaction and fast launching, there exist great needs forautomated mobile app testing. In automated app testing, detection of usage-state changes is one of the most importantissues for minimizing human intervention and testing of various usage scenarios. Because conventional approachesutilizing pre-collected training examples can not handle the rapid evolution of apps, we propose a novel methoddetecting changes in usage-state through graph-entropy. In the proposed method, widgets in a screen shot arerecognized through DNNs and `onverted graphs. We compared the performance of the proposed method with a SIFT(Scale-Invariant Feature Transform) based method on 20 real-world apps. In most cases, our method achievedsuperior results, but we found some situations where further improvements are required.
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