Abstract
Android applications are widely used around the world. Most of these applications contain potential crashes. Many recent academic studies focus on black-box testing of Android applications to detect these crashes. A simple random testing tool, Monkey, detects more crashes than the state-of-the-art black-box testing tools, but can not reach some activities that are located deep within the application. We propose an hybrid approach that combines our model-learning tool, AndroFrame, and Monkey. With this hybrid approach, we aim to increase activity coverage and improve crash detection. We conduct experiment on 20 Android applications. As a result, our hybrid approach achieves 2% more activity coverage and detects 21 more crashes compared to AndroFrame. Compared to Monkey, our hybrid approach achieves 24% more coverage and detects 5 more crashes.
| Translated title of the contribution | Combining model-based and random approaches to improve crash detection in android |
|---|---|
| Original language | Turkish |
| Pages (from-to) | 89-100 |
| Number of pages | 12 |
| Journal | CEUR Workshop Proceedings |
| Volume | 1980 |
| Publication status | Published - 2017 |
| Externally published | Yes |
| Event | 11th Turkish National Software Engineering Symposium, UYMS 2017 - Alanya, Turkey Duration: 18 Oct 2017 → 20 Oct 2017 |