DALT: Deep Activity Launching Test via Intent-Constraint Extraction

• Published in: 2022 IEEE 33rd International Symposium on Software Reliability Engineering (ISSRE) pp. 482 - 493
• Main Authors: Liu, Ao, Guo, Chenkai, Dong, Naipeng, Wang, Yinjie, Xu, Jing
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2022
• Subjects: Activity launching; Android application; Bug detection; Codes; Computer bugs; Inter-component communication; Operating systems; Proposals; Software reliability; Static analysis; Symbolic execution
• ISSN: 2332-6549
• DOI: 10.1109/ISSRE55969.2022.00053

The frequent usage of the activity and intent in Android app development makes activity launching communication the focus of app analysis, which inspires the proposal of Activity Launching Test (ALT). Existing static analysis approaches are limited in test case generation and crash triggering of ALT, due to their path-insensitive nature and insufficient intent attribute exploration. This work proposes DALT, an activity test frame-work for launching-related bug detection, which empowers an inter-procedural, context-, flow- and path-sensitive static analysis to generate proper intents as test cases. By tailoring symbolic execution for intent propagation, DALT is able to explore statements in deep code position and extract conditional constraints in diverse launching-related execution paths. Consequently, invalid test paths are substantially reduced via the guidance of the extracted constraints. DALT also supports significantly more intent attribute types and value types of attributes, by reformatting them to be compatible with the commonly used constraint solvers. Extensive comparison experiments have been conducted from diverse validation dimensions. The results demonstrate that compared to the state-of-the-art approach, DALT is more effective in successfully launching activities and detecting bugs hiding in deep positions of the program paths.