Improving the exploration strategy of an automated android GUI testing tool based on the Q-Learning algorithm by selecting potential actions

• Main Authors: Goh, Kwang Yi, Baharom, Salmi, Din, Jamilah
• Format: Article
• Published: Science Publication 2022
• Online Access: http://psasir.upm.edu.my/id/eprint/101827/; https://thescipub.com/abstract/jcssp.2022.90.102

Researchers have proposed automated testing tools to minimise the effort and resources spent on testing GUIs. A relatively simple strategy employed by the proposed tools thus far is the observe-select-execute approach, where all of a GUI’s actions on its current state are observed, one action is selected and the selected action is executed on the software. The strategy’s key function is to select an action that may achieve new and desirable GUI states. Due to difficulties in comparing actions, most existing test generators ignore this step and randomly select an action. However, a randomly selected action has limitations. It does not test most parts of a GUI within a reasonable amount of time and there is a high probability that the same actions are re-selected. This reduces code coverage, thereby resulting in undetected failures. To overcome this limitation, the Q-Learning algorithm was proposed by several researchers to minimise randomness. The idea was to change the probability distribution over the sequence space. Instead of making purely random selections, the least frequently executed action is selected so that the GUI can be further explored. Q-Learning showed better results than the random exploration strategy but it also presented a weakness. Q-Learning’s reward function assigns the highest value to the least frequently executed action without taking into consideration its potential ability in detecting failures. Furthermore, the proposed techniques based on the Q-Learning algorithm do not consider context-based actions. Thus, these techniques are unable to detect failures that occur due to the improper use of context data, which is becoming an increasingly common issue in mobile applications nowadays. We propose a tool, namely the Crash Droid, that allows the automation of testing context-aware Android applications. We utilise the Q-Learning algorithm to compare actions, including context-based actions, to effectively detect crashes and achieve a higher code coverage.