Guided GUI testing of android apps with minimal restart and approximate learning

Abstract

Smartphones and tablets with rich graphical user interfaces (GUI) are becoming increasingly popular. Hundreds of thousands of specialized applications, called apps, are available for such mobile platforms. Manual testing is the most popular technique for testing graphical user interfaces of such apps. Manual testing is often tedious and error-prone. In this paper, we propose an automated technique, called SwiftHand, for generating sequences of test inputs for Android apps. The technique uses machine learning to learn a model of the app during testing, uses the learned model to generate user inputs that visit unexplored states of the app, and uses the execution of the app on the generated inputs to refine the model. A key feature of the testing algorithm is that it avoids restarting the app, which is a significantly more expensive operation than executing the app on a sequence of inputs. An important insight behind our testing algorithm is that we do not need to learn a precise model of an app, which is often computationally intensive, if our goal is to simply guide test execution into unexplored parts of the state space. We have implemented our testing algorithm in a publicly available tool for Android apps written in Java. Our experimental results show that we can achieve significantly better coverage than traditional random testing and L * -based testing in a given time budget. Our algorithm also reaches peak coverage faster than both random and L * -based testing.

EventBreak

“…As an alternative to the scenarios we evaluate here, EventBreak can build on other drivers for the first phase. First, EventBreak can build upon more sophisticated automated test generation approaches [6,34], which will explore an application more effectively than our random exploration. Second, EventBreak can leverage an existing suite of correctness tests and turn them into responsiveness tests by amplifying the initial execution.…”

“…The problem was reported by others and has been recognized by the developers. 6 Figure 4 shows the cost plot produced by EventBreak. The cost of handling e ef f ect , which loads the "Content" page, increases gradually and suddenly drops when Drupal crashes.…”

“…The approach again explores the potential slowdown pair from Section 6.3.1 and adds more and more articles because the 6 Total 120,086 3,563 89% Table 1. Usage scenarios for the evaluation.…”

“…The clos- 2 Issue 30274 est existing work falls into two categories. On the one hand, there are test generation approaches for event-driven applications [2,3,6,16,27,33,34]. They aim for high coverage of the application's source code, which is an appropriate goal for discovering correctness problems, but it does not necessarily expose responsiveness problems.…”

PATS: A Parallel GUI Testing Framework for Android Applications

“…Choi et al propose a testing mechanism SwiftHand to learn the GUI model based on the State-merging DFA (Deterministic Finite Automaton) induction machine learning algorithm [18]. In SwiftHand, The application source code needs to be inject some instrumentation code to learn the behavior pattern.…”