Today, Android applications (apps) have become ubiquitous in various aspects of life, highlighting the importance of graphical user interface (GUI) testing in ensuring their quality. To address the high cost associated with manual GUI testing, automated GUI test scripts are commonly developed. However, as Android apps evolve rapidly to offer more services or enhance existing ones, the GUI of these applications undergoes changes between the base and updated versions, leading to the obsolescence of existing GUI test scripts and increased maintenance costs. While existing repair approaches effectively manage minor GUI changes by replacing the locators of relevant widgets, they face challenges in addressing complex updates that bring significant GUI changes, thereby altering the interaction logic. This paper proposes a novel GUI test script repair approach to tackle these challenges. The approach utilizes an exploration and backtracking method to address the substantial GUI changes introduced by complex updates. By controlling the exploration space through exploration priority and pruning techniques, the approach achieves high effectiveness in repairing obsolete GUI tests caused by complex updates. We implemented our approach into a tool and evaluated its effectiveness and efficiency on 22 open-source Android apps and a total of 122 obsolete GUI test scripts. The experimental results showed that our approach can successfully repair 90% of the obsolete GUI test scripts.

Deep Neural Network (DNN) testing is one of the common practices to guarantee the quality of DNNs. However, DNN testing in general requires a significant amount of test inputs with oracle information (labels), which can be challenging and resource-intensive to obtain. To relieve this problem, we propose DATIS, a distance-aware test input selection approach for DNNs. Specifically, DATIS adopts a two-step approach for selecting test inputs. In the first step, it selects test inputs based on improved uncertainty scores derived from the distances between the test inputs and their nearest neighbor training samples. In the second step, it further eliminates test inputs that may cover the same faults by examining the distances among the selected test inputs. To evaluate DATIS, we conduct extensive experiments on 8 diverse subjects, taking into account different domains of test inputs, varied DNN structures, and diverse types of test inputs. Evaluation results show that DATIS significantly outperforms 15 baseline approaches in both selecting test inputs with high fault-revealing power and guiding the selection of data for DNN enhancement.

Automated GUI testing through the reuse of existing tests has recently gained prominence in research. Cross-platform migration of GUI tests between different platform versions of an application offers a promising opportunity for test reuse. Widget mapping, identifying similarities between source and target application widgets and connecting semantically analogous pairs, is central to these approaches. Vision-based widget mapping approaches are supposed to provide platform-agnostic solutions more suitable for cross-platform migration, considering that different platform versions frequently display strong resemblances in the appearance of their semantically similar widgets. However, the efficacy of vision-based widget mapping for cross-platform migration remains limited and the reasons remain unclear. In this paper, we present the first comprehensive investigation of vision-based widget mapping for cross-platform GUI test migration. We devote considerable effort to constructing a dataset consisting of 6,730 bi-directional mapped widget pairs across the iOS and Android platforms, and categorize the mapped widgets into eight classifications to thoroughly assess the capabilities of various approaches. We implement 89 configurations, derived from five distinct vision-based widget mapping methodologies, and evaluate their performance utilizing our dataset. Our findings reveal valuable insights that can be employed to advance vision-based widget mapping techniques: (1) The current approach exhibits potential for improvement, as certain configurations demonstrate superior performance in comparison to existing methods; (2) Some features can adversely impact the mapping, requiring more consideration; (3) A substantial proportion of mapped widgets display varying inconsistent contents in their appearance, which require more sophisticated vision algorithms.

The development of mobile apps has become the current mantra for any business' success. The rise of many types of mobile devices and mobile OS has instantly created the need to develop multiple versions for the same app. In order to grasp as much market share as possible, it is desirable to have all the versions of an app demonstrate similar user interface (UI) appearances, to make users feel comfortable when switching from one platform to another and more likely to stick to the app. However, to ensure consistent UIs among cross-platform versions can be a challenging and costly endeavor, since different platforms have their own UI controls and programming languages. In this paper, we propose an automatic approach to transforming mobile app UIs across platforms, and illustrate our approach by transforming the UIs of iOS apps to Android ones. We leverage the enormous existing apps carefully designed by developers to achieve similar UI effects between iOS and Android versions, since these apps contain valuable knowledge of mapping relations between the iOS and Android UI controls. Starting from the reverse engineering of these apps, our approach separates each user interface into modules of adequate sizes. Then it maps the modules from both versions that contribute to the same visual and functional effect, and automatically mines the mapping relations. By applying the mined relations, our approach has successfully transformed the iOS app UIs into Android app UIs, as confirmed by a series of experiments.

Modern software systems rely on information networks for communication. Such information networks are inherently unpredictable and unreliable. Consequently, software systems behave in an unstipulated manner in uncertain network conditions. Discovering unknown behaviors of these software systems in uncertain network conditions is essential to ensure their correct behaviors. Such discovery requires the development of systematic and automated methods. We propose an online and iterative model-based testing approach to evolve test models with search algorithms. Our ultimate aim is to discover unknown expected behaviors that can only be observed in uncertain network conditions. Also, we have implemented an adaptive search-based test case generation strategy to generate test cases that are executed on the system under test. We evaluated our approach with an open source video conference application-Jitsi with three search algorithms in comparison with random search. Results show that our approach is efficient in discovering unknown system behaviors. In particular, (1+1) Evolutionary Algorithm outperformed the other algorithms.