Core features (functionalities) of an app can often be accessed and invoked in several ways, i.e., through alternative sequences of user-interface (UI) interactions. Given the manual effort of writing tests, developers often only consider the typical way of invoking features when creating the tests (i.e., the “sunny day scenario”). However, the alternative ways of invoking a feature are as likely to be faulty. These faults would go undetected without proper tests. To reduce the manual effort of creating UI tests and help developers more thoroughly examine the features of apps, we present Route , an automated tool for feature-based UI test augmentation for Android apps. Route first takes a UI test and the app under test as input. It then applies novel heuristics to find additional high-quality UI tests, consisting of both inputs and assertions, that verify the same feature as the original test in alternative ways. Application of Route on several dozen tests for popular apps on Google Play shows that for 96% of the existing tests, Route was able to generate at least one alternative test. Moreover, the fault detection effectiveness of augmented test suites in our experiments showed substantial improvements of up to 39% over the original test suites.

GUI testing is important for examining the end-to-end workflows and usability of GUI-based software. To reduce the manual effort of writing GUI tests, recent research has explored the potential of automatically reusing GUI tests by transferring them across similar applications. However, what is missing from the prior work is that such transfer may be required for apps available on different platforms. In particular, both web and Android are dominant platforms on which many organizations provide their software services. At the state-of-the-practice, even if the web and Android versions of an app provisioned by an organization substantially share the functionality, the developers have to manually write separate sets of tests for each version. This paper proposes TransDroid, an automated tool that transfers GUI tests from a web app to its Android counterpart. Evaluation of TransDroid on real-world web and Android apps corroborates its effectiveness by achieving 77% success rate among the attempted transfers, along with 82% precision and 99% recall in the mapping of the GUI events and oracles.

For 15% of the world population with disabilities, accessibility is arguably the most critical software quality attribute. The ever-growing reliance of users with disability on mobile apps further underscores the need for accessible software in this domain. Existing automated accessibility assessment techniques primarily aim to detect violations of predefined guidelines, thereby produce a massive amount of accessibility warnings that often overlook the way software is actually used by users with disability. This paper presents a novel, high-fidelity form of accessibility testing for Android apps, called Latte, that automatically reuses tests written to evaluate an app’s functional correctness to assess its accessibility as well. Latte first extracts the use case corresponding to each test, and then executes each use case in the way disabled users would, i.e., using assistive services. Our empirical evaluation on real-world Android apps demonstrates Latte’s effectiveness in detecting substantially more useful defects than prior techniques.

Automated testing of mobile apps has received significant attention in recent years from researchers and practitioners alike. In this paper, we report on the largest empirical study to date, aimed at understanding the test automation culture prevalent among mobile app developers. We systematically examined more than 3.5 million repositories on GitHub and identified more than 12, 000 non-trivial and real-world Android apps. We then analyzed these non-trivial apps to investigate (1) the prevalence of adoption of test automation; (2) working habits of mobile app developers in regards to automated testing; and (3) the correlation between the adoption of test automation and the popularity of projects. Among others, we found that (1) only 8% of the mobile app development projects leverage automated testing practices; (2) developers tend to follow the same test automation practices across projects; and (3) popular projects, measured in terms of the number of contributors, stars, and forks on GitHub, are more likely to adopt test automation practices. To understand the rationale behind our observations, we further conducted a survey with 148 professional and experienced developers contributing to the subject apps. Our findings shed light on the current practices and future research directions pertaining to test automation for mobile app development.

In this article, we present a high delay resolution and all-digital transmit-beamforming circuit for use in high-frequency and wearable ultrasound imaging systems. The proposed transmit beamformer comprises an all-digital delay-locked loop (ADDLL), a cycle-based beamforming delay line (CBDL), and a pulse controller. ADDLL can provide specific digital control code for the CBDL to generate stable and suitable delays to excite array transducer elements without variations in process, voltage, and temperature. CBDL not only provides a finer delay resolution for high-frequency ultrasound imaging systems but also requires a few delay cells to maintain the duty cycle of long prorogation signals, thereby significantly reducing hardware costs. The pulse controller can adjust the number and width of output pulses to meet the requirements of different ultrasonic transducers and Doppler ultrasound color imaging. The proposed transmit beamformer uses a 0.18-$\mu \text{m}$CMOS technology for a 30-MHz high-frequency linear array with 256 elements, and the focusing depth is from 3.5 to 8.1 mm. The measurement results indicate that the proposed transmit-beamforming design can achieve a maximum time delay of 562.6 ns with an average time resolution of 535 ps. The proposed design offers a smaller volume and finer delay resolution, and it is highly suitable for wearable ultrasound imaging systems.

The utility of a smartphone is limited by its battery capacity and the ability of its hardware and software to efficiently use the device's battery. To properly characterize the energy consumption of an app and identify energy defects, it is critical that apps are properly tested, i.e., analyzed dynamically to assess the app's energy properties. However, currently there is a lack of testing tools for evaluating the energy properties of apps. We present COBWEB, a search-based energy testing technique for Android. By leveraging a set of novel models, representing both the functional behavior of an app as well as the contextual conditions affecting the app's energy behavior, COBWEB generates a test suite that can effectively find energy defects. Our experimental results using real-world apps demonstrate not only its ability to effectively and efficiently test energy behavior of apps, but also its superiority over prior techniques by finding a wider and more diverse set of energy defects.

GUI-based testing has been primarily used to examine the functionality and usability of mobile apps. Despite the numerous GUI-based test input generation techniques proposed in the literature, these techniques are still limited by (1) lack of context-aware text inputs; (2) failing to generate expressive tests; and (3) absence of test oracles. To address these limitations, we propose CraftDroid, a framework that leverages information retrieval, along with static and dynamic analysis techniques, to extract the human knowledge from an existing test suite for one app and transfer the test cases and oracles to be used for testing other apps with the similar functionalities. Evaluation of CraftDroid on real-world commercial Android apps corroborates its effectiveness by achieving 73% precision and 90% recall on average for transferring both the GUI events and oracles. In addition, 75% of the attempted transfers successfully generated valid and feature-based tests for popular features among apps in the same category.

Multi-criteria test-suite minimization aims to remove redundant test cases from a test suite based on some criteria such as code coverage, while trying to optimally maintain the capability of the reduced suite based on other criteria such as fault-detection effectiveness. Existing techniques addressing this problem with integer linear programming claim to produce optimal solutions. However, the multi-criteria test-suite minimization problem is inherently nonlinear, due to the fact that test cases are often dependent on each other in terms of test-case criteria. In this paper, we propose a framework that formulates the multi-criteria test-suite minimization problem as an integer nonlinear programming problem. To solve this problem optimally, we programmatically transform this nonlinear problem into a linear one and then solve the problem using modern linear solvers. We have implemented our framework as a tool, called Nemo, that supports a number of modern linear and nonlinear solvers. We have evaluated Nemo with a publicly available dataset and minimization problems involving multiple criteria including statement coverage, fault-revealing capability, and test execution time. The experimental results show that Nemo can be used to efficiently find an optimal solution for multi-criteria test-suite minimization problems with modern solvers, and the optimal solutions outperform the suboptimal ones by up to 164.29% in terms of the criteria considered in the problem.

The prevalent use of mobile applications in enterprise computing requires more stringent yet flexible enforcement of security policies on the mobile devices. Existing enforcement mechanisms such as mobile device management system focus on the management of device features and cannot cover the diverse security policies of enterprise applications precisely. We address the challenge by proposing a novel security policy enforcement system based on Plugin framework. The system provides fine-grained security policy enforcement at each library call site in an application. With root privilege (targeting company-owned devices), fine-grained enforcement can be applied to any application. Without root privilege (targeting BYOD devices), fine-grained enforcement can be applied to the applications installed via the enforcement system.

To automatically test web applications, crawling-based techniques are usually adopted to mine the behavior models, explore the state spaces or detect the violated invariants of the applications. However, their broad use is limited by the required manual configurations for input value selection, GUI state comparison and clickable detection. In existing crawlers, the configurations are usually string-matching based rules looking for tags or attributes of DOM elements, and often application-specific. Moreover, in input topic identification, it can be difficult to determine which rule suggests a better match when several rules match an input field to more than one topic. This paper presents a natural-language approach based on semantic similarity to address the above issues. The proposed approach represents DOM elements as vectors in a vector space formed by the words used in the elements. The topics of encountered input fields during crawling can then be inferred by their similarities with ones in a labeled corpus. Semantic similarity can also be applied to suggest if a GUI state is newly discovered and a DOM element is clickable under an unsupervised learning paradigm. We evaluated the proposed approach in input topic identification with 100 real-world forms and GUI state comparison with real data from industry. Our evaluation shows that the proposed approach has comparable or better performance to the conventional techniques. Experiments in input topic identification also show that the accuracy of the rule-based approach can be improved by up to 22% when integrated with our approach.

This paper proposes a novel solution to cross-layer optimization for streaming of image/video over adaptive MIMO-OFDM wireless networks. Joint source-channel coding (JSCC) that includes progressive coding and adaptive modulation/code set (MCS) is applied to a singular value decomposition (SVD)-based MIMO-OFDM transmission system. Armed with a packet error rate (PER) model that considers modulation, code rate and SNR, we present a novel resource allocation algorithm that determines the optimal set of parameters. Wireless transmission simulation indicates that the proposed design can deliver images at same level of PSNR with 3–5dB smaller required channel SNR than the conventional approach.