Writing and maintaining UI tests for mobile apps is a time-consuming and tedious task. While decades of research have produced auto- mated approaches for UI test generation, these approaches typically focus on testing for crashes or maximizing code coverage. By contrast, recent research has shown that developers prefer usage-based tests, which center around specific uses of app features, to help support activities such as regression testing. Very few existing techniques support the generation of such tests, as doing so requires automating the difficult task of understanding the semantics of UI screens and user inputs. In this paper, we introduce Avgust, which automates key steps of generating usage-based tests. Avgust uses neural models for image understanding to process video recordings of app uses to synthesize an app-agnostic state-machine encoding of those uses. Then, Avgust uses this encoding to synthesize test cases for a new target app. We evaluate Avgust on 374 videos of common uses of 18 popular apps and show that 69% of the tests Avgust generates successfully execute the desired usage, and that Avgust’s classifiers outperform the state of the art.

Writing UI tests manually requires significant effort. Several approaches have tried to address this problem in mobile apps: by exploiting the similarities of different apps within the same domain on a single platform, they have shown that it is possible to transfer tests that exercise similar functionality between the apps. A related recent technique enables transfer of UI tests uni-directionally, from an open-source iOS app to the same app implemented for Android. This paper presents MAPIT, a technique that expands existing work in three important ways: (1) it enables bi-directional UI test transfer between pairs of "sibling" Android and iOS apps; (2) it does not assume that the apps’ source code is available; (3) it is capable of transferring tests containing oracles in addition to UI events. MAPIT runs existing tests on a "source" app and builds a partial model of the app corresponding to each test. The model comprises the app’s screenshots, obtainable properties of each screenshot’s constituent elements, and labeled transitions between the screenshots. MAPIT uses this model to determine the corresponding information on the "target" app and generates an equivalent test, via a novel approach that leverages computer vision and NLP. Our evaluation on a diverse set of widely used, closed-source sibling Android and iOS apps shows that MAPIT is feasible, accurate, and useful in transferring UI tests across platforms.

Noise in software patches impacts their understanding, analysis, and use for tasks such as change prediction. Although several approaches have been developed to identify noise in patches, this issue has persisted. An analysis of a dataset of security patches for the Tomcat web server, which we further expanded with security patches from five additional systems, uncovered several kinds of previously unreported noise which we call nonessential casualty changes. These are changes that themselves do not alter the logic of the program but are necessitated by other changes made in the patch. In this paper, we provide a comprehensive taxonomy of casualty changes. We then develop CasCADe, an automated technique for automatically identifying casualty changes. We evaluate CasCADe with several publicly available datasets of patches and tools that focus on them. Our results show that CasCADe is highly accurate, that the kinds of noise it identifies occur relatively commonly in patches, and that removing this noise improves upon the evaluation results of a previously published change-based approach.

UI testing is tedious and time-consuming due to the manual effort required. Recent research has explored opportunities for reusing existing UI tests from an app to automatically generate new tests for other apps. However, the evaluation of such techniques currently remains manual, unscalable, and unreproducible, which can waste effort and impede progress in this emerging area. We introduce FrUITeR, a framework that automatically evaluates UI test reuse in a reproducible way. We apply FrUITeR to existing test-reuse techniques on a uniform benchmark we established, resulting in 11,917 test reuse cases from 20 apps. We report several key findings aimed at improving UI test reuse that are missed by existing work.

Reducing network latency in mobile applications is an effective way of improving the mobile user experience and has tangible economic benefits. This paper presents PALOMA, a novel client-centric technique for reducing the network latency by prefetching HTTP requests in Android apps. Our work leverages string analysis and callback control-flow analysis to automatically instrument apps using PALOMA's rigorous formulation of scenarios that address "what" and "when" to prefetch. PALOMA has been shown to incur significant runtime savings (several hundred milliseconds per prefetchable HTTP request), both when applied on a reusable evaluation benchmark we have developed and on real applications.

Network latency in mobile software has a large impact on user experience, with potentially severe economic consequences. Prefetching and caching have been shown effective in reducing the latencies in browser-based systems. However, those techniques cannot be directly applied to the emerging domain of mobile apps because of the differences in network interactions. Moreover, there is a lack of research on prefetching and caching techniques that may be suitable for the mobile app domain, and it is not clear whether such techniques can be effective or whether they are even feasible. This paper takes the first step toward answering these questions by conducting a comprehensive study to understand the characteristics of HTTP requests in over 1,000 popular Android apps. Our work focuses on the prefetchability of requests using static program analysis techniques and cacheability of resulting responses. We find that there is a substantial opportunity to leverage prefetching and caching in mobile apps, but that suitable techniques must take into account the nature of apps’ network interactions and idiosyncrasies such as untrustworthy HTTP header information. Our observations provide guidelines for developers to utilize prefetching and caching schemes in app development, and motivate future research in this area.

Android's communication model has a major security weakness: malicious apps can manipulate other apps into performing unintended operations and can steal end-user data, while appearing ordinary and harmless. This paper presents SEALANT, a technique that combines static analysis of app code, which infers vulnerable communication channels, with runtime monitoring of inter-app communication through those channels, which helps to prevent attacks. SEALANT's extensive evaluation demonstrates that (1) it detects and blocks inter-app attacks with high accuracy in a corpus of over 1,100 real-world apps, (2) it suffers from fewer false alarms than existing techniques in several representative scenarios, (3) its performance overhead is negligible, and (4) end-users do not find it challenging to adopt.

Design problems affect every software system. Diverse software systems have been discontinued or reengineered due to design problems. As design documentation is often informal or nonexistent, design problems need to be located in the source code. The main difficulty to identify a design problem in the implementation stems from the fact that such problem is often scattered through several program elements. Previous work assumed that code anomalies -- popularly known as code smells -- may provide sufficient hints about the location of a design problem. However, each code anomaly alone may represent only a partial embodiment of a design problem. In this paper, we hypothesize that code anomalies tend to "flock together" to realize a design problem. We analyze to what extent groups of inter-related code anomalies, named agglomerations, suffice to locate design problems. We analyze more than 2200 agglomerations found in seven software systems of different sizes and from different domains. Our analysis indicates that certain forms of agglomerations are consistent indicators of both congenital and evolutionary design problems, with accuracy often higher than 80%.