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Context: 2nd generation (Layout-based) and 3rd generation (Visual) GUI testing are two approaches for testing mobile GUIs, both with individual benefits and drawbacks. Previous research has presented approaches to translate 2nd generation scripts to 3rd generation scripts but not the vice versa. Goal: The objective of this work is to provide Proof of Concept of the effectiveness of automatic translation between existing 3rd generation test scripts to 2nd generation test scripts. Method: A tool architecture is presented and implemented in a tool capable of translating most 3rd generation interactions with the GUI of an Android app into 2nd generation instructions and oracles for the Espresso testing tool.Results: We validate our approach on two test suites of our own creation, consisting of 30 test cases each. The measured success rate of the translation is 96.7% (58 working test cases out of 60 applications of the translator). Conclusion: The study provides support for the feasibility of a translation-based approach from 3rd to 2nd generation test cases. However, additional work is needed to make the approach applicable in real-world scenarios or larger open-source test suites.

Android applications do not seem to be tested as thoroughly as desktop ones. In particular, graphical user interface (GUI) testing appears generally limited. Like web-based applications, mobile apps suffer from GUI test fragility, i.e., GUI test classes failing or needing updates due to even minor modifications in the GUI or in the application under test. The objective of our study is to estimate the adoption of GUI testing frameworks among Android open-source applications, the quantity of modifications needed to keep test classes up to date, and their amount due to GUI test fragility. We introduce a set of 21 metrics to measure the adoption of testing tools and the evolution of test classes and test methods, and to estimate the fragility of test suites. We computed our metrics for six GUI testing frameworks, none of which achieved a significant adoption among Android projects hosted on GitHub. When present, GUI test methods associated with the considered tools are modified often, and a relevant portion (70% on average) of those modifications is induced by GUI-related fragilities. On average, for the projects considered, more than 7% of the total modified lines of code between consecutive releases belong to test classes developed with the analyzed testing frameworks. The measured percentage was higher on average than the one required by other generic test code, based on the JUnit testing framework. Fragility of GUI tests constitutes a relevant concern, probably an obstacle for developers to adopt test automation. This first evaluation of the fragility of Android scripted GUI testing can constitute a benchmark for developers and testers leveraging the analyzed test tools and the basis for the definition of a taxonomy of fragility causes and guidelines to mitigate the issue.

Automated GUI testing is a fundamental part of the V&V process of every software, but - especially for fast evolving applications - is linked to relevant maintenance costs. Android (and mobile, in general) apps are also particularly prone to fragility, i.e. even minor modifications in the graphical user interface may induce relevant adaptation effort on test code. Based on a preliminary exploration about the evolution and fragility of test suites that we made on a set of nearly 20 thousand Android open-source projects hosted on GitHub, we performed a deeper inspection about the causes underlying the modifications in test classes and methods. We manually examined a set of 423 projects featuring the Espresso automated GUI testing tool, and 945 diff files containing modifications in test methods. We derived a set of 27 different causes of modifications, that we grouped into nine different macro-categories, two related to modifications performed just in code and application logic, and seven pertaining modifications in the GUI functioning and appearance, and hence related to our definition of GUI testing fragility. Based on the set of projects we inspected, we conclude that modifications related to changes in the GUI occured in 55% of modified test methods, a higher frequency than the ones related to changes in test logic only (35%) and to adaptations to modified application logic (27%). The described taxonomy can serve as a basis for further research, aimed at giving guidelines to developers to avoid fragility, and reduce the maintenance costs for automated test suites for Android applications.

Predictive lossy compression has been shown to represent a very flexible framework for lossless and lossy onboard compression of multispectral and hyperspectral images with quality and rate control. In this paper, we improve predictive lossy compression in several ways, using a standard issued by the Consultative Committee on Space Data Systems, namely CCSDS-123, as an example of application. First, exploiting the flexibility in the error control process, we propose a constant-signal-to-noise-ratio algorithm that bounds the maximum relative error between each pixel of the reconstructed image and the corresponding pixel of the original image. This is very useful to avoid low-energy areas of the image being affected by large errors. Second, we propose a new rate control algorithm that has very low complexity and provides performance equal to or better than existing work. Third, we investigate several entropy coding schemes that can speed up the hardware implementation of the algorithm and, at the same time, improve coding efficiency. These advances make predictive lossy compression an extremely appealing framework for onboard systems due to its simplicity, flexibility, and coding efficiency.