Scripted GUI Testing of Android Apps: A Study on Diffusion, Evolution and Fragility

Background. Evidence suggests that mobile applications are not thoroughly tested as their desktop counterparts. In particular GUI testing is generally limited. Like web-based applications, mobile apps suffer from GUI test fragility, i.e. GUI test classes failing due to minor modifications in the GUI, without the application functionalities being altered. Aims. The objective of our study is to examine the diffusion of GUI testing on Android, and the amount of changes required to keep test classes up to date, and in particular the changes due to GUI test fragility. We define metrics to characterize the modifications and evolution of test classes and test methods, and proxies to estimate fragility-induced changes. Method. To perform our experiments, we selected six widely used open-source tools for scripted GUI testing of mobile applications previously described in the literature. We have mined the repositories on GitHub that used those tools, and computed our set of metrics. Results. We found that none of the considered GUI testing frameworks achieved a major diffusion among the open-source Android projects available on GitHub. For projects with GUI tests, we found that test suites have to be modified often, specifically 5\%-10\% of developers' modified LOCs belong to tests, and that a relevant portion (60\% on average) of such modifications are induced by fragility. Conclusions. Fragility of GUI test classes constitute a relevant concern, possibly being an obstacle for developers to adopt automated scripted GUI tests. This first evaluation and measure of fragility of Android scripted GUI testing can constitute a benchmark for developers, and the basis for the definition of a taxonomy of fragility causes, and actionable guidelines to mitigate the issue.Comment: PROMISE'17 Conference, Best Paper Awar

Evolution and Fragility of Mobile Automated Test Suites

L'abstract è presente nell'allegato / the abstract is in the attachmen

Connected Car: technologies, issues, future trends

The connected car -a vehicle capable of accessing to the Internet, of communicating with smart devices as well as other cars and road infrastructures, and of collecting real-time data from multiple sources- is likely to play a fundamental role in the foreseeable Internet Of Things. In a context ruled by very strong competitive forces, a significant amount of car manufacturers and software and hardware developers have already embraced the challenge of providing innovative solutions for new generation vehicles. Today’s cars are asked to relieve drivers from the most stressful operations needed for driving, providing them with interesting and updated entertainment functions. In the meantime, they have to comply to the increasingly stringent standards about safety and reliability. The aim of this paper is to provide an overview of the possibilities offered by connected functionalities on cars and the associated technological issues and problems, as well as to enumerate the currently available hardware and software solutions and their main features

Quality Assessment Methods for Textual Conversational Interfaces: A Multivocal Literature Review

The evaluation and assessment of conversational interfaces is a complex task since such software products are challenging to validate through traditional testing approaches. We conducted a systematic Multivocal Literature Review (MLR), on five different literature sources, to provide a view on quality attributes, evaluation frameworks, and evaluation datasets proposed to provide aid to the researchers and practitioners of the field. We came up with a final pool of 118 contributions, including grey (35) and white literature (83). We categorized 123 different quality attributes and metrics under ten different categories and four macro-categories: Relational, Conversational, User-Centered and Quantitative attributes. While Relational and Conversational attributes are most commonly explored by the scientific literature, we testified a predominance of User-Centered Attributes in industrial literature. We also identified five different academic frameworks/tools to automatically compute sets of metrics, and 28 datasets (subdivided into seven different categories based on the type of data contained) that can produce conversations for the evaluation of conversational interfaces. Our analysis of literature highlights that a high number of qualitative and quantitative attributes are available in the literature to evaluate the performance of conversational interfaces. Our categorization can serve as a valid entry point for researchers and practitioners to select the proper functional and non-functional aspects to be evaluated for their products

An Agent-based Modelling Framework for Driving Policy Learning in Connected and Autonomous Vehicles

Due to the complexity of the natural world, a programmer cannot foresee all possible situations, a connected and autonomous vehicle (CAV) will face during its operation, and hence, CAVs will need to learn to make decisions autonomously. Due to the sensing of its surroundings and information exchanged with other vehicles and road infrastructure, a CAV will have access to large amounts of useful data. While different control algorithms have been proposed for CAVs, the benefits brought about by connectedness of autonomous vehicles to other vehicles and to the infrastructure, and its implications on policy learning has not been investigated in literature. This paper investigates a data driven driving policy learning framework through an agent-based modelling approaches. The contributions of the paper are two-fold. A dynamic programming framework is proposed for in-vehicle policy learning with and without connectivity to neighboring vehicles. The simulation results indicate that while a CAV can learn to make autonomous decisions, vehicle-to-vehicle (V2V) communication of information improves this capability. Furthermore, to overcome the limitations of sensing in a CAV, the paper proposes a novel concept for infrastructure-led policy learning and communication with autonomous vehicles. In infrastructure-led policy learning, road-side infrastructure senses and captures successful vehicle maneuvers and learns an optimal policy from those temporal sequences, and when a vehicle approaches the road-side unit, the policy is communicated to the CAV. Deep-imitation learning methodology is proposed to develop such an infrastructure-led policy learning framework

Constant-SNR, rate control and entropy coding for predictive lossy hyperspectral image compression

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

Mobile GUI Testing Fragility: A Study on Open-Source Android Applications

Android applications do not seem to be tested as thoroughly as desktop ones. In particular, GUI testing appears generally limited. Like webbased 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 opensource applications, the quantity of modifications needed to keep test classes up to date, and the amount of them due to GUI test fragility. We introduce a set of 21 metrics to measure the adoption of testing tools, 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 analysed 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 constitute 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 analysed test tools, and the basis for the definition of a taxonomy of fragility causes and guidelines to mitigate the issue

Evolution and Fragilities in Scripted GUI Testing of Android applications

In literature there is evidence that Android applications are not rigorously tested as their desktop counterparts. However – especially for what concerns the graphical User Interface of mobile apps – a thorough testing should be advisable for developers. Some peculiarities of Android applications discourage developers from performing automated testing. Among them, we recognize fragility, i.e. test classes failing because of modifications in the GUI only, without the application functionalities being modified. The aim of this study is to provide a preliminary characterization of the fragility issue for Android apps, dentifying some of its causes and estimating its frequency among Android open-source projects. We defined a set of metrics to quantify the amount of fragility of any testing suite, and measured them automatically for a set of repositories hosted on GitHub. We found that, for projects featuring GUI tests, the incidence of fragility is around 10% for test classes, and around 5% for test methods. This means that a significant effort has to be put by developers in fixing their test suites because of the occurrence of fragilities

Methodological Guidelines for Measuring Energy Consumption of Software Applications

Energy consumption information for devices, as available in the literature, is typically obtained with ad hoc approaches, thus making replication and consumption data comparison difficult. We propose a process for measuring the energy consumption of a software application. The process contains four phases, each providing a structured deliverable that reports the information required to replicate the measurement. The process also guides the researcher on a threat to validity analysis to be included in each deliverable. This analysis ensures better reliability, trust, and confidence to reuse the collected consumption data. Such a process produces a structured consumption data for any kind of electronic device (IoT devices, mobile phones, personal computers, servers, etc.), which can be published and shared with other researchers fostering comparison or further investigations. A real case example demonstrates how to apply the process and how to create the required deliverables

A Metric Framework for the Gamification of Web and Mobile GUI Testing

System testing through the Graphical User Interface (GUI) is a valuable form of Verification & Validation for modern applications, especially in graphically-intensive domains like web and mobile applications. However, the practice is often overlooked by developers mostly because of its costly nature and the absence of immediate feedback about the quality of test sequence. This paper describes a proposal for the Gamification of exploratory GUI testing. We define - in a tool and domain- agnostic way - the basic concepts, a set of metrics, a scoring scheme and visual feedbacks to enable a gamified approach to the practice; we finally discuss the potential implications and envision a roadmap for the evaluation of the approach