Model-based testing of apps in real network scenarios.

Política de acceso abierto tomada de: https://v2.sherpa.ac.uk/id/publication/8039?template=romeoTraditional testing methods for mobile apps focus on detecting execution errors. However, the evolution of mobile networks towards 5G will require additional support for app developers to also ensure good performance and user-experience. Manual testing in a number of scenarios is not enough to satisfy the expectations of the apps’ end users. This paper presents the testing framework developed in the TRIANGLE project, which integrates a complete mobile network testbed to test, benchmark and certify mobile apps. In this paper, we focus on a recent extension of the TRIANGLE framework that uses model-based testing based on model checking to support the automatic generation of user interactions. We introduce the complete testing framework and the basis of the model-based extension. Finally, we use the testing framework to evaluate the performance of the ExoPlayer app in different network scenarios. ExoPlayer is a video streaming app for Android that implements different adaptive streaming protocols.This work is funded by the European Union Horizon 2020 research and innovation programme, grant agreement No 688712 (TRIANGLE project) and 815178 (5GENESIS project)

Performance mutation testing

Performance bugs are known to be a major threat to the success of software products. Performance tests aim to detect performance bugs by executing the program through test cases and checking whether it exhibits a noticeable performance degradation. The principles of mutation testing, a well-established testing technique for the assessment of test suites through the injection of artificial faults, could be exploited to evaluate and improve the detection power of performance tests. However, the application of mutation testing to assess performance tests, henceforth called performance mutation testing (PMT), is a novel research topic with numerous open challenges. In previous papers, we identified some key challenges related to PMT. In this work, we go a step further and explore the feasibility of applying PMT at the source-code level in general purpose languages. To do so, we revisit concepts associated with classical mutation testing, and design seven novel mutation operators to model known bug-inducing patterns. As a proof of concept, we applied traditional mutation operators as well as performance mutation operators to open-source C++ programs. The results reveal the potential of the new performance-mutants to help assess and enhance performance tests when compared to traditional mutants. A review of live mutants in these programs suggests that they can induce the design of special test inputs. In addition to these promising results, our work brings a whole new set of challenges related to PMT, which will hopefully serve as a starting point for new contributions in the areaMinisterio de Economía y Competitividad TIN2015-65845-C3-3-RMinisterio de Economía y Competitividad RTI2018- 093608-B-C33Ministerio de Economía y Competitividad BELI (TIN2015-70560-R)Ministerio de Economía y Competitividad (HORATIO) RTI2018-101204-B-C2

Performance mutation testing

Performance bugs are known to be a major threat to the success of software products. Performance tests aim to detect performance bugs by executing the program through test cases and checking whether it exhibits a noticeable performance degradation. The principles of mutation testing, a well-established testing technique for the assessment of test suites through the injection of artificial faults, could be exploited to evaluate and improve the detection power of performance tests. However, the application of mutation testing to assess performance tests, henceforth called performance mutation testing (PMT), is a novel research topic with numerous open challenges. In previous papers, we identified some key challenges related to PMT. In this work, we go a step further and explore the feasibility of applying PMT at the source-code level in general-purpose languages. To do so, we revisit concepts associated with classical mutation testing, and design seven novel mutation operators to model known bug-inducing patterns. As a proof of concept, we applied traditional mutation operators as well as performance mutation operators to open-source C++ programs. The results reveal the potential of the new performance-mutants to help assess and enhance performance tests when compared to traditional mutants. A review of live mutants in these programs suggests that they can induce the design of special test inputs. In addition to these promising results, our work brings a whole new set of challenges related to PMT, which will hopefully serve as a starting point for new contributions in the area

Study and Refactoring of Android Asynchronous Programming

To avoid unresponsiveness, a core part of mobile development is asynchronous programming. Android provides several async constructs that developers can use. However, developers can still use the inappropriate async constructs, which result in memory leaks, lost results, and wasted energy. Fortunately, refactoring tools can eliminate these problems by transforming async code to use the appropriate constructs. In this paper we conducted a formative study on a corpus of 611 widely-used Android apps to map the asynchronous landscape of Android apps, understand how developers retrofit asynchrony, and learn about barriers encountered by developers. Based on this study, we designed, implemented, and evaluated ASYNCDROID, a refactoring tool which enables Android developers to transform existing improperly-used async constructs into correct constructs. Our empirical evaluation shows that ASYNCDROID is applicable, accurate, and saves developers effort. We submitted 30 refactoring patches, and developers consider that the refactorings are useful.Ope

Study and Refactoring of Android Asynchronous Programming

To avoid unresponsiveness, a core part of mobile development is asynchronous programming. Android provides several async constructs that developers can use. However, developers can still use the inappropriate async constructs, which result in memory leaks, lost results, and wasted energy. Fortunately, refactoring tools can eliminate these problems by transforming async code to use the appropriate constructs. In this paper we conducted a formative study on a corpus of 611 widely-used Android apps to map the asynchronous landscape of Android apps, understand how developers retrofit asynchrony, and learn about barriers encountered by developers. Based on this study, we designed, implemented, and evaluated ASYNCDROID, a refactoring tool which enables Android developers to transform existing improperly-used async constructs into correct constructs. Our empirical evaluation shows that ASYNCDROID is applicable, accurate, and saves developers effort. We submitted 30 refactoring patches, and developers consider that the refactorings are useful.Keywords: Android, responsiveness, asynchronous programming, asynchrony, refactoringKeywords: Android, responsiveness, asynchronous programming, asynchrony, refactorin

Enhancing Automated GUI Exploration Techniques for Android Mobile Applications

Mobile software applications ("apps") are used by billions of smartphone owners worldwide. The demand for quality to these apps has grown together with their spread. Therefore, effective techniques and tools are being requested to support developers in mobile app quality engineering activities. Automation tools can facilitate these activities since they can save humans from routine, time consuming and error prone manual tasks. Automated GUI exploration techniques are widely adopted by researchers and practitioners in the context of mobile apps for supporting critical engineering tasks such as reverse engineering, testing, and network traffic signature generation. These techniques iteratively exercise a running app by exploiting the information that the app exposes at runtime through its GUI to derive the set of input events to be fired. Although several automated GUI exploration techniques have been proposed in the literature, they suffer from some limitations that may hinder them from a thorough app exploration. This dissertation proposes two novel solutions that contribute to the literature in Software Engineering towards improving existing automated GUI exploration techniques for mobile software applications. The former is a fully automated GUI exploration technique that aims to detect issues tied to the app instances lifecycle, a mobile-specific feature that allows users to smoothly navigate through an app and switch between apps. In particular, this technique addresses the issues of crashes and GUI failures, that consists in the manifestation of unexpected GUI states. This work includes two exploratory studies that prove that GUI failures are a widespread problem in the context of mobile apps. The latter solution is a hybrid exploration technique that combines automated GUI exploration with capture and replay through machine learning. It exploits app-specific knowledge that only human users can provide in order to explore relevant parts of the application that can be reached only by firing complex sequences of input events on specific GUIs and by choosing specific input values. Both the techniques have been implemented in tools that target the Android Operating System, that is today the world’s most popular mobile operating system. The effectiveness of the proposed techniques is demonstrated through experimental evaluations performed on real mobile apps

Supporting Evolution and Maintenance of android Apps

Mobile developers and testers face a number of emerging challenges. These include rapid platform evolution and API instability; issues in bug reporting and reproduction involving complex multitouch gestures; platform fragmentation; the impact of reviews and ratings on the success of their apps; management of crowd-sourced requirements; continuous pressure from the market for frequent releases; lack of effective and usable testing tools; and limited computational resources for handheld devices. Traditional and contemporary methods in software evolution and maintenance were not designed for these types of challenges; therefore, a set of studies and a new toolbox of techniques for mobile development are required to analyze current challenges and propose new solutions. This dissertation presents a set of empirical studies, as well as solutions for some of the key challenges when evolving and maintaining android apps. In particular, we analyzed key challenges experienced by practitioners and open issues in the mobile development community such as (i) android API instability, (ii) performance optimizations, (iii) automatic GUI testing, and (iv) energy consumption. When carrying out the studies, we relied on qualitative and quantitative analyses to understand the phenomena on a large scale by considering evidence extracted from software repositories and the opinions of open-source mobile developers. From the empirical studies, we identified that dynamic analysis is a relevant method for several evolution and maintenance tasks, in particular, because of the need of practitioners to execute/validate the apps on a diverse set of platforms (i.e., device and OS) and under pressure for continuous delivery. Therefore, we designed and implemented an extensible infrastructure that enables large-scale automatic execution of android apps to support different evolution and maintenance tasks (e.g., testing and energy optimization). In addition to the infrastructure we present a taxonomy of issues, single solutions to the issues, and guidelines to enable large execution of android apps. Finally, we devised novel approaches aimed at supporting testing and energy optimization of mobile apps (two key challenges in evolution and maintenance of android apps). First, we propose a novel hybrid approach for automatic GUI-based testing of apps that is able to generate (un)natural test sequences by mining real applications usages and learning statistical models that represent the GUI interactions. In addition, we propose a multi-objective approach for optimizing the energy consumption of GUIs in android apps that is able to generate visually appealing color compositions, while reducing the energy consumption and keeping a design concept close to the original

Program analysis for anomaly detection

When interacting with mobile applications, users may not always get what they expect. For instance, when users download Android applications from a market, they do not know much about their actual behavior. A brief description, a set of screenshots and a list of permissions, which give a high level intuition of what applications might be doing, form user expectations. However applications do not always meet them. For example, a gaming application intentionally could send SMS messages to a premium number in a background without a user’s knowledge. A less harmful scenario would be a wrong message confirming a successful action that was never executed. Whatever the behavior of a mobile application might (app) be, in order to test and fully understand it, there needs to be a technique that can analyse the User Interface (UI) of the app and the code associated with it as the whole. This thesis presents a static analysis framework called SAFAND that given an ANDROID app performs the following analysis: - gathers information on how the application uses sensitive data; - identifies and analyses UI elements of the application; - binds UI elements with their corresponding behavior. The thesis illustrates how results obtained from the framework can be used to identify problems ranging from small usability issues to malicious behavior of real-world applications.Bei der Interaktion mit mobilen Anwendungen erhalten Benutzer möglicherweise nicht immer das, was sie erwarten. Wenn Benutzer beispielsweise Android- Anwendungen von einem Marktplatz herunterladen, wissen sie nicht viel über das tatsächliche Verhalten dieser Anwendungen. Eine kurze Beschreibung, eine Reihe von Screenshots und eine Liste von Berechtigungen, die eine umfassende Vorstellung davon geben sollen, welche Anwendungen möglicherweise ausgeführt werden können, bilden die Erwartungen der Benutzer. Die Anwendungen entsprechen diesen Erwartungen aber nicht immer. Zum Beispiel könnte ein Spiel ohne Wissen des Benutzers im Hintergrund absichtlich SMS-Nachrichten an eine Premium-Nummer senden. Ein weniger schädliches Szenario wäre eine falsche Meldung, welche eine erfolgreiche Aktion bestätigt, die jedoch niemals durchgeführt wurde. Unabhängig vom Verhalten einer mobilen Anwendung (App) muss eine Technik vorhanden sein, die die Benutzeroberfläche (User Interface, UI) der App und des damit verbundenen Codes testet und als Ganzes versteht. In dieser Arbeit wird ein statisches Analyseframework namens SAFAND2 vorgestellt, bei dem eine ANDROID-App die folgende Analyse durchführt: * sammelt Informationen darüber, wie die Anwendung sensible Daten verwendet; * identifiziert und analysiert UI-Elemente der Anwendung; * verbindet UI-Elemente mit ihrem entsprechenden Verhalten. Die Arbeit zeigt, wie Probleme, von kleinen Usability-Problemen bis hin zu böswilligem Verhalten realer Anwendungen, mit den Ergebnissen des Frameworks identifiziert werden können. 2SAFAND = Static Analysis For Anomaly Detectio