ChimpCheck: Property-Based Randomized Test Generation for Interactive Apps

We consider the problem of generating relevant execution traces to test rich interactive applications. Rich interactive applications, such as apps on mobile platforms, are complex stateful and often distributed systems where sufficiently exercising the app with user-interaction (UI) event sequences to expose defects is both hard and time-consuming. In particular, there is a fundamental tension between brute-force random UI exercising tools, which are fully-automated but offer low relevance, and UI test scripts, which are manual but offer high relevance. In this paper, we consider a middle way---enabling a seamless fusion of scripted and randomized UI testing. This fusion is prototyped in a testing tool called ChimpCheck for programming, generating, and executing property-based randomized test cases for Android apps. Our approach realizes this fusion by offering a high-level, embedded domain-specific language for defining custom generators of simulated user-interaction event sequences. What follows is a combinator library built on industrial strength frameworks for property-based testing (ScalaCheck) and Android testing (Android JUnit and Espresso) to implement property-based randomized testing for Android development. Driven by real, reported issues in open source Android apps, we show, through case studies, how ChimpCheck enables expressing effective testing patterns in a compact manner.Comment: 20 pages, 21 figures, Symposium on New ideas, New Paradigms, and Reflections on Programming and Software (Onward!2017

ERP implementation for an administrative agency as a corporative frontend and an e-commerce smartphone app

This document contains all the descriptions, arguments and demonstrations of the researches, analysis, reasoning, designs and tasks performed to achieve the requirement to technologically evolve an managing agency in a way that, through a solution that requires a reduced investment, makes possible to arrange a business management tool with e-commerce and also a mobile application that allows access and consultation of mentioned tool. The first part of the document describes the scenario in order to contextualize the project and introduces ERP (Enterprise Resources Planning). In the second part, a deep research of ERP market products is carried out, identifying the strengths and weaknesses of each one of the products in order to finish with the choice of the most suitable product for the scenario proposed in the project. A third part of the document describes the installation process of the selected product carried out based on the use of Dockers, as well as the configurations and customizations that they make on the selected ERP. A description of the installation and configuration of additional modules is also made, necessary to achieve the agreed scope of the project. In a fourth part of the thesis, the process of creating an iOS and Android App that connects to the selected ERP database is described. The process begins with the design of the App. Once designed, it is explained the process of study and documentation of technologies to choose the technology stack that allows making an application robust and contemporary without use of licensing. After choosing the technologies to use there are explained the dependencies and needs to install runtime enviornments prior to the start of coding. Later, it describes how the code of the App has been raised and developed. The compilation and verification mechanisms are indicated in continuation. And finally, it is showed the result of the development of the App once distributed. Finally, a chapter for the conclusions analyzes the difficulties encountered during the project and the achievements, analyzing what has been learned during the development of this project

Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection

Machine learning based solutions have been successfully employed for automatic detection of malware in Android applications. However, machine learning models are known to lack robustness against inputs crafted by an adversary. So far, the adversarial examples can only deceive Android malware detectors that rely on syntactic features, and the perturbations can only be implemented by simply modifying Android manifest. While recent Android malware detectors rely more on semantic features from Dalvik bytecode rather than manifest, existing attacking/defending methods are no longer effective. In this paper, we introduce a new highly-effective attack that generates adversarial examples of Android malware and evades being detected by the current models. To this end, we propose a method of applying optimal perturbations onto Android APK using a substitute model. Based on the transferability concept, the perturbations that successfully deceive the substitute model are likely to deceive the original models as well. We develop an automated tool to generate the adversarial examples without human intervention to apply the attacks. In contrast to existing works, the adversarial examples crafted by our method can also deceive recent machine learning based detectors that rely on semantic features such as control-flow-graph. The perturbations can also be implemented directly onto APK's Dalvik bytecode rather than Android manifest to evade from recent detectors. We evaluated the proposed manipulation methods for adversarial examples by using the same datasets that Drebin and MaMadroid (5879 malware samples) used. Our results show that, the malware detection rates decreased from 96% to 1% in MaMaDroid, and from 97% to 1% in Drebin, with just a small distortion generated by our adversarial examples manipulation method.Comment: 15 pages, 11 figure