1,850 research outputs found
Towards a Generic Framework for Automating Extensive Analysis of Android Applications
Despite much effort in the community, the momentum of Android research has not yet produced complete tools to perform thorough analysis on Android apps, leaving users vulnerable to malicious apps. Because it is hard for a single tool to efficiently address all of the various challenges of Android programming which make analysis difficult, we propose to instrument the app code for reducing the analysis complexity, e.g., transforming a hard problem to a easy-resolvable one. To this end, we introduce in this paper Apkpler, a plugin-based framework for supporting such instrumentation. We evaluate Apkpler with two plugins, demonstrating the feasibility of our approach and showing that Apkpler can indeed be leveraged to reduce the analysis complexity of Android apps
Genetic Programming for Smart Phone Personalisation
Personalisation in smart phones requires adaptability to dynamic context
based on user mobility, application usage and sensor inputs. Current
personalisation approaches, which rely on static logic that is developed a
priori, do not provide sufficient adaptability to dynamic and unexpected
context. This paper proposes genetic programming (GP), which can evolve program
logic in realtime, as an online learning method to deal with the highly dynamic
context in smart phone personalisation. We introduce the concept of
collaborative smart phone personalisation through the GP Island Model, in order
to exploit shared context among co-located phone users and reduce convergence
time. We implement these concepts on real smartphones to demonstrate the
capability of personalisation through GP and to explore the benefits of the
Island Model. Our empirical evaluations on two example applications confirm
that the Island Model can reduce convergence time by up to two-thirds over
standalone GP personalisation.Comment: 43 pages, 11 figure
Boosting Static Analysis of Android Apps through Code Instrumentation
Static analysis has been applied to dissect Android apps for many years. The main advantage of using static analysis is its efficiency and entire code coverage characteristics. However, the community has not yet produced complete tools to perform in-depth static analysis, putting users at risk to malicious apps. Because of the diverse challenges caused by Android apps, it is hard for a single tool to efficiently address all of them. Thus, in this work, we propose to boost static analysis of Android apps through code instrumentation, in which the knotty code can be reduced or simplified into an equivalent but analyzable code. Consequently, existing static analyzers, without any modification, can be leveraged to perform extensive analysis, although originally they cannot.
Previously, we have successfully applied instrumentation for two challenges of static analysis of Android apps: Inter-Component Communication (ICC) and Reflection. However, these two case studies are implemented separately and the implementation is not reusable, letting some functionality, that could be reused from one to another, be reinvented and thus lots of resources are wasted. To this end, in this work, we aim at providing a generic and non-invasive approach for existing static analyzers, enabling them to perform more broad analysis
Automated Test Input Generation for Android: Are We There Yet?
Mobile applications, often simply called "apps", are increasingly widespread,
and we use them daily to perform a number of activities. Like all software,
apps must be adequately tested to gain confidence that they behave correctly.
Therefore, in recent years, researchers and practitioners alike have begun to
investigate ways to automate apps testing. In particular, because of Android's
open source nature and its large share of the market, a great deal of research
has been performed on input generation techniques for apps that run on the
Android operating systems. At this point in time, there are in fact a number of
such techniques in the literature, which differ in the way they generate
inputs, the strategy they use to explore the behavior of the app under test,
and the specific heuristics they use. To better understand the strengths and
weaknesses of these existing approaches, and get general insight on ways they
could be made more effective, in this paper we perform a thorough comparison of
the main existing test input generation tools for Android. In our comparison,
we evaluate the effectiveness of these tools, and their corresponding
techniques, according to four metrics: code coverage, ability to detect faults,
ability to work on multiple platforms, and ease of use. Our results provide a
clear picture of the state of the art in input generation for Android apps and
identify future research directions that, if suitably investigated, could lead
to more effective and efficient testing tools for Android
Translating Video Recordings of Mobile App Usages into Replayable Scenarios
Screen recordings of mobile applications are easy to obtain and capture a
wealth of information pertinent to software developers (e.g., bugs or feature
requests), making them a popular mechanism for crowdsourced app feedback. Thus,
these videos are becoming a common artifact that developers must manage. In
light of unique mobile development constraints, including swift release cycles
and rapidly evolving platforms, automated techniques for analyzing all types of
rich software artifacts provide benefit to mobile developers. Unfortunately,
automatically analyzing screen recordings presents serious challenges, due to
their graphical nature, compared to other types of (textual) artifacts. To
address these challenges, this paper introduces V2S, a lightweight, automated
approach for translating video recordings of Android app usages into replayable
scenarios. V2S is based primarily on computer vision techniques and adapts
recent solutions for object detection and image classification to detect and
classify user actions captured in a video, and convert these into a replayable
test scenario. We performed an extensive evaluation of V2S involving 175 videos
depicting 3,534 GUI-based actions collected from users exercising features and
reproducing bugs from over 80 popular Android apps. Our results illustrate that
V2S can accurately replay scenarios from screen recordings, and is capable of
reproducing 89% of our collected videos with minimal overhead. A case
study with three industrial partners illustrates the potential usefulness of
V2S from the viewpoint of developers.Comment: In proceedings of the 42nd International Conference on Software
Engineering (ICSE'20), 13 page
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
Generic Home Automation System Using IoT Gateway Based on Wi-Fi and ant+ Sensor Network
This research article explores the use of internet of things (IoT) technology in home automation, including cloud computing and sensor networks to improve quality of life, and the increasing affordability through mobile connectivity. In this proposed smart home system, our main objective is to build a home automation system for the common consumer, which can help him to use home appliances with confidence and control at a low cost. The paper describes the building of an IoT gateway using the ANT multi-hop wireless network protocol and the Wi-Fi protocol, specifically utilizing the nRF24L01 and Esp8266 chips. Various sensor nodes, such as a water tank level sensor, human presence sensor, smart LED door sensor, and smart switch, will be integrated into the system. The main goal of the research is to develop an affordable solution for smart home technology for everyday consumers
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