1,382 research outputs found
Overcoming Language Dichotomies: Toward Effective Program Comprehension for Mobile App Development
Mobile devices and platforms have become an established target for modern
software developers due to performant hardware and a large and growing user
base numbering in the billions. Despite their popularity, the software
development process for mobile apps comes with a set of unique, domain-specific
challenges rooted in program comprehension. Many of these challenges stem from
developer difficulties in reasoning about different representations of a
program, a phenomenon we define as a "language dichotomy". In this paper, we
reflect upon the various language dichotomies that contribute to open problems
in program comprehension and development for mobile apps. Furthermore, to help
guide the research community towards effective solutions for these problems, we
provide a roadmap of directions for future work.Comment: Invited Keynote Paper for the 26th IEEE/ACM International Conference
on Program Comprehension (ICPC'18
Keeping Context In Mind: Automating Mobile App Access Control with User Interface Inspection
Recent studies observe that app foreground is the most striking component
that influences the access control decisions in mobile platform, as users tend
to deny permission requests lacking visible evidence. However, none of the
existing permission models provides a systematic approach that can
automatically answer the question: Is the resource access indicated by app
foreground? In this work, we present the design, implementation, and evaluation
of COSMOS, a context-aware mediation system that bridges the semantic gap
between foreground interaction and background access, in order to protect
system integrity and user privacy. Specifically, COSMOS learns from a large set
of apps with similar functionalities and user interfaces to construct generic
models that detect the outliers at runtime. It can be further customized to
satisfy specific user privacy preference by continuously evolving with user
decisions. Experiments show that COSMOS achieves both high precision and high
recall in detecting malicious requests. We also demonstrate the effectiveness
of COSMOS in capturing specific user preferences using the decisions collected
from 24 users and illustrate that COSMOS can be easily deployed on smartphones
as a real-time guard with a very low performance overhead.Comment: Accepted for publication in IEEE INFOCOM'201
Target Directed Event Sequence Generation for Android Applications
Testing is a commonly used approach to ensure the quality of software, of
which model-based testing is a hot topic to test GUI programs such as Android
applications (apps). Existing approaches mainly either dynamically construct a
model that only contains the GUI information, or build a model in the view of
code that may fail to describe the changes of GUI widgets during runtime.
Besides, most of these models do not support back stack that is a particular
mechanism of Android. Therefore, this paper proposes a model LATTE that is
constructed dynamically with consideration of the view information in the
widgets as well as the back stack, to describe the transition between GUI
widgets. We also propose a label set to link the elements of the LATTE model to
program snippets. The user can define a subset of the label set as a target for
the testing requirements that need to cover some specific parts of the code. To
avoid the state explosion problem during model construction, we introduce a
definition "state similarity" to balance the model accuracy and analysis cost.
Based on this model, a target directed test generation method is presented to
generate event sequences to effectively cover the target. The experiments on
several real-world apps indicate that the generated test cases based on LATTE
can reach a high coverage, and with the model we can generate the event
sequences to cover a given target with short event sequences
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
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