144,621 research outputs found
Automatically Discovering, Reporting and Reproducing Android Application Crashes
Mobile developers face unique challenges when detecting and reporting crashes
in apps due to their prevailing GUI event-driven nature and additional sources
of inputs (e.g., sensor readings). To support developers in these tasks, we
introduce a novel, automated approach called CRASHSCOPE. This tool explores a
given Android app using systematic input generation, according to several
strategies informed by static and dynamic analyses, with the intrinsic goal of
triggering crashes. When a crash is detected, CRASHSCOPE generates an augmented
crash report containing screenshots, detailed crash reproduction steps, the
captured exception stack trace, and a fully replayable script that
automatically reproduces the crash on a target device(s). We evaluated
CRASHSCOPE's effectiveness in discovering crashes as compared to five
state-of-the-art Android input generation tools on 61 applications. The results
demonstrate that CRASHSCOPE performs about as well as current tools for
detecting crashes and provides more detailed fault information. Additionally,
in a study analyzing eight real-world Android app crashes, we found that
CRASHSCOPE's reports are easily readable and allow for reliable reproduction of
crashes by presenting more explicit information than human written reports.Comment: 12 pages, in Proceedings of 9th IEEE International Conference on
Software Testing, Verification and Validation (ICST'16), Chicago, IL, April
10-15, 2016, pp. 33-4
Formal Modelling, Testing and Verification of HSA Memory Models using Event-B
The HSA Foundation has produced the HSA Platform System Architecture
Specification that goes a long way towards addressing the need for a clear and
consistent method for specifying weakly consistent memory. HSA is specified in
a natural language which makes it open to multiple ambiguous interpretations
and could render bugs in implementations of it in hardware and software. In
this paper we present a formal model of HSA which can be used in the
development and verification of both concurrent software applications as well
as in the development and verification of the HSA-compliant platform itself. We
use the Event-B language to build a provably correct hierarchy of models from
the most abstract to a detailed refinement of HSA close to implementation
level. Our memory models are general in that they represent an arbitrary number
of masters, programs and instruction interleavings. We reason about such
general models using refinements. Using Rodin tool we are able to model and
verify an entire hierarchy of models using proofs to establish that each
refinement is correct. We define an automated validation method that allows us
to test baseline compliance of the model against a suite of published HSA
litmus tests. Once we complete model validation we develop a coverage driven
method to extract a richer set of tests from the Event-B model and a user
specified coverage model. These tests are used for extensive regression testing
of hardware and software systems. Our method of refinement based formal
modelling, baseline compliance testing of the model and coverage driven test
extraction using the single language of Event-B is a new way to address a key
challenge facing the design and verification of multi-core systems.Comment: 9 pages, 10 figure
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
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