3,339 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
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
Scripted GUI Testing of Android Apps: A Study on Diffusion, Evolution and Fragility
Background. Evidence suggests that mobile applications are not thoroughly
tested as their desktop counterparts. In particular GUI testing is generally
limited. Like web-based applications, mobile apps suffer from GUI test
fragility, i.e. GUI test classes failing due to minor modifications in the GUI,
without the application functionalities being altered.
Aims. The objective of our study is to examine the diffusion of GUI testing
on Android, and the amount of changes required to keep test classes up to date,
and in particular the changes due to GUI test fragility. We define metrics to
characterize the modifications and evolution of test classes and test methods,
and proxies to estimate fragility-induced changes.
Method. To perform our experiments, we selected six widely used open-source
tools for scripted GUI testing of mobile applications previously described in
the literature. We have mined the repositories on GitHub that used those tools,
and computed our set of metrics.
Results. We found that none of the considered GUI testing frameworks achieved
a major diffusion among the open-source Android projects available on GitHub.
For projects with GUI tests, we found that test suites have to be modified
often, specifically 5\%-10\% of developers' modified LOCs belong to tests, and
that a relevant portion (60\% on average) of such modifications are induced by
fragility.
Conclusions. Fragility of GUI test classes constitute a relevant concern,
possibly being an obstacle for developers to adopt automated scripted GUI
tests. This first evaluation and measure of fragility of Android scripted GUI
testing can constitute a benchmark for developers, and the basis for the
definition of a taxonomy of fragility causes, and actionable guidelines to
mitigate the issue.Comment: PROMISE'17 Conference, Best Paper Awar
Stack Overflow: A Code Laundering Platform?
Developers use Question and Answer (Q&A) websites to exchange knowledge and
expertise. Stack Overflow is a popular Q&A website where developers discuss
coding problems and share code examples. Although all Stack Overflow posts are
free to access, code examples on Stack Overflow are governed by the Creative
Commons Attribute-ShareAlike 3.0 Unported license that developers should obey
when reusing code from Stack Overflow or posting code to Stack Overflow. In
this paper, we conduct a case study with 399 Android apps, to investigate
whether developers respect license terms when reusing code from Stack Overflow
posts (and the other way around). We found 232 code snippets in 62 Android apps
from our dataset that were potentially reused from Stack Overflow, and 1,226
Stack Overflow posts containing code examples that are clones of code released
in 68 Android apps, suggesting that developers may have copied the code of
these apps to answer Stack Overflow questions. We investigated the licenses of
these pieces of code and observed 1,279 cases of potential license violations
(related to code posting to Stack overflow or code reuse from Stack overflow).
This paper aims to raise the awareness of the software engineering community
about potential unethical code reuse activities taking place on Q&A websites
like Stack Overflow.Comment: In proceedings of the 24th IEEE International Conference on Software
Analysis, Evolution, and Reengineering (SANER
Automated Test Selection for Android Apps Based on APK and Activity Classification
Several techniques exist for mobile test automation, from script-based techniques to automated test generation based on GUI models. Most techniques fall short in being adopted extensively by practitioners because of the very costly definition (and maintenance) of test cases. We present a novel testing framework for Android apps that allows a developer to write effective test scripts without having to know the implementation details and the user interface of the app under test. The main goal of the framework is to generate adaptive tests that can be executed on a significant number of apps, or different releases of the same app, without manual editing of the tests. The frameworks consists of: (1) a Test Scripting Language, that allows the tester to write generic test scripts tailored to activity and app categories; (2) a State Graph Modeler, that creates a model of the app’s GUI, identifying activities (i.e., screens) and widgets; (3) an app classifier that determines the type of application under test; (4) an activity classifier that determines the purpose of each screen; (5) a test adapter that executes test scripts that are compatible with the specific app and activity, automatically tailoring the test scripts to the classes of the app and the activities under test. We evaluated empirically the components of our testing framework. The classifiers were able to outperform available approaches in the literature. The developed testing framework was able to correctly adapt high-level test cases to 28 out of 32 applications, and to reduce the LOCs of the test scripts of around 90%. We conclude that machine learning can be fruitfully applied to the creation of high-level, adaptive test cases for Android apps. Our framework is modular in nature and allows expansions through the addition of new commands to be executed on the classified apps and activities
An App Performance Optimization Advisor for Mobile Device App Marketplaces
On mobile phones, users and developers use apps official marketplaces serving
as repositories of apps. The Google Play Store and Apple Store are the official
marketplaces of Android and Apple products which offer more than a million
apps. Although both repositories offer description of apps, information
concerning performance is not available. Due to the constrained hardware of
mobile devices, users and developers have to meticulously manage the resources
available and they should be given access to performance information about
apps. Even if this information was available, the selection of apps would still
depend on user preferences and it would require a huge cognitive effort to make
optimal decisions. Considering this fact we propose APOA, a recommendation
system which can be implemented in any marketplace for helping users and
developers to compare apps in terms of performance.
APOA uses as input metric values of apps and a set of metrics to optimize. It
solves an optimization problem and it generates optimal sets of apps for
different user's context. We show how APOA works over an Android case study.
Out of 140 apps, we define typical usage scenarios and we collect measurements
of power, CPU, memory, and network usages to demonstrate the benefit of using
APOA.Comment: 18 pages, 8 figure
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