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12 results for “Android applications”
Data and Code for the paper "GUI Testing of Android Applications: Investigating the Impact of the Number of Testers on Different Exploratory Testing Strategies"
<p>This package contains data and code to replicate the findings presented in our paper titled "<em>GUI Testing of Android Applications: Investigating the Impact of the Number of Testers on Different Exploratory Testing Strategies</em>".</p> <p><strong>Abstract</strong></p> <p>Graphical User Interface (GUI) testing plays a pivotal role in ensuring the quality and functionality of mobile apps. In this context, Exploratory Testing (ET), a distinctive methodology in which individual testers pursue a creative, and experience-based approach to test design, is often used as an alternative or in addition to traditional scripted testing. Managing the exploratory testing process is a challenging task, that can easily result either in wasteful spending or in inadequate software quality, due to the relative unpredictability of exploratory testing activities, which depend on the skills and abilities of individual testers. A number of works have investigated the<br> diversity of testers’ performance when using ET strategies, often in a crowdtesting setting. These works, however, investigated ET effectiveness in detecting bugs, and not in scenarios in which the goal is to generate a re-executable test suite, as well. Moreover, less work has been conducted on evaluating the impact of adopting different exploratory testing strategies. As a first step towards filling this gap in the literature, in this work we conduct an empirical evaluation involving four open-source Android apps and twenty masters students, that we believe can be representative of practitioners partaking in exploratory testing activities. The students were asked to generate test suites for the apps using a Capture and Replay tool and different exploratory testing strategies. We then compare the effectiveness, in terms of aggregate code coverage, that different-sized groups of students using different exploratory testing strategies may achieve. Results provide deeper insights into code coverage dynamics to project managers interested in using exploratory approaches to test simple Android apps, on which they can make more informed decisions.</p> <p> </p> <p><strong>Contents and Instructions</strong></p> <p>This package contains:</p> <ul> <li><strong>apps-under-test.zip</strong> A zip archive containing the source code of the four Android applications we considered in our study, namely MunchLife, TippyTipper, Trolly, and SimplyDo.</li> <li><strong>apps-under-test-instrumented.zip</strong> A zip archive containing the instrumented source code of the four Android applications we used to compute branch coverage.</li> <li><strong>students-test-suites.zip</strong> A zip archive containing the test suites developed by the students using Uninformed Exploratory Testing (referred to as "Black Box" in the subdirectories) and Informed Exploratory Testing (referred to as "White Box" in the subdirectories). This also includes coverage reports.</li> <li><strong>compute-coverage-unions.zip </strong>A zip archive containing Python scripts we developed to compute the aggregate LOC coverage of all possible subsets of students. The scripts have been tested on MS Windows. To compute the LOC coverage achieved by any possible subsets of testers using IET and UET strategies, run the <em>analysisAndReport.py</em> script. To compute the LOC coverage achieved by mixed crowds in which some testers use a U+IET approach and others use a UET approach, run the <em>analysisAndReport_UET_IET_combinations_emma.py</em> script.</li> <li><strong>branch-coverage-computation.zip </strong>A zip archive containing Python scripts we developed to compute the aggregate branch coverage of all considered subsets of students. The scripts have been tested on MS Windows. To compute the branch coverage achieved by any possible subsets of testers using UET and I+UET strategies, run the <em>branch_coverage_analysis.py</em> script. To compute the code coverage achieved by mixed crowds in which some testers use a U+IET approach and others use a UET approach, run the <em>mixed_branch_coverage_analysis.py</em> script.</li> <li><strong>data-analysis-scripts.zip</strong> A zip archive containing R scripts to merge and manipulate coverage data, to carry out statistical analysis and draw plots. All data concerning RQ1 and RQ2 is available as a ready-to-use R data frame in the <em>./data/all_coverage_data.rds</em> file. All data concerning RQ3 is available in the <em>./data/all_mixed_coverage_data.rds </em>file.</li> </ul>
Data supplementing the conference paper "Who you gonna call? Analyzing web requests in Android applications", 14th International Conference on Mining Software Repositories 2017.
<p>This repository contains the data supplementing the paper:</p> <p>M. Rapoport, P. Suter, E. Wittern, O. Lhótak, J. Dolby, "Who you gonna call? Analyzing web requests in Android applications", MSR 2017.</p> <p>A detailed description of the data is included in the archive in README.md.</p>
DEDICAT 6G Enhanced Experience Android streaming application delay measurements
<p>Performance measurements for the developed streaming application and comparison with the latest smartphones.</p>
Artifact for A Survey on Techniques to Profile the Energy Consumption of Android Applications
<p>To ensure availability and reproducibility, we have made all study artifacts publicly available for "<strong>A Survey on Techniques to Profile the Energy Consumption of Android Applications</strong>", a paper submitted to <strong><em>ACM Computing Surveys</em></strong>. In this study, we review state-of-the-art software-based energy profilers tailored for Android applications and propose a novel taxonomy for comparative analysis. We provide a brief overview of these energy profilers and compare their features. Based on the conducted literature review, we present conceptual architectures of the different types of software-based energy profilers to help build a tool that addresses the existing limitations in energy profiling.</p> <p>This repository contains the following files:</p> <ul> <li>Coding list generated from the Nvivo project (Files compared by number of codes). This file contains the codes used in taxonomy and the number of code references.</li> <li>Coding hierarchy generated from the Nvivo project (Codes compared by number of coding references). This file shows the hierarchy of codes and their share visually.</li> <li>List of selected papers (generated from the Nvivo project). This file presents the list of selected papers and metadata about these papers including publishing year, publisher, type of publication, etc.</li> <li>Search queries (SearchQueries). This file contains the search queries that are used to obtain the publications for review.</li> <li>Summary of the papers generated from the Nvivo project (Summary). This file contains the summary of the papers.</li> </ul>
Android applications obfuscation analysis
<p>Dataset for the research "Android Software Protection in the Wild: A Survey"</p>
Measurement of Pulse Pressure Variation and Cardiac Output by a New Application for Android™ (Captesia™)
ClinicalTrials.gov study NCT02692222. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Development and Validation of an Android-based Application for Anaesthesia Neuromuscular Monitoring
ClinicalTrials.gov study NCT03605225. IPD Sharing: Not stated. Countries: 1. Publications: 1.
CroCoord v1.0 – an Android application for nearly real-time 3D PPP multi-frequency multi-GNSS static surveying using smartphones
<p>Two hour of GNSS data collected on 31st December 2019 on one point with Android dual-frequency smartphone Xiaomi Mi Mix-3 and permanent station in the surrounding of the measured point.</p>
e-GAB: Electronic Geriatric Assessment Bundle: Development of an Arabic Self-administered Android CGA Application
ClinicalTrials.gov study NCT04211285. IPD Sharing: Not stated. Countries: 1. Publications: 0.
The Effectiveness of the Android Application as a Pharmaceutical Education Tool in Hypertensive Patients
ClinicalTrials.gov study NCT05728723. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Context-Aware Android Applications Testing
<p>Vídeo do SBES - Research</p>
A dataset of open-source android applications collected from F-droid
<p>The dataset contains 3489 open-source android applications collected from F-droid.</p>
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