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9 results for “GUI test”
Dataset for Code Review Guidelines for GUI-based Testing Artifacts
<p>The Excel file contains meta-data about collected white and gray literature, applied inclusion/exclusion criteria, the code system, and a list of identified guidelines.</p>
Dataset for the workshop paper titled "Automating GUI-based Software Testing with GPT-3" published at AIST 2023
<p>The training dataset for the research paper "Automating GUI-based Software Testing with GPT-3" presented at the 3rd International Workshop on Artificial Intelligence in Software Testing (AIST 2023), which was a part of the 16th IEEE International Conference on Software Testing, Verification and Validation (ICST 2023). The dataset contains prompt completion pairs acquired through user interaction with the software and was used to fine-tune the GPT-3 model. The dataset is in the .jsonl format specified by OpenAI.</p>
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>
PopSweeper: Automatically Detecting and Resolving App-Blocking Pop-Ups to Assist Automated Mobile GUI Testing
<p>Collected data for paper: PopSweeper: Automatically Detecting and Resolving App-Blocking Pop-Ups to Assist Automated Mobile GUI Testing</p>
Um estudo sobre as práticas de testes funcionais de GUI na indústria brasileira de software
<p>Teste de software é uma importante atividade no ciclo de desenvolvimento de software, a qual busca garantir a qualidade dos software criados. Quando um software não supre as necessidades implícitas e explicitas de seus usuários, pode-se dizer que ele carece de qualidade. A falta de qualidade de um software pode gerar vários problemas para o projeto de desenvolvimento de software, como atritos entre fornecedor e clientes/usuários, perdas financeiras, problemas legais por descumprimento de contratos e até risco à vida dos seus usuários em contextos críticos.</p> <p>Nesse sentido, distintos níveis de testes foram desenvolvidos para garantir a qualidade em diferentes aspectos do software. Uma abordagem popular é o teste funcional de interface, que avalia o software através da execução de eventos na tela do software, como o preenchimento de um campo de texto ou o clique de um botão. Os testes funcionais de interface ou GUI (\textit{Graphical User Interface}), podem ser executados tanto de forma manual, com a interação direta do testador no software, quanto automatizada, com a execução do software através de um \textit{script} programado.</p> <p>Nos últimos anos, é possível encontrar na literatura diversas soluções e técnicas propostas para lidar com os testes de GUI. No entanto, as pesquisas não se aprofundam em analisar como os profissionais da indústria vem executando esse tipo de teste, deixando assim uma lacuna na área. Por isso, este estudo propõe investigar como os testadores estão realizando testes de interface em seus projetos. Assim, primeiramente foi realizado um questionário online com 222 profissionais de teste com distintas experiências, cargos e funções. O resultado indicou que ainda há muitos profissionais que realizam testes exclusivamente de forma manual. Além disso, a maioria dos profissionais concordam que as ferramentas utilizadas na automação de testes atendem as necessidades de negócio, embora tenham limitações técnicas. </p> <p>Em seguida, realizamos uma entrevista semiestruturada com 20 profissionais de testes para complementar os resultados obtidos com o questionário. Os resultados indicaram que testadores que realizam apenas testes de GUI manuais não criam testes de GUI automatizados devido ao contexto do projeto em que trabalham, suas preferências profissionais e também por questões educacionais, como dificuldade com programação. Além disso, foram observadas distintas limitações para testes de GUI manuais e automatizados que impactam negativamente as atividades desempenhadas pelos profissionais, como dificuldades relacionadas à cultura da empresa e problemas técnicos relacionados ao software sob teste. Os resultados combinados fornecem uma visão geral dos testes de GUI na indústria de software brasileira.</p>
Supporting Data Set for Paper "Using GUI Test Videos to Obtain Stakeholders' Feedback"
<p>This data set is a supporting material for an accepted paper "Using GUI Test Videos to Obtain Stakeholders’ Feedback" on <a href="https://conf.researchr.org/track/icssp-2023/">ICSSP 2023</a>.</p> <p>This dataset consists of</p> <ul> <li>Questionnaires of control and experimental groups <ul> <li> <p>Questionnaire-Control Group (German).pdf -> Original quetionnaire for control group (in German)</p> </li> <li> <p>Questionnaire-Control Group (translated)-v03.pdf -> Translated quetionnaire for control group (in English)</p> </li> <li> <p>Questionnaire-Experimental Group (German).pdf -> Original quetionnaire for experimental group (in German)</p> </li> <li> <p>Questionnaire-Experimental Group (translated)-v03.pdf -> Translated quetionnaire for experimental group (in English)</p> </li> </ul> </li> <li>Survey-Data-v25.ods -> Collected data through questionnaires</li> <li>Calculate-Mann-Whitney-U-Test-v07.ods -> Detailed calculation of Mann Whitney U Test</li> <li>The videos of the ten scenarios in this study are also available on OneDrive <a href="https://1drv.ms/f/s!AtqkJ5cB802BoABI7w_Bft9H8Psi?e=upToep">https://1drv.ms/f/s!AtqkJ5cB802BoABI7w_Bft9H8Psi?e=upToep</a> , where you can play them directly in browsers.</li> </ul>
A Comprehensive Evaluation of Q-Learning Based Automatic Web GUI Testing
<p>This repository holds experimental data and figures of our paper "A Comprehensive Evaluation of Q-Learning Based Automatic Web GUI Testing" in 10th International Conference on Dependable Systems and Their Applications (DSA) 2023.</p>
Supporting Data Set for Paper "Can Videos as a By-Product of GUI Testing Help Developers Understand GUI Tests?"
<p>This data set is a supporting material for an accepted paper "Can Videos as a By-Product of GUI Testing Help Developers Understand GUI Tests?" on 2023 IEEE 31st International Requirements Engineering Conference Workshops (REW 2023).</p> <p>This dataset consists of</p> <ul> <li>a consent form of the study in English;</li> <li>a tutorial video for TakeNote App (see <em>TakeNote Tutorial-v02</em>);</li> <li>a questionnaire in HTML format;</li> <li>used videos (in <em>HTML Video Player with Videos and VTT files</em>) and screenshots;</li> <li>the source code of the HTML Video Player (in <em>HTML Video Player with Videos and VTT files</em>);</li> <li>obtained and coded results from the questionnaire (see <em>Study-Data-4EmpiRE-v22</em>);</li> <li>calculation steps of the Mann-Whitney U Test;</li> <li>the source code of the TakeNote App.</li> </ul>
Dataset for the workshop paper titled "GUI-Based Software Testing: An Automated Approach Using GPT-4 and Selenium WebDriver" submitted at A-Test 2023
<p>The results dataset is associated with the research paper titled "GUI-Based Software Testing: An Automated Approach Using GPT-4 and Selenium WebDriver", which has been submitted to the 14th edition of A-TEST, a workshop co-located with ASE 2023. ASE, or the Automated Software Engineering conference, is a premier event in the software engineering domain that emphasizes the role of automation in the software development process. The conference is set to take place in Kirchberg, Luxembourg on September 15.</p> <p>Inside the zipped results folder, each test run is cataloged in a timestamped subdirectory. Each of these subdirectories contains three files:</p> <ul> <li><strong>config.json:</strong> A configuration file specific to that test run</li> <li><strong>output.log:</strong> An output log detailing the test's processes and results</li> <li><strong>past_actions.json:</strong> A JSON file designed for streamlined, programmatic access to the test run's data</li> </ul> <p> </p>
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