Web Application Testing Research Articles

Pengujian Aplikasi Kas Keuangan Menggunakan Katalon

The most important thing in making financial improvements in a business is by recording cash receipts and cash disbursements. The output generated from the application is a report on cash receipts and cash disbursements per day, per month and per year, but before the application is published it is necessary to test, if there is a problem when the admin records cash then the risk is very fatal because it is related to joint finance. To solve this problem the author uses a test with Katalon. Katalon Studio is a software used to perform a mechanization test which is extended by Katalon, Inc. The software is built on Selenium's open source mechanization framework, Appium with a dedicated IDE interface for testing web, API, mobile and desktop applications. With Katalon, it is hoped that the cash website will run well and smoothly

FEATURES OF MODERN WEB APPLICATION TESTING

This paper investigates the technology and approaches to testing web applications in modern frameworks. The main techniques of manual testing, which require a large amount of manual work, are considered. The disadvantages of implementing the black-box testing method include the development of a formal specification, and the application of the white-box testing method requires analysis of software code. Particular attention is given to automated testing technologies, which are proposed to be used to develop sites in modern frameworks. The Test pyramid and test levels are described in detail, and the main advantages and disadvantages of each level are analyzed. At the first level, a set of tests consists of unit tests that verify that an individual unit is working correctly according to the requirements of the specification. At the second level of the automation pyramid – integration tests check the interaction of a fragment of code with external components. At the third level – end-to-end tests perform testing of various user scenarios, and UI tests check the correct operation of the web application interface. It is proposed to use behavioral development technology (BDD) for automated testing of web applications. BDD focuses on studying problems formulated based on customer stories and the construction of logic and tests based on these problems. BDD tests are best worked for integration testing, which involves testing different user scenarios. A dependency injection (DI) design template has been used to improve the efficiency of the web application development process, with tools built into modern frameworks. This template applies the transfer of dependencies to an external, specially designed software component. Dependence is injected using a special IoC container.

Approaches to web application performance testing and real-time visualization of results

Problem. Today, performance testing is an integral part of the web applications quality assurance whose performance failures and performance issues affect the business of their owners. Goal. The goal of the work is to generalize approaches and methods to improve the quality of web applications and develop recommendations for improving performance testing using open source tools. The object of research is the processes of testing web applications. The subject of research is the approaches, methods and tools of performance testing. Methodology. The study identified the impact of software performance testing on its quality and its main types, namely load testing, stress testing, volume testing, stability testing. The main stages of performance testing and their content were identified. To implement modern automated testing technologies, the advantages and disadvantages of the most popular tools for testing performance in the modern IT market and continuous visualization of their results were analyzed and identified. The following factors should be considered when selecting a performance testing tool: compatibility, scalability, clarity, and monitoring. Time series databases and visualization tools are used for continuous monitoring of test results together with testing tools. Results. During the practical implementation of the research results, the goals of different types of performance testing, indicators of normal operation of the system without exceeding the permissible limits, test scenarios and test results were identified. Visualization of test results in JMeter is shown and a board for continuous real-time visualization is created. Originality. The originality of the study lies in unlocking the potential of open source tools for testing the performance of web applications and visualizing its results. On the basis of comparative analysis the spheres of application of tools for performance testing are substantiated. Practical value. The practical value lies in the development of methodological bases for testing the performance of web applications in real time on the example of the connection of tools Jmeter – InfluxDB – Grafana.

Selenium-Jupiter: A JUnit 5 extension for Selenium WebDriver

Selenium WebDriver is a library that allows controlling web browsers (e.g., Chrome, Firefox, etc.) programmatically. It provides a cross-browser programming interface in several languages used primarily to implement end-to-end tests for web applications. JUnit is a popular unit testing framework for Java. Its latest version (i.e., JUnit 5) provides a programming and extension model called Jupiter. This paper presents Selenium-Jupiter, an open-source JUnit 5 extension for Selenium WebDriver. Selenium-Jupiter aims to ease the development of Selenium WebDriver tests thanks to an automated driver management process implemented in conjunction with the Jupiter parameter resolution mechanism. Moreover, Selenium-Jupiter provides seamless integration with Docker, allowing the use of different web browsers in Docker containers out of the box. This feature enables cross-browser testing, load testing, and troubleshooting (e.g., configurable session recordings). This paper presents an example case in which Selenium-Jupiter is used to evaluate the performance of video conferencing systems based on WebRTC. This example case shows that Selenium-Jupiter can build and maintain the required infrastructure for complex tests effortlessly.

Development and Pilot Testing of the Baby-Feed Web Application for Healthcare Professionals and Parents to Improve Infant Diets

Development and Pilot Testing of the Baby-Feed Web Application for Healthcare Professionals and Parents to Improve Infant Diets

RESEARCH OF METHODS OF AUTOMATED SEARCH OF “SQL INJECTION” TYPE VULNERABILITIES IN WEB APPLICATIONS

The article presents the results of a scientific and methodological study of the methods of automated search for SQL vulnerabilities in web applications. An example of an attack using a typical SQL injection is provided. The classification ofweb application security assessment methods based on penetration testing is given. The results of practical studies of the operation of the most widely used web scanners for automated vulnerability testing of web applications are given. Based on the results, a comparison of the effectiveness of penetration testing methods has been made. The possible directions of further research into the methods of automated search for SQL vulnerabilities in web applications are substantiated, taking into ac-count the results obtained, in particular the values of the Youden Index.

Architecture and Model of Neural Network Based Service for Choice of the Penetration Testing Tools

During penetration testing of web applications, different tools are actively used to relieve the tester from repeating monotonous operations. The difficulty of the choice is in the fact that there are tools with similar functionality, and it is hard to define which tool is best to choose for a particular case. In this paper, a solution of the problem with making a choice by creating a Web service that will use a neural network on the server side is proposed. The neural network is trained on data obtained from experts in the field of penetration testing. A trained neural network will be able to select tools in accordance with specified requirements. Examples of the operation of a neural network trained on a small sample of data are shown. The effect of the number of neural network learning epochs on the results of work is shown. An example of input data is given, in which the neural network could not select the tool due to insufficient data for training. The advantages of the method shown are the simplicity of implementation (the number of lines of code is used as a metric) and the possibility of using opinions about tools from various experts. The disadvantages include the search for data for training, the need for experimental selection of the parameters of the neural network and the possibility of situations where the neural network will not be able to select tool that meets the specified requirements.

Model-based automated testing of JavaScript Web applications via longer test sequences

JavaScript has become one of the most widely used languages for Web development. Its dynamic and event-driven features make it challenging to ensure the correctness of Web applications written in JavaScript. A variety of dynamic analysis techniques have been proposed which are, however, limited in either coverage or scalability. In this paper, we propose a simple, yet effective, model-based automated testing approach to achieve a high code-coverage within the time budget via testing with longer event sequences. We implement our approach as an open-source tool LJS, and perform extensive experiments on 21 publicly available benchmarks. On average, LJS is able to achieve 86.5% line coverage in 10 minutes. Compared with JSDEP, a state-of-the-art breadth-first search based automated testing tool enriched with partial order reduction, the coverage of LJS is 11%–19% higher than that of JSDEP on real-world large Web applications. Our empirical findings support that proper longer test sequences can achieve a higher code coverage in JavaScript Web application testing.

An Approach to detect multiple diseases using machine learning algorithm

There are multiple techniques in machine learning that can in a variety of industries, do predictive analytics on large amounts of data. Predictive analytics in healthcare is a difficult endeavour, but it can eventually assist practitioners in making timely decisions regarding patients’ health and treatment based on massive data. Diseases like Breast cancer, diabetes, and heart-related diseases are causing many deaths globally but most of these deaths are due to the lack of timely check-ups of the diseases. The above problem occurs due to a lack of medical infrastructure and a low ratio of doctors to the population. The statistics clearly show the same, WHO recommended, the ratio of doctors to patients is 1:1000 whereas India’s doctor-to-population ratio is 1:1456, this indicates the shortage of doctors. The diseases related to heart, cancer, and diabetes can cause a potential threat to mankind, if not found early. Therefore, early recognition and diagnosis of these diseases can save a lot of lives. This work is all about predicting diseases that are harmful using machine learning classification algorithms. In this work, breast cancer, heart, and diabetes are included. To make this work seamless and usable by the mass public, our team made a medical test web application that makes predictions about various diseases using the concept of machine learning. In this work, our aim to develop a disease-predicting web app that uses the concept of machine learning-based predictions about various diseases like Breast cancer, Diabetes, and Heart diseases.

On the Use of Generative Deep Learning Approaches for Generating Hidden Test Scripts

With the advent of web 2.0, web application architectures have been evolved, and their complexity has grown enormously. Due to the complexity, testing of web applications is getting time-consuming and intensive process. In today’s web applications, users can achieve the same goal by performing different actions. To ensure that the entire system is safe and robust, developers try to test all possible user action sequences in the testing phase. Since the space of all the possibilities is enormous, covering all user action sequences can be impossible. To automate the test script generation task and reduce the space of the possible user action sequences, we propose a novel method based on long short-term memory (LSTM) network for generating test scripts from user clickstream data. The experiment results clearly show that generated hidden test sequences are user-like sequences, and the process of generating test scripts with the proposed model is less time-consuming than writing them manually.

Multiple Disease Prediction Using Machine Learning

There are several techniques in machine learning that can perform predictive analytics on large amounts of data across industries. Predictive analytics in healthcare is a difficult task, but it can ultimately help practitioners in making timely decisions regarding the health and treatment of patients based on massive data. Diseases such as breast cancer, diabetes and heart disease outbreaks cause many deaths worldwide, but most of these deaths are due to a lack of early disease control. The above problem occurs due to inadequate medical infrastructure and low ratio of doctors to population. Statistics clearly show the same, WHO advises, doctor to patient ratio is 1:1000, while doctor to population ratio in India is 1:1456, this shows shortage of doctors. Diseases related to heart, cancer and diabetes can pose a potential threat to humanity if not detected early. Therefore, early recognition and diagnosis of these diseases can save many lives. This thesis is all about predicting diseases that are harmful using machine learning classification algorithms. Breast cancer, heart and diabetes are included in this work. To make this work seamless and usable by the general public, our team has created a medical test web application that predicts various diseases using the concept of machine learning. In this work, we aim to develop a machine learning-based prediction concept for various diseases such as breast cancer, diabetes, and heart disease.

Web Application Penetration Testing Using SQL Injection Attack

A web application is a very important requirement in the information and digitalization era. With the increasing use of the internet and the growing number of web applications, every web application requires an adequate security level to store information safely and avoid cyber attacks. Web applications go through rapid development phases with short turnaround times, challenging to eliminate vulnerabilities. The vulnerability on the web application can be analyzed using the penetration testing method. This research uses penetration testing with the black-box method to test web application security based on the list of most attacks on the Open Web Application Security Project (OWASP), namely SQL Injection. SQL injection allows attackers to obtain unrestricted access to the databases and potentially collecting sensitive information from databases. This research randomly tested several websites such as government, schools, and other commercial websites with several techniques of SQL injection attack. Testing was carried out on ten websites randomly by looking for gaps to test security using the SQL injection attack. The results of testing conducted 80% of the websites tested have a weakness against SQL injection attacks. Based on this research, SQL injection is still the most prevalent threat for web applications. Further research can explain detailed information about SQL injection with specific techniques and how to prevent this attack.

Evaluation of an e-health platform for informal caregivers and health professionals: the case study of Help2Care

ABSTRACT The Help2Care e-Health platform was developed in order to capacitate informal caregivers with digital, multimedia training materials. Health professionals select these materials according to the needs of the homebound patients under the supervision of these caregivers. In turn, caregiver can then use their smartphones to consult and apply the care procedures illustrated by these materials. In this paper, we present the results of performed usability tests for both web and mobile software applications of the Help2Care platform. These indicate an overall positive outcome, revealing less usable aspects such as the navigation flow in the web application and some design elements in the mobile application. Important written feedback was also collected, which we took into consideration to improve the software features of the platform.

A Multi-Objective Approach for Test Suite Reduction During Testing of Web Applications

During the development and maintenance phases of evolving software, new test cases would be needed for the verification of the accuracy of the modifications as well as for new functionalities leading to an increase in the size of the test suite. Various related objectives are to be kept in mind while reducing the original test suite by removing redundancy and generating a practical representative set of the unique test cases, some of which may need to be maximized and the remaining ones minimized. This paper presents a multi-objective approach for the test suite reduction problem in which one objective is to be minimized and the remaining two maximized. In this study, experiments were performed on diverse versions of four web applications. Various state-of-the-art algorithms and their updated versions were compared with non-dominated sorting genetic algorithm-II (NSGA-II) for performance evaluation. Based on experimental findings, it was concluded that NSGA-II outperforms all other algorithms; moreover, the algorithm attempts to satisfy all the objectives without compromising coverage.

Comparative analysis of solutions used in Automated Testing of Internet Applications

This article is devoted to the comparison of the effectiveness of solutions used in automatic testing of web applications. In order to carry out the analysis, test scenarios were created and tests were carried out using each of the tested programming frameworks (Selenium WebDriver, Cucumber, Ranorex, Robot Framework, Cypress, Unified Functional Testing, TestComplete and Katalon Studio). The study showed that there is no single best tool that meets all the requirements. Taking into account the analyzed aspects of effectiveness, the TestComplete program was the best, however, when choosing a solution, the team's skills and project specification should be taken into account.

Automated Visual Testing of Application User Interfaces Using Static Analysis of Screenshots

Mobile and web applications must operate and be displayed correctly on many different devices and browsers. The visual testing of web or mobile applications is usually a manual process that requires a significant amount of testing time, meaning that applications are tested only on a few devices. It is then assumed that the applications will be displayed correctly on other compatible or similar devices. This paper presents an automated visual testing method for user interfaces. The main contributions of this paper are a classification scheme for visual defects of user interfaces and the definition of an automatic visual testing method that tests applications on many different devices with varying hardware and software parameters. The method is based on an automated search for defects using heuristic and expected state prediction algorithms, which involves analyzing the resources used by applications and screenshots. The testing method works by executing applications on a full set of devices, taking a screenshot at every execution step, and analyzing each of these screenshots. The manual as well as automated testing approaches were validated on 781 of Android applications. The experimental results show that the proposed method has advantages over manual testing.

Discovering reflected cross-site scripting vulnerabilities using a multiobjective reinforcement learning environment

Tools that automate testing of web applications for Cross-Site Scripting (XSS) vulnerabilities perform well when they have a strong knowledge base. Though, they heavily rely on brute force, which is not always an effective choice. On the other hand, expert penetration testers adopt exploit methods that are more accurate, but often not structured. We propose to solve the above mentioned problems, by designing and implementing an intelligent agent, called Suggester, that recommends actions to penetration testers. First, a black-box testing methodology inspired by a penetration tester’s behavior, is developed. Such methodology consists of sending a sequence of strings to a web application and observing the responses. Then, an agent is trained to produce attack strings using the framework of a Multiobjective Reinforcement Learning environment (MORL), with a parameterized action space. Each complete attack string is identified as a separate objective to reach. Q-Learning is used to train the agent upon separate, unrelated objectives. Then, the learned actions are suggested to a human-in-the-loop, who performs the actions and collects observations. This allows to orchestrate the agent into pursuing the right objective and selecting the next best action to recommend. The final evaluation proves the scalability of the proposed solution, as well as show an increase in accuracy when compared to other automated scanners.

Genetic-based web regression testing: an ontology-based multi-objective evolutionary framework to auto-regression testing of web applications

Genetic-based web regression testing: an ontology-based multi-objective evolutionary framework to auto-regression testing of web applications

AutoScaleSim: A simulation toolkit for auto-scaling Web applications in clouds

Auto-scaling of Web applications is an extensively investigated issue in cloud computing. To evaluate auto-scaling mechanisms, the cloud community is facing considerable challenges on either real cloud platforms or custom test-beds. Challenges include – but not limited to – deployment impediments, the complexity of setting parameters, and most importantly, the cost of hosting and testing Web applications on a massive scale. Hence, simulation is presently one of the most popular evaluation solutions to overcome these obstacles. Existing simulators, however, fail to provide support for hosting, deploying and subsequently auto-scaling of Web applications. In this paper, we introduce AutoScaleSim, which extends the existing CloudSim simulator, to support auto-scaling of Web applications in cloud environments in a customizable, extendable and scalable manner. Using AutoScaleSim, the cloud community can freely implement/evaluate policies for all four phases of auto-scaling mechanisms, that is, Monitoring, Analysis, Planning and Execution. AutoScaleSim can also be used for evaluating load balancing algorithms similarly. We conducted a set of experiments to validate and carefully evaluate the performance of AutoScaleSim in a real cloud platform, with a wide range of performance metrics.

Prioritising Server Bugs via Inter-process Concolic Testing

Context: Most approaches to automated white-box testing consider the client side and the server side of a web application in isolation from each other. Such testers lack a whole-program perspective on the web application under test. Inquiry: We hypothesise that an additional whole-program perspective would enable the tester to discover which server side errors can be triggered by an actual end user accessing the application through the client, and which ones can only be triggered in hypothetical scenarios. Approach: In this paper, we explore the idea of employing such a whole-program perspective in testing. To this end, we develop , a novel concolic tester which operates on full-stack JavaScript web applications, where both the client and the server side are JavaScript processes communicating via asynchronous messages -- as enabled by the WebSocket or Socket.IO-libraries. Knowledge: We find that the whole-program perspective enables discerning high-priority errors, which are reachable from a particular client, from low-priority errors, which are not accessible through the tested client. Another benefit of the perspective is that it allows the automated tester to construct practical, step-by-step scenarios for triggering server side errors from the end user's perspective. Grounding: We apply on a collection of web applications to evaluate how effective testing is in distinguishing between high- and low-priority errors. The results show that correctly classifies the majority of server errors. Importance: This paper demonstrates the feasibility of testing as a novel approach for automatically testing web applications. Classifying errors as being of high or low importance aids developers in prioritising bugs that might be encountered by users, and postponing the diagnosis of bugs that are less easily reached.