A Literature Review on Automation Testing Using Selenium+Sikuli

Automation testing is a methodology that uses an application to implement the entire life cycle of the software in less time and provides efficiency and effectiveness to the testing software. In automation testing, the tester writes scripts and uses any suitable application software to test the software application. Automation is basically an automated process that is comprised of lots of manual activities. In other words, automation testing uses automation tools like Selenium, Sikuli, Appium, etc., to write test script and execute test cases, with no or minimal manual involvement required while executing an automated test suite. Usually, automation testers write test scripts and test cases using any of the automation tool and then groups test several cases. Here, we will discuss a neat case study explaining the automation testing using a hybrid test script.

Machine Learning Model to Predict Automated Testing Adoption

Software testing is an activity conducted to test the software under test. It has two approaches: manual testing and automation testing. Automation testing is an approach of software testing in which programming scripts are written to automate the process of testing. There are some software development projects under development phase for which automated testing is suitable to use and other requires manual testing. It depends on factors like project requirements nature, team which is working on the project, technology on which software is developing and intended audience that may influence the suitability of automated testing for certain software development project. In this paper we have developed machine learning model for prediction of automated testing adoption. We have used chi-square test for finding factors’ correlation and PART classifier for model development. Accuracy of our proposed model is 93.1624%.

The Impact of NLP on Software Testing

The number of applications being built and deployed everyday are increasing by leaps and bounds. To ensure the best user/client experience, the application needs to be free of bugs and other service issues. This marks the importance of testing phase in application development and deployment phase. Basically, testing is dissected into couple of parts being Manual Testing and Automation Testing. Manual testing, which is usually, an individual tester is given software guidance to execute. The tester would post the findings as “passed” or “failed” as per the guidance. But this kind of testing is very costly and time taking process. To eliminate these short comings, automation testing was introduced but it had very little scope and applications are limited. Now, that Artificial Intelligence has been foraying into many domains and has been showing significant impact over those domains. The core principles of Natural Language Processing that can be used in Software Testing are discussed in this paper. It also provides a glimpse at how Natural Language Processing and Software Testing will evolve in the future. Here we focus mainly on test case prioritization, predicting manual test case failure and generation of test cases from requirements utilizing NLP. The research indicates that NLP will improve software testing outcomes, and NLP-based testing will usher in a coming age of software testers work in the not-too-distant times.

Scriptless GUI Automation Testing Tool for Web Applications

Software tests must be repeated frequently throughout development cycles to attain certain quality. Every time program code is changed software assessments need to be repeated. Once created, automated tests may be run repeatedly at no extra value and they may be tons quicker than manually conducted test and free from human errors. Automated software program testing can lessen the time to run repetitive tests from days to hours. Test automation can easily run thousands of different complex test cases in each test run, so there is no manual testing involved. But Automation testing has its own disadvantages one of it is that the testers should come from a programming background. To eliminate this dependency over programmers Scriptless automation testing tools are emerging. There are many Scriptless GUI automation testing tools in the market that use various methods to achieve the goal, this paper proposes a new record and playback method to achieve the same using Selenium framework and JavaScript for web application.

Test Case Recording using JavaScript for Automation Testing

Test automation is the usage of software to monitor the execution of experiments and the comparison of actual results with expected results, apart from the software being evaluated. In a formalized web testing method already in place, test automation may automatize certain routine but required tasks and carry out extra testing which can be troublesome to try and perform manually. There is a need to simplify the test case recording process as most of the methods used at present requires the testers to generate scripts using any coding language. Hence, a light weight script less model is developed which uses JavaScript to record mouse and keyboard actions. Selenium framework is used to induce the JavaScript in the browser. Therefore, this paper focuses on developing a lightweight script less model for recording test cases on browsers.

TEST COVERAGE ENHANCEMENT USING HYBRID MODULAR APPROACH FOR WEB BASED APPLICATION

Automation testing may be a process that compares the particular results to the specified outcome within the software application. Automation testing is one of the foremost effective ways to check web-based applications since it allows you to hide more scenarios in less time. Selenium is one every of the foremost popular and effective automation frameworks that have been widely used for automating the testing of applications. The modular testing framework is one of the techniques used for automated testing, in which test scripts are built by breaking down the entire application into smaller pieces. Multiple data sets cannot be used thanks to hardcoded data within the test scripts. In this paper, a hybrid approach of keyword-data driven strategy is presented, in which the test data and script are separated to allow for various datasets and greater coverage. Furthermore, for improved test coverage, this split procedure is used across many modules.