QA & Software Testing
Artificial intelligence in the software testing domain will only optimize the testing process by performing repetitive tasks with utmost precision, providing reports on code quality and also systematically organizing the testing process. AI testing tools will prove to be rewarding in terms of taking up the automation activities a notch higher. These tools can be properly configured, performance can be analysed and correct results can be made attainable. In this article, you will know about the role of artificial intelligence in software testing.
What is AI testing?
AI will enhance every aspect of testing that includes environment management, migration, test case optimization, result analysis, automation execution and defect prediction. AI testing will make the software development life cycle process easier by applying problem-solving and reasoning techniques.
The role of AI in software testing:
With the inclusion of AI in software testing, the testing team can scale up towards a precision-based and automate continuous testing process. An AI-powered continuous testing platform can identify changed controls effectively, and with constant algorithm updates, even minute changes can be observed.
From an automation testing standpoint, AI can be used in object application categorization for all user interfaces. Here, when you create tools, recognized controls are being categorized and testers can pre-train controls that are basically seen in out-of-the-box setups. Once proper observation takes place, a technical map can be created by testers, so that AI can focus on the Graphical User Interface in order to obtain labels for different controls.
AI will streamline the testing process:
Systems will learn source analysis through the AI platform and apply knowledge as and when required. AI tester will be in a position to come up with precise results. Applying AI technologies in the sphere of data analysis will eliminate the probability of human errors, time to run a test will be shortened and find possible defects. The QA team will not be burdened to handle large data volumes.
The role of AI in strengthening the knowledge base of testers and engineers:
AI testing will eventually make the whole testing process way more efficient. A huge load of test execution can be taken over by AI. QA engineers will have to put in strategic efforts to build new skills that are in line with artificial intelligence. With the application of AI in the software testing domain, competencies must be diversified into neuro-linguistic programming, algorithm analysis, business intelligence, math optimization and AI testing.
Engineers and testers must learn and understand the above mentioned competencies in order to work justifiably. On the contrary, companies must also invest in skill development of the testing teams. Following are the three prominent testing roles in line with artificial intelligence:
Conclusion: The above article clearly depicts the important roles of AI in software testing from different perspectives. If you are looking forward to implementing AI-based software testing for your specific software development projects, then do get connected with a premium software testing services company that will provide you strategic guidance and a viable strategy that can prove to be beneficial for your testing needs.
About the author: I am a technical content writer focused on writing technology specific articles. I strive to provide well-researched information on the leading market savvy technologies.
The inclusion of artificial intelligence and machine learning in the software testing domain is steadily gaining demand. Certain organizations have implemented AI in software testing and were able to see astounding results in line with speed and efficiency. The combination is such that AI can be used in software testing and on the contrary, software testing methods can be applied to AI systems for improved performance. In this article, you will get to know about the types of machine learning and the best AI testing tools.
What is Machine Learning (ML)?
It is a branch of AI that is focused on developing applications by learning from data patterns, without the need for any programming.
Following are the three types of machine learning:
1. Supervised learning: In this type, on the labeled data platform, the machine learning algorithm is trained. For this method of work, the data needs to be labeled accurately. A small training dataset is given to the ML algorithm to work with. The training dataset is a small part of the big dataset and a basic idea is given to the algorithm about the problem, solutions and data points that need to be dealt with. The final dataset is almost similar to the training dataset when compared to its characteristics. The labeled parameters required for the problem are provided to the algorithm.
2. Unsupervised learning: This type of ML can easily work with unlabelled data. This means to make the dataset machine-readable, human labor is not required. This in turn allows for much larger datasets that can be worked out by the program.
3. Reinforcement learning: In this type, an algorithm is used that basically improves upon itself and from new situations tries to learn using a trial-and-error method. Favorable outputs are reinforced or encouraged and non-favorable outputs are discouraged. The algorithm is put into a work environment with a reward system and interpreter, and this is how reinforcement learning works. When the algorithm is iterated, the interpreter receives the output result, which then decides whether the outcome is favorable or not.
What is AI in software testing?
The concept of introducing AI in software testing is to make the software development process more efficient and streamlined. By applying problem-solving and reasoning methods, the testing process can be carried out in a more efficient manner. The amount of repetitive tasks can also be drastically reduced by applying AI in software testing.
Following are some of the best AI testing tools:
1. Testim: It is a functional testing tool that uses AI and ML to speed up the maintenance, execution and authoring of automated tests. This tool can run on different browsers such as Firefox, Chrome, Internet Explorer, Edge and Safari. There are two plans available by this tool and those are “basic” and “pro.” The basic plan as usual comes with limited features, whereas the Pro plan supports almost everything in line with test automation activities.
2. Functionize: It is a cloud-based automated testing tool that is basically used for load, performance and functional testing. This tool uses artificial intelligence and machine learning for test creation, diagnosis and maintenance. One of the best things about this tool is that there is not much thought process involved before carrying out any tests. You have to just type in English what you want to test and the Natural Language Processing (NLP) will create the functional test cases.
3. Applitools: It is an AI-powered visual UI testing and monitoring software. It can be used by professionals in manual QA, DevOps, test automation, engineering and digital transformation teams. The ML and AI algorithms are adaptive.
4. Mabi: It is a unified DevTestOps platform that provides testers and developers to easily create and run automated functional UI tests at a faster pace. Auto-healing tests can be generated by this tool, which can adapt to UI changes.
Conclusion: After reading this article, you might have got some idea about the types of ML and some of the best AI testing tools. If you are looking for methodical and strategic information on AI and ML from a software testing viewpoint, then do get connected with a well-renowned software testing services company.
About the author: I am a technical content writer focused on writing technology specific articles. I strive to provide well-researched information on the leading market savvy technologies.
Rapidly changing IT has necessitated the need for high-quality and stable products. To deliver quality products to customers’ enterprises adopt various software testing practices. Among the various software testing methods, functional testing is one of the most important testing methods. Let us now try to understand some of the best practices of this testing method in detail.
What is functional testing?
It is a black box type of software testing method that is performed to validate the functionality of the software. The main purpose of this test is to check each functionality of the application by providing certain inputs and validating the outputs against the functional requirements. It is also known as specification-based testing as it is completely based on program specifications. This test checks User Interface, APIs, Database, Security, Client/Server communication, and other functionalities of the application.
Why functional testing is performed?
Here are some of the reasons that depict the need for this testing
What are the best practices of functional testing?
Here are some of the best practices of this testing method:
Gather all information and plan the test: Before starting the test all teams must have a proper understanding of what is to be tested and what strategy is to be followed.
Prioritize the test cases: Prioritize the test cases based on difficulty levels and write test cases early in the requirement analysis and design phase.
Write reusable test cases: Test cases take a lot of time and expertise to work on. So having reusable test cases is beneficial to save time. And, to create reusable test cases, plain language should be preferred as it makes the process quick and tests are easy to execute.
Test early and test often: Early testing helps in the early identification and fixing of defects in the initial phases of SDLC. Also, the software should be tested often with the help of the regression testing method to ensure that any recent code changes do not affect the existing functionality of the software.
Automate wherever possible: Automation helps teams to save time and money, however, it should be noted that 100% automation is a myth. Thus, testers should segregate the test cases that can be automated and that need manual testing to ensure a smooth testing process.
Centralize data storage system: By centralizing the data store system, data will be available to all team members and can be referred by anyone at any point in time. It will eliminate the chances of repeated data, report, or errors that occur due to a decentralized data system
Create a traceability matrix: To capture all requirements provided by the client, it is necessary to create a traceability matrix and it maps the user requirements with the test cases. Providing coverage of all functionalities when conducting software testing is beneficial.
Conclusion: A fully functional product that works as expected and delivers seamless UX to customers is needed by every business. To achieve such a product functional testing plays an important role. It works as an integral part of a software testing and quality assurance process and tests the end-to-end functionality of the software. Leverage functional testing from a next-gen QA and software testing services provider to get high-quality software.
About the author: I am a technical content writer and I write articles on various IT-related topics. In this article, I have tried to share my views on the best practices of functional testing.
According to a study done by BCG research, around 70% of digital transformations do not meet the actual goals, which in turn leads to untoward consequences. To avoid these kinds of pitfalls, organizations must have a robust IT infrastructure, a clearly defined strategy and a skilled team who not only have good knowledge in digital technologies but also knows how to implement those technologies in a real-time scenario for optimal results.
What is digital transformation?
It creates new or modifies existing culture, customer experiences and business processes by using digital technologies. This process is conducted to meet the changing business needs and market requirements. It integrates digital technology into specific areas of a business to transform the way value is delivered to customers. It pushes organizations to experiment more and come out with innovative solutions regardless of how many failures the organization goes through. Analysing customer needs and implementing specific digital technologies are the two core factors that need to be focused upon.
Elements of digital transformation:
1. Develop clearly defined goals: In many instances, it has been found that lack of goal-setting and planning the digital transformation activities has been the cause of failure. Certain organizations just go for those technologies that are popular and trendy without even knowing whether it is required to meet their business objectives. Hence, goals need to be meticulously prepared that covers processes, technologies and tasks for accomplishing goals.
Some of the key questions to ponder upon are:
2. Implement a workable strategy: An organization’s IT infrastructure needs to be focused upon. How well are the systems integrated and what is their level of efficiency needs to be scrutinized. The data and processes that are integrated across departments and multiple systems while breaking through information silos is what actually brings fruition.
3. Top-level management is accountable: To carry out digital transformation strategies seamlessly, a top-level management team, especially CMOs and CIOs are required. If due to any non-availability, at least their respective departments can come up with some meaningful solutions. To leverage marketing activities and enhance customer experience, the CMO and CIO must work unitedly to come up with effective and pragmatic ideas.
The CMO and marketing team must collaborate with CIO and IT team in order to ensure that the security and operational aspects of marketing’s digital systems are optimized.
4. A flexible infrastructure needs to be constructed: Another key element is to maintain flexibility. To increase flexibility, legacy, on-premises systems can be transformed into cloud-based, microservices-based systems. Organizations can achieve greater scalability, agility and location-independence when on-premises monolithic applications are refactored into networks of microservices that are cloud-friendly. Through a microservices-based model, different features and services can be added, removed and updated in a faster, easier and affordable manner.
5. Hire skilled professionals: Hiring the right IT professionals goes a long way in making the digital transformation strategies a success. The IT staff must understand that to meet specific objectives, they have to be skilled in certain technologies. Some of the technologies where the need to hire IT professionals are listed below:
Conclusion: The above-mentioned elements will help an organization embrace and tactically implement digital technologies to chart out a new roadmap for progressive development. If you are looking for in-depth advice on how digital transformation can help your business grow through some definitive strategies, then just go for a company that can truly justify your requirements.
About the author: I am a technical content writer focused on writing technology specific articles. Software testing is one of the areas in which I’m really interested.
As enterprises continue to work in a fast-paced environment, the need for high-quality and stable products that deliver a seamless experience to the customer is also rising. To achieve this goal enterprise adopts various software testing practices. Among the various kind of software testing methods that enterprises adopt, functional testing is one most important testing methods. This test checks the main thing in software i.e. its functionality which needs to as expected for its success. Let us try to understand some of the best practices of this testing method in detail.
What is functional testing?
It is a black box software testing method that validates the functionality of the software. The main purpose of this test is to test each functionality of the application by providing certain inputs and validating the outputs against the functional requirements. It is also known as specification-based testing as it is completely based on program specifications. This test helps teams to ensure that the software meets the functional and business requirements and works as expected.
Why is functional testing required?
What are the different types of functional testing?
Below mentioned are the different types of functional tests:
What are the best practices of functional testing?
Below mentioned are some of the best practices of this testing method:
Conclusion: Every enterprise needs fully functional and high-quality software to uplift their customer’s experience. This is where functional testing plays its role. It is an integral part of a software testing and quality assurance process. This testing method helps businesses in delivering quality software that assures satisfying user experiences. Leverage functional testing from a next-gen, QA, and software testing company to get high-quality products that are free from all functional defects and that also perfectly meets customer requirements and business objectives.
About the author: I am a technical content writer and I write articles on various IT-related topics. In this article, I have tried to share my views on best practices for functional testing.
Today, every enterprise wants faster and continuous releases. Therefore, businesses are shifting from the waterfall approach to the DevOps approach. In DevOps methodology, continuous delivery plays an important role, but sometimes people use the terms DevOps and continuous delivery interchangeably. However, there is a clear distinction between these two terms, and at the same time, these two terms share common goals and background. Let us now try to understand these two terms in more detail and let’s see how these terms are related to each other.
What is DevOps?
DevOps is an evolution from agile methodology and is a software development approach that aims to break traditional silos that existed between cross-functional teams. The main aim of this methodology is to bring developers, IT operations, testers, and QA teams together. It is more like culture rather than a philosophy that fosters collaboration and communication between cross-functional teams. The main of this methodology is to automate the process of software delivery by enabling CI/CD pipelines. DevOps lifecycle has the following phases – continuous development, continuous integration, continuous testing, continuous feedback, continuous monitoring, and continuous deployment/delivery.
What is continuous delivery?
Continuous delivery is an essential phase of the DevOps lifecycle. In this phase, teams ensure that after doing iterations in the software, the software can be delivered at any point of time. With this approach, teams ensure that every change in the system can be released or any version of the software can be released without any delay. With the help of continuous delivery, teams can deliver the product frequently, with less risk, and can achieve feedbacks from end-users constantly and continuously. This approach promotes the use of an automated deployment pipeline or CI/CD pipeline by which businesses can release reliable software quickly. The main aim of the continuous delivery phase is to speed up the time-to-market and to eliminate risks in the traditional software delivery process.
What is the relationship between DevOps and Continuous Delivery?
The primary relation between DevOps and continuous delivery is that DevOps supports continuous delivery of the value chain. This is achieved by automating the CI/CD pipeline. Both of these approaches share common traits i.e. they follow aimed at agile and lean principles. Each of them works on iterations where small and quick changes are delivered; cross-functional teams collaborate and both of them work towards a common goal i.e. faster time to market.
Automation plays a major role in DevOps and continuous delivery. By automating the DevOps process businesses can manage continuous delivery of software to end-users. The end-result of automating the DevOps process and CI/CD pipeline is faster delivery of high-quality software.
To achieve faster and continuous delivery of high-quality software to end-users, businesses are rapidly adopting the DevOps methodology. Not only this methodology helps them to achieve faster time-to-market but also helps them to achieve improved collaboration between cross-functional teams such as developers, operations, testers, and QA teams. Leverage DevOps consulting from a next-gen QA and software testing services provider to achieve strategic DevOps implementation and faster time-to-market.
About the author: I am a technical content writer and I write articles on various IT-related topics. In this article, I have tried to share my views on what is DevOps and the relationship between DevOps and continuous delivery.
Whenever there is a discussion about automation testing, the one tool that automatically comes to testers mind is selenium. The reason is simple, it is a free, open-source, fast, and reliable tool for automating tests for web applications. But just like other tools, there are certain challenges associated with this tool also, that makes automation testing quite challenging. Let’s try to understand some of these challenges in detail.
What is selenium automation testing?
Selenium automation testing is a software testing method where the selenium framework is used to automate the test. Selenium is an open-source test automation tool that enables developers to automate tests for web applications. Selenium is not just a single tool rather it is a suite of software that contains several components containing selenium IDE, selenium grid, and selenium WebDriver.
Selenium’s latest version is Selenium 4 Alpha 5. As per the latest version of this tool, several changes have been made to the components of the suite. Selenium WebDriver is now Selenium W3C (World Wide Web Consortium) standardization. Selenium Grid is now much improved. Also now Selenium IDE is updated to New Selenium 4 IDE (Chrome and Firefox).
What are the top challenges faced in selenium automation?
Let’s take a look at some of the major challenges of this framework:
Inability to handle pop windows and alerts
One of the challenges of this framework is its inability to record pop-ups which makes it difficult for developers to automate the process. Also, this framework is unable to handle windows based alerts. This framework can handle a web-based alert with the help of the switch to method.
Provides false positives and false negatives
Another common challenge with this tool is the problem of flaky tests. A flaky test is a situation where the tool provides false positive and false negative reports. When you are running hundreds or thousands of tests with the help of this tool, some chances are there that test results can come out successful, if it is not successful (false positive), or test result can come out negative even when it is not negative (false negative). This results in flaky tests and poses a great challenge in software testing.
Inability to switch browser windows
Another challenge with this framework is that it creates problem while multi-tab testing or cross-browser tests. Many developers believe that some elements of the web app work in one browser but not in other browsers.
Difficulty in handling dynamic content
Websites and web apps have dynamic web elements, which are not visible on the site when you visit it for the first time. Usually, the content of the web page varies from one visitor to another. This poses a great challenge to automation testing. But with the help of explicit wait command, this issue can be handled easily.
Inability to test mobile apps
One of the drawbacks of this tool is that it tests on web apps and websites. You cannot use this tool to automate tests for mobile apps. This makes the scope of this tool very limited. However one can use the Appium framework to test the content on the native mobile OS.
Automation testing tools have helped testers all across the globe to save extended man-hours, reduce efforts required to test monotonous tasks, and eliminate human prone errors. One of the standard tools that come to every tester’s mind whenever we talk about automation testing is selenium. But some challenges are faced by testers while using this tool for test automation. Leverage automation testing from a next-gen QA and software testing services provider to achieve high-quality software.
About the author: I am a technical content writer and I write articles on various IT related topics. In this article, I have tried to share my views on the top challenges faced in selenium automation.
Regression testing has now become an essential part of the software testing life cycle as it helps in checking that the existing functionality of the software works as expected whenever the change is made to the software. Also with the advent of automation testing tools, it has become more easy and convenient for testers to run regression tests. But like every testing method or approach regression test automation also have some limitations which one needs to look upon before automating the regression test.
What is regression testing?
Regression testing is a type of software testing that validates that any new code change is the software that does not affect the existing functionality of the software. In Regression testing already existing test cases are fully or partially executed to ensure that existing functionalities of the software are not affected and they work as expected even after frequent changes.
Regression testing sometimes becomes tedious as testing the whole system again and again consumes time and fixing the defects requires efforts. Therefore testers use automation regression testing tools where pre-written test scripts are executed on code change with no human intervention to ensure that existing functionalities are working fine.
What are the limits of regression test automation?
As every testing method has its own pros and cons and similarly automated regression test also has some cons or we can say limits which makes it difficult for some organizations to automated their regression tests.
A false sense of quality – Automation regression test provides a false sense of quality as the automated test scripts will only look for those bugs for which it is programmed and hence some defects may be overlooked or remain unseen.
Lack of accuracy – Automation regression test can sometimes fail due to various factors such as bugs or error while running long test scripts which can give a false sense of accuracy.
Low chances of finding new defects – Automation regression test follows the same pre-defined path, sometimes with the same set of test data. Hence the chances of finding new defects are low as no new path is followed while testing.
Test cases require maintenance – The more test cases we automate the more test cases we require to maintain which further consumes more time and effort. Also if test cases are written too loosely they get passed in an automation test and if the test cases are written too rigidly they either fail and require to be updated every time a change is made which further requires more maintenance time and effort.
Time consumed in deciding what to automate- Regression tests are run in reduced time cycles, often and repetitively to identify that new change in the software does not affect the existing functionality of the software. But it is very difficult and time-consuming to identify which test cases to automate and which test case to execute manually.
Selecting the right automation regression test tool is not easy: Whenever we talk of automation regression testing tool the first tool that comes to our mind is the record and playback tool, but record and playback tools typically do not work well in automated regression tests as it involves a lot of rework. Also, there are various automation regression testing tools available but not every tool is best for every organization. Therefore many organizations find it difficult to identify right tool that caters to their requirements.
Automated regression test has helped testers worldwide to save considerable testing efforts and time involved in running a manual regression test. Also in today’s agile environment, frequent changes are made in software which makes it more necessary to run a regression test every time a change is made to the software. Here automation regression testing tools play a major role in saving testing time, effort, and speeding up the testing process.
Still, automated regression tests has some limitations such as a huge requirement for test case maintenance, lack of accuracy and reliability, the problem in finding the right testing tool, and several other that have been mentioned in this article that makes it difficult for organizations to fully rely on automation regression testing.
Leverage end-to-end regression testing services by an independent next-gen software testing services provider to ensure that your software remains defect-free even after frequent changes.
About the author:
I am a technical content writer and I write articles on various IT related topics. In this article, I have tried to share my views on the limits of regression test automation.