What are the latest Quality Assurance tools and methods for software development?

One of the most difficult task an automation engineer has is to teach the manual testers how to use the automation. So at the end of the day the automation tool that is selected is not as important as how easy it is to read the report and understand the bugs that the automation found

At the company where I kickstarted my QA career, the team would always focus on manual tests and we did not have any automation suite. Being an overall massive project and working in quick, agile sprints, the QA team quickly found itself under pressure with having to complete regression and feature test cases. These test cases would often be in the low four digits, to be covered by a team of 4 QA engineers. Eventually, we decided automation and in this case, Cypress was a necessity in order to streamline workflow and reduce load. It was a hassle to set up at the start since I had no experience. Eventually, the pros quickly presented themselves with quick test runs, immediate results, and detail-oriented reports.

in my experience, I think one of the most important things to think of is “Software validation tools”. Software validation tools are essential to ensure that the software will perform its intended functions accurately, safely, and consistently. There are various software validation tools available such as: -Requirements Management Tools -Static Analysis Tools -Dynamic Analysis Tools -Automated Testing Tools -Code Review Tools -Model-Based Testing Tools -Compliance and Certification Tools -Simulation and Emulation Tools -Security Scanning Tools The choice of a software validation tool depends on the project's requirements, the type of software being developed, and the industry standards and regulations that need to be adhered to.

I would add Eggplant from Keysight to the list of automation tools. It uses image capture and OCR for testing and validation and supports multiple platforms.

Here are some names of popular Quality Assurance tools and methods for software development: 1. Selenium 2. JUnit 3. Jenkins 4. Docker 5. Agile methodology 6. DevOps 7. Shift-left testing 8. JIRA 9. LoadRunner 10. TestRail These tools and methods are widely used in the software development industry to enhance the Quality Assurance process and ensure the delivery of high-quality software products.

I think that an article like this should provide the most relevant an up-to-date info, therefore I believe you should replace these two "CircleCI, and Travis CI" with Jenkins or at least "Travice CI"

In my experience CI/CD is very important, and also quite crucial to design it's stages correctly and be support for the development and testing processes. Like not merging the code if unit tests failed, automatically deploy new version of build to the environments, trigger all necessary test runs by schedule or after deploy. Also important to keep those pipelines easy to use for all participants of testing process.

Of course, in order for us to work in a CI CD way, our tests must be stable and fail only in a real case. A test pipeline that does not pass regularly is a pipeline that cannot be trusted.

A pipeline is a must when running automation suite. It makes easy to trigger the job whenever required with few parameters modification. The job can be analysed for time range and can be optimized if required.

One of the most crucial parts of the whole QA process. Specially if you are dealing with automated solutions. Integrating your automated tests into a pipeline will surely improve your whole QA process speed.

Leveraging cloud-based testing tools has benefits, but do not forget to methodically evaluate the risks involved factoring in your organization's data protection policy and risk appetite for involving a third-party in the testing process. Things like storage of application logs, payloads and backups have to be catered for when considering the cloud.

In my experience, Cloud-based testing has several benefits: 1. We can test the software from anywhere with an internet connection. 2. We can easily scale up / down the testing resources to match our needs. 3. We don’t need to invest in expensive hardware or software; Hence we pay for what we use. 4. Cloud testing platform provides automatic backups and security measures. 5. Cloud testing platform enables teams to quickly set up testing environments without physical setup or configuration hassles.

The proliferation of cloud computing has undoubtedly improved the quality of our testing, not only because of the obvious benefits such as scalability, flexibility, and cost-effectiveness (with a caveat that environments must be sized correctly to gain the cost benefits) but also because it gives us the flexibility to create representative as well as exact replicas of the environments our apps are meant to operate in a straightforward way. Performance test engineers can also leverage cloud computing to simulate countless numbers of users. Finally, cloud computing can help in your automation execution by giving you access to compute power to run tests on the cloud.

The current low-hanging fruit is incorporating AI and ML features into Quality Engineering and associated Quality Assurance processes. This includes self-healing tests, automatic web page object generation, re-generation, etc. Looking at a 5-year horizon, in my opinion, Autonomous Testing is the nirvana where we strive to be as an organization. In this model, entire test suites do not need to be written. Instead, they are generated every time a test run is triggered, effectively making tests ephemeral and test case writing a thing of the past. There are already tools that do a good job at this, Appvance is one example. AI/ML are building blocks that will eventually get us to realize autonomous testing.

As of today: tests developed using AI are more stable for unit testing and integration testing but less so for ETE testing.

The challenge isn't just about automating tests but ensuring they evolve with the product. I've seen how traditional methods can fall short. It's refreshing to see tools like Testim and Applitools leveraging AI and ML. At Devzery, we're taking a slightly different approach, focusing on enhancing tester capabilities and making the process more adaptive. It's all about striking the right balance between technology and human insight.

One thing I've found helpful is using AI in testing approach can give more stable and accurate results covering edge cases. Ex. I have used Applitools as part of automation and integrated to existing framework. Setup was hassle free but result is amazing. Have you ever used this tool? Do give it a try :)

I have seen many people have this misconception of BDD and cucumber is same. Do you think so? No, BDD is an approach and cucumber is a tool to use this approach. Its not only necessary for test scenarios but also useful for bridge the gap between stakeholders and keep everyone on the same page. A good scenario must have a background to create action which gives desired output. BDD is easy,convinient and crucial for agile teams.

From my QA days to now leading Product Management, I've seen how crucial it is to keep the user at the heart of development. BDD nails that! At Devzery, we're mixing the best of BDD with some AI magic to make sure our software doesn't just meet user needs but grows with them.

Test Driven Development (TDD) is a methodology that emphasizes writing automated tests before developing software functionality. The process follows cycles of writing a test, running it and causing it to fail ("red" result), writing code so that the test passes ("green" result), and then refactoring the code. TDD improves code quality, acts as living documentation, facilitates refactoring, focuses on specific requirements, provides early feedback, and is an integral part of agile practices.

I believe the QA Team, is also important in detecting APP/CODE vulnerabilities. Besides developers, we end up being one of the first layers of defense. When developing test cases, it's important to always keep in mind how the information is being treated. How secure are the application connections and input fields. With small steps we can detected small breaches in development, that could lead to massive ones in productions. Preventing massive losses, of money, time, security and customer integrity.

Robotic Process Automation should be considered as a tool for QA. At times , there are a lot of repetitive tasks during the process. It requires very little knowledge of programming and can be a good introduction to automation in QA.