Software testing has evolved over the past few decades. In 1980and 90s, companies realised that they should have a separate QA team to find defects in the software. It is not fair to present the defect in the later stages when the software is built. It will leave the impression on developers that they built perfect software. Thus how separate QA jobs came into existence.
Earlier testing was manual testing, and at that time, companies did not have any such tools for automation, and the development methodology was waterfall methodology. In this methodology, developers worked for 6–8 months, built a product, and then forwarded it to the testing tester. The tester finds bugs at a later stage and then sends them back to the developer team to fix bugs. After fixing the bug, the dev team send it back for re-test. This process happened in the early stage of software QA testing, and some companies still follow the same procedure.
In the late 1990s and 2003, software testing service companies started looking at software tools to help testers perform testing fast and effortlessly. This is how QA automation tools came into existence. It helped manual testers perform testing on regression test cases and smoke testing. Automation helped a lot in the monotonous job of testers and helped them focus on more functional test cases and functionalities.
Many automation tools came into existence like Selenium, QTP, WinRunner, Test complete, etc. Many companies gave software testers opportunities in IT service projects and Product-based projects and hired them for QA roles at a designation of manual and automation testers.
Effect of artificial intelligence and machine learning in software testing jobs
Artificial intelligence, machine learning and robotic process automation (RPA) is emerging as widely accepted technology in the current era.
As compared to future decades, technological advancement in software testing and upcoming new technologies is taking a huge place in the software industry. Experts predict that AI and associated technologies will present across software industries with a good number of software packages and change the role of working and become a part of our daily lives by 2021. According to some experts, soon, AI and associated technology will replace manual jobs or humans from the software industry.
If we talk about QA jobs, technologies like AI and ML will replace manual software testers. Soon AI tools will take over the market and start correcting the code while developers write code. AI comes with many more innovations in testing, and this comes with improved speed and accuracy. Soon AI and ML will be more integrated into QA and automated testing. The advantage of automated testing is an excellent speed and faster bug resolution. To create a more accurate test framework, architectural experiments and research is going on.
In the manual and test automation industry, it may slow down the QA opportunities. AI will soon replace human intervention. Well, somehow, these points might look correct, but it is not practical in a few scenarios. It is not easy to remove all human intervention, but yes, in a few places, it is possible. Some experts believe there may be transitional roles for software testing professionals, which will shift to artificial intelligence, machine learning, and data science product testing engineers.
Quality analysts need to learn new skills and upskill themselves to meet the pace with constantly changing technologies. The fear in QA professionals is AI/ML will soon replace their jobs. Instead of fearing, QA professionals should learn new technologies and learn AI/ML/Data Science. It will help testers become professionals, and in future, they can test using these applications. In the future, the job of QA will need the knowledge to perform testing using the latest technologies. Experts believe AI will make testing easier, which will prove good for QA professionals.
The role of AI in software testing
The agile methodologies in software development have brought the rise of test automation. It enables the team to deliver robust and bug-free software. In the case of manual testing, it is limited to business acceptance testing only. Test automation and DevOps help the agile team deliver fail-safe error-free SaaS/cloud deployment through CI/CD pipeline. In software testing, AI plays the role of cognitive automation, reasoning, machine learning, natural language processing and analytics. Cognitive automation leveraged various technological approaches like text analytics, semantic technology, data mining, natural language processing and machine learning. The best example is RPA (Robotic Process Automation), which connects AI and Cognitive Computing.
How AI has changed the traditional way of testing
- Automation visual validation
- Automatically writing test cases
- Reduced AI-based testing
- Improving reliability
Testing based on AI reduces overall testing cost, time, error and scripting. Isn’t it what a company wishes for? There is no doubt that AI and ML are game-changers in the software industry and will become a trend soon in the market. It’s high time for the software testing team to move towards an AI-based testing and management approach. If you have any queries and want to share your automated testing experience with AI, please contact us or share your experience in the comment section below.