Artificial Intelligence (AI) and Machine Learning (ML) have come a long way in recent years. Today, most of us are using AI and ML-based applications in our everyday life both consciously and unconsciously.
Over the coming years, you can expect smart technologies to have an increasing impact on how work is done. When it comes to software development, we are already starting to see the impact of AI and ML within the industry.
According to the World Quality Report 2018-19, end-user satisfaction is now the primary objective of testing-strategy goals. So to improve and accelerate Quality Assurance (QA) processes, you can expect the introduction of machine-based intelligence in software testings.
QA test automation promises to make software testing much better and faster. This is because AI and ML-powered algorithms can write their own code and can be leveraged for test execution and maintenance. This process will also make regression testing faster, smarter, and better.
The inclusion of smart technologies within the software development space will allow QA testers to concentrate on more critical tasks and leave the boring stuff to smart algorithms.
1. AL & ML Will Accelerate Manual Testing Processes
When you’re dealing with thousands or even millions of lines of code, testing one line after another will come with a flood of disadvantages. It can confuse the testers and take several days, weeks, or months to get through.
Manual software testing is time and resource intensive. Even if you hire the best in the business, you just can’t expect your testing team to get through it rapidly.
AI will help resolve this by integrating itself during the development process. It can write scripts, analyze large data sets, and accelerate the whole testing cycle.
This is because smart algorithms can sort through log files quickly and make sure that the code is correct (based on the parameters set by humans). When you follow this approach to software development, the QA engineers will have a comprehensive view of the changes that are needed to be made right from the beginning.
2. AL & ML Will Make Testing Inexpensive
The more you have to test the code, the more time intensive and expensive it will become. Whenever the code has been updated, AI-bots will also evolve with the changes. This will help them identify defects in the new releases much faster.
However, sometimes, interference by AI might demand human intervention to perform clean up and maintenance tasks.
3. AI & ML Will Help Eliminate Bugs in the Code
The whole purpose of software testing is to eliminate bugs in the system. But even the best QA teams in the business sometimes miss bugs.
Whenever an issue pops up, the tester will be tasked with analyzing the causals relationships of the problem. This can take quite some time for a software tester to do, but AI can ask the relevant questions and find the right answers within seconds.
So testers can leverage AI to engage in this activity and then decide how to resolve the error. In this scenario, AI can enhance QA teams to make them more effective.
Another problem that is rampant in software testing is the fact that (understaffed) businesses often choose to ignore bugs just to get the product to market. But this is a terrible idea that tends to come back to haunt you once the product has been released.
Whenever bugs are left in the system, it can have a significant impact on the brand image and your bottom line. Yet, it’s quite common within the software development industry.
However, with AI and ML, we now have the opportunity to make this a thing of the past. An ML approach can provide more reliable outcomes than traditional testing. It can also do it much faster than the best software testing team on the planet.
Smart technologies will also help expand and improve the overall length and scope of testing. All these elements combined will help produce quality software, quickly.
Does This Spell the End of Software Testing Teams?
No, not quite!
Software testers don’t have to fear AI because technology isn’t going to replace them. Instead, they will be tasked with finding practical ways of deploying smart technologies to identify bugs faster and achieve better results.
Even if companies wanted to replace entire software testing teams with AI-based applications, they can’t. While smart applications can deliver precise results, they lack documentation, management, performance, scalability, and security.
This also brings up another problem. While AI might know how to interact with the system, how will it know the difference between the right and wrong behaviors within the System Under Test (SUT)? This is what’s now known as “The Oracle Problem.”
Humans can do this by finding the source of the truth. This means that, at least for the time being, it can only be done by manual testing teams.
Having said that, we have to accept that over time, QA testing roles will have to evolve to accommodate AI.
According to the World Quality Report, we will start seeing the following new roles within the software testing space:
AI QA Strategist: This job will be concentrated on how teams apply AI to business. This means that they will need to have both technical and business acumen to drive testing teams forward.
AI Test Expert: This role will require traditional testing expertise and experience with machine-learning algorithms and natural-language processing techniques.
Data Scientist: This role be will be focused on sifting through the data and building models by applying predictive analytics, mathematics, and statistics. So candidates will have to possess a deep understanding of data analytics and software testing.
These new roles are expected to start popping up as soon as AI cements its place in software testing. But the reality on the ground is the fact that it’s just not easy to find professionals with these skillsets today. As a result, the adoption of AI within software testing will be slow.