The Future Role of a QA Tester When AI and Automation Takes Over

Artificial intelligence and machine automation has come a long way since its inception and is growing and evolving at an exponential pace.

Its multifaceted application in various fields, ranging from finance to trade securities and more, has enabled machine learning to improve software-testing processes by making them faster, easier, and efficient.

The rapid evolution of AI and automation has left some professionals speculating about the future role of QA testers and the impact of their role in the automation era.

Although machine learning plays an invaluable role in the ongoing evolution of software testing, both humans and machines will remain invaluable when it comes to facing challenges and optimizing processes to save both money and time.

By working in collaboration with machines, QA professionals can upgrade testing procedures, boost their professional skills, and guarantee business growth.

However, the key to achieving these goals is to assign the most logical roles to a machine that it excels in and to leave difficult testing procedures to professional testers.

What Will Artificial Intelligence Bring To The Table?

Here are the benefits AI renders in the field of software testing:

Minimizing Inaccuracies

Both humans and machines have their weaknesses and strengths. Humans excel in exploration, creativity, and applying knowledge. On the other hand, machines are great at accurately analyzing huge amounts of data in a timely fashion, which helps to keep track of performance over time. However, traditional testing still requires humans to source and analyze data themselves.

Large volumes of data can be difficult to work through and can lead to inaccuracies caused by a lack of focus. This can then lead to software defects which can derail projects because they are usually only picked up in the later stages of production.

It also has further rippling effects when it comes to product positioning in the market, general trustworthiness of the application, and its overall brand reputation.

In this regard, artificial intelligence and machine learning allow for more accurate results by teaching systems to learn source analysis, applying learned knowledge and alerting you if it picks up any deviations over time.

By minimizing the probability of inaccuracies through eliminating human error, companies can save time when it comes to the testing process, which also frees up software testers to focus more on technical aspects of testing that machines won’t be able to master.

Diversifying The Roles of Software Testers

With Artificial Intelligence continually evolving and maturing, it is easier to predict machines will take over most of the test execution. In doing so, companies will be able to implement efficient testing methodologies, however, this also calls for the development of new skills for QA engineers.

To continually better the working relationship between man and machine, software testers will need to outsmart machines in areas that will drive more growth.

By increasing competency in AI testing, math optimization, algorithmic analysis, business intelligence, and neuro-linguistic programming, future software testers will have the skill set they need to step into the new roles required to continually better this area of software development.

Some of these new roles will require software testers to build machine learning algorithms, understand data flow and math models, as well as to conduct predictive analysis to form new AI strategies.

It is predicted that software testers will have to take on a new supervisory role to teach AI to execute tests, analyze certain information, and work through huge amounts of data in record time.

Although there is still a long way to go, it is believed that AI will be taking over nearly 70% of repetitive testing space. Software testers will focus on the remaining 30% by monitoring the progress of AI, modeling workflow, and devising new test plans and QA strategies.

Improve Customer Data Analysis

Another thing that artificial intelligence will bring to the table is its ability to boost sales and enhance the competitiveness of business by improving customer insight. Data from the World Quality Report predicts that 64% of businesses will use AI to better testing strategies to improve customer processes.

Since businesses need to stay ahead of the curve by better understanding and predicting the needs and behavior of their customers, there will always be a demand for bettering technology in this regard.

However, manually, this is no easy feat. Artificial intelligence allows for an increase in the accuracy of predictive analytics, which may help companies to gain clarity on their customer’s preferences.

Future Challenges

Although artificial intelligence will be paramount in the evolution of QA, there is still a long way to go in achieving these ambitious long and short-term goals.

One can already see in what ways AI will be able to quicken and perfect the process of software testing but to fully adopt AI to achieve those goals some of the pressing obstacles standing in the path of implementation will first need to be overcome.

Processing Data

To process huge clumps of unstructured data, companies will still need the aid of human resources and additional financial support. Due to unstructured data and the fact that each batch may be presented differently, machines will have trouble analyzing and extracting meaningful information because of the changes in format.

For companies to implement a heavy data analysis, the investment in structuring data in a conforming way that is fit for AI consumption has to make up for the incurring costs for it to be truly beneficial. However, this is rarely the case, which means that those companies are not able to proceed with the use of artificial intelligence and robotics.

Identifying Key Areas In Need Of AI Integration

Since AI integration is still a new development, and new roles that bridge the gap between numerous business requirements and different AI functions still need to be formed and filled, there is currently a lack of expertise with regards to integrating AI to truly maximize profits.

The real conundrum lies in identifying key business areas that require AI technology to improve on its profitability and save on expensive resources.

More often than not, individuals who excel in business do not possess adequate levels of AI expertise to know how and where it can be implemented. On the other hand, AI professionals will also not be able to understand every company’s unique needs and where AI can improve on performance.

AI Testing Expertise

As we broaden our scope on intelligent testing, the skills of software testers will continue to improve.

Over time, more fields of expertise will emerge and roles within those fields will be filled. However, the current demand for qualified software AI testers will continue to climb as more industries experiment with intelligent testing which means that the current market is running on short supply.

Until the level of AI expertise needed to effortlessly grow businesses is readily available to all companies, growth in AI application will be limited.

Future Roles of QA Testers

As established, AI will not be necessarily taking over the entire job spectrum of a software tester. However, to stay ahead of the curve, testing will become more intricate and complex as testers integrate AI into the QA testing sphere.

Software development has always enabled the discipline of testing to attain a fixed or determined result. For instance, computers can only provide us with results that we determine as right or wrong. However, as we incorporate machine learning into software testing, this will change. This is because machine learning allows for non-deterministic results in more intricate problems.

As a result, testers will have to determine new ways of exposing defects in application results. This will require testers to focus more on the unique task at hand rather than on the execution of the test itself, which means that the role of testers will have to evolve to stay relevant.

Understanding how to train and utilize mathematical models to effectively analyze the results of algorithms to make sure that they mimic the thought processes of testers with regards to risk assessment will be crucial.

Conclusion

Although AI has come a long way and has lots of potential in terms of accuracy, data analysis, and furthering business prospects on every level, the integration of this rapidly growing technology with software testing is still far from perfect. In the long run, the merging of AI with automation will require QA testers to fill and excel in new roles required to continually evolve software testing and establish the best ways to integrate AI.