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Is AI & Automation Testing a threat to our jobs?

Is AI & Automation Testing a threat to our jobs?

AI & Automation Testing

The evolution of Artificial Intelligence (AI) and Automation Testing could wipe out approximately 73 million jobs in the US by 2030. The Indian market may also face the inevitable threat of losing nearly 120 million jobs due to automation by 2030.

Almost 44% of workers are vulnerable to lose their jobs by mid 2030s.

Nearly 30% of the jobs are prone to get affected by the Automation thrust by mid 2030s.

These are alarming figures and so is the situation, which is constantly changing with the intervention of AI & Automation. It is a well-known fact now that Artificial intelligence (AI), robotics and other forms of ‘smart automation’ are advancing at a rapid pace and somewhere along the line they do pose a threat to the current jobs across the globe. It is predicted that automation will lead to mass unemployment by the 2030s and it will be the biggest since the inception of the digital revolution. But, on the contrary it is also true that they have the potential to offer great benefits to the digital economy – by heavily boosting the rate of production and create better products and services.

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However, AI based automation testing will definitely disrupt the markets and it would be interesting to see the outcomes around new opportunities that get generated. With these continuous advancements in robotics, machine learning (ML) and artificial intelligence (AI), the new age of automation is getting redefined. Machines have now started to match or even outperform their human counterparts in a wide range of activities including the ones that earlier required cognitive capabilities.

Understanding the impact of AI in Software Testing

Performance Testing

Complex applications require a seasoned approach to Performance testing and monitoring. A typical testing scenario starts with the analysing the application’s interface and creating customized frameworks and test scripts. This is followed by the users hitting the application through the load testing scripts which reveals the response time, memory utilization, CPU utilization time and more. Artificial Intelligence in this case acts like the brain of the project, thereby possessing overall control which includes regular test designing, scripting and implementation. This gives the testing engineer enough scope to focus on the creative side of performance testing such as dashboard design and more.

One case scenario where AI can directly be incorporated with performance testing is code less automation script because writing performance test scripts with natural language processing (NLP) can make the task much easier. In this AI-ML based performance testing form, the computers learn the data without programming, which saves a remarkable amount of time. The test environment thus developed has advanced capabilities such as self-heal and intuitive dash-boarding. With the usage of deep learning algorithms handling corrections is completely automatic.. The recorded test scenarios can be tested using the available data where almost no coding is required and thus the solutions implemented are transparent to the user.

Simple case monitoring tools have integrated AI with their systems, which is now helping identify loopholes in the application strata during the early stages of SDLC. It keeps a keen check and thoroughly analyses the application to predict the issues in performance right at the code level. Artificial Intelligence has been playing a crucial role in performance testing services and has helped in tedious tasks like scripting and monitoring apart from getting real time results quickly. It has proved to be a boon.

Automation Testing

Pizza or burger? Sedan or SUV? This is just a matter of choice for humans. The case is pretty much the same even in our workplace when it comes to testing and automating the processes. We have already chosen artificial intelligence where machine learning and automation are changing our lives to create value. The future depends upon the blend of human ability and machine capability. With the acceleration of technology and a little human input,AI is all set to help testers re-shape the testing process with higher efficiencies and accuracy.

AI in automation simply means intelligent automation which performs faster and more precisely when trained to detect the issues, its causes and solutions. AI integrated with automation testing services is capable of accessing the data, run tests, spot an issue and detect other defected tests by improving its quality over time. AI is trained to make automatic decisions and manage huge databases with accuracy and negligible scope of error. Even the healing process is automatic!

How is AI favouring Automation Testing?

Higher Accuracy – To err is human. The most experienced of us are prone to mistakes, varying under circumstances and with repetitive tasks. This was one of the topmost reasons why automation testing services gained prominence in the software testing industry. However, automation is still only as smart as we design it to be. AI on the contrary performs the task exactly as intended, even in repeated case scenarios with no exceptions and also stretching itself to learn and unlearn as trained. While AI performs, automation engineers can easily invest in other automation solutions which can be otherwise performed by human counterparts only.

  1. Greater Flexibility – Test failures can happen even due to the slightest changes in the application code and can create a negative impact due to traditional testing scenarios which are, at times, rigid. Flexible testing process is one factor which is highly enjoyed by AI – ML. The systems based on these technologies are made to adapt in real time by being reliable and flexible.
  2. Validating Visually – AI has the ability to visually recognize and validate a pattern or image for potential bug detection while performing visual testing of the application. It seamlessly ensures proper functioning of the elements. Irrespective of shape and size, AI can detect UI controls, by analysing them at pixel level. Though AI is not fully capable of handling a test on its own, without its human counterparts, it can still speed up the testing process by reducing the time and increasing the accuracy.

  3. Highly Cost Effective – The integration of AI into the automation testing process greatly reduces the cost of manual efforts done by human resources and effectively manages repetitive tasks for continuous delivery and increased productivity.

By greatly extending the scope of automation testing services Artificial Intelligence over-the-time has also enabled non-technical team members to contribute in test cases. This has helped greatly in streamlining creations, executions, maintenance and reducing the delivery turnaround time.

Security Testing

The projected size of the global cybersecurity market is expected to rise to USD$ 248.26 Bn by 2023. This shows a huge scope for Artificial Intelligence on the digital security platform. It is undeniably the most important global concern of the 21st century, and the way we navigate the security implications could definitely reshape the digital future we are exposed to. To elaborate, it is tougher to secure an app that has modern complex software architecture, is adaptable and is data intensive.

Security Testing a.k.a Penetration testing (or pentesting) is a vital component when it comes to cybersecurity. AI significantly helps in making pentesting easier and to be executed at a continuously. This helps digital organizations cater to their issues related to skills and culture apart from efficiently taking care of their cybersecurity strategies.

The Impact of Artificial Intelligence in Security Testing

AI powered security testing tools allow software developers to execute smart hacking through white hat attacks and locate the loopholes in web and mobile apps. The most effective ones are an amalgam of threat intelligence, vulnerability scanning, and human expertise which validates the severity of the threats through simulated attacks. AI – ML is now being greatly deployed by security professionals to increase pentest efforts.

Human counterparts have always played a crucial role in pentesting, but the experts of the 21st century are looking for new methods in which the AI-ML integration can contribute to a larger extent. The AI-ML systems must always be trained for the environments in which they are being used, which can therefore only be done by human testers. Also, logical reasoning and judgement and common sense are some factors which can only be imbibed by humans and thus makes them efficient enough to detect a threat that could cripple the production system. AI is capable of smart learning from the algorithms that humans design, but that cognitive piece still has to come in from humans.

Must Read: How Can AI Enable Software Testing

Is AI really the Job Killer?

By 2020, AI will create 2.3 million more jobs while eliminating 1.8 million. The effective use of AI by QA testers would transform the entire testing process, will enhance the skills of the testers and would contribute towards a much higher ROI.

The AI based app testing services of the future is projected to be promising with the entire process becoming more efficient. It will effectively take a huge amount of the tester load, while QA testers will have bandwidth to acquire new skills because working in-sync with AI would require diversified competencies such as logical reasoning, mathematical optimization, algorithmic analysis and business intelligence. This will ensure higher investments by digital businesses on their QA teams for their skill development.

It is being predicted that Artificial Intelligence will replace tasks, not jobs. The QA engineers and software testers will eventually transform into the test automation team who will move ahead with a supervisory role wherein they will teach AI. It is believed that in the software testing industry AI will occupy approximately 70% of repetitive test space where the remaining 30% will be controlled by human intelligence which would therefore comprise of user scenario tests, tooling, workflow modeling and the setting up of environment.

AI will double up as a smart assistant where the human counterpart will monitor the progress, design the test plan and would take over the QA strategy.

Our intelligence is what makes us human, and AI is an extension of that quality.”

-Yann LeCun

French-American Computer Scientist

Vice President, Chief AI Scientist at Facebook.

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Established in 2003, with less than five testing experts, QA InfoTech has grown leaps and bounds with three QA Centers of Excellence globally; two of which are located in the hub of IT activity in India, Noida, and the other, our affiliate QA InfoTech Inc Michigan USA. In 2010 and 2011, QA InfoTech has been ranked in the top 100 places to work for in India.