Artificial Intelligence in Software Testing | Benefits and its Trends

Artificial Intelligence in Software Testing
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Big Data Testing

With the explosive increase in data generated by more users and devices, organisations need to store this data so that it is easily accessible for further analysis, for example, to improve business strategies and marketing tactics.

When making critical business decisions based on analytics, it is imperative to be confident that such data accurately represents user behaviour. Therefore, handling such a huge amount of data requires end-to-end testing to avoid failures .

Mobile apps generate multiple pieces of data through logins, location tracking, and social media integration. Testers can use performance testing tools to verify the performance of these applications under different load conditions.

The future of RPA may include the integration of chatbots and natural language processing (NLP) into virtual assistants, which could help testers complete basic tasks like creating test cases.

Improving skills and the rise of the SDET role

To fill the need to develop new skills and to fill the remaining quality gap by extending the skills to more members of the software development and testing team, a new trend that is growing rapidly is Software Development Engineer in Testing , also known as SDET (Software Development Engineer in Test), for its acronym in English.

The World Quality Report strongly recommends that testing industry professionals invest in knowledge that spans the entire testing and quality assurance process : from short-term tactical initiatives to long-term planning and strategic direction.

Early detection of incidents

Today, to ensure that the budget increase is practical, it is essential to involve the QA team early in the software development life cycle (SDLC). Thus, the sooner incidents are detected, the lower the cost to correct them and the lower the total cost of quality .

The key to implementing shift left testing is to measure the effectiveness of a change and determine if it meets the expectations of the end user. Another alternative is to have QA teams write unit test cases for development teams to test.

API and Service Test Automation

The separation of the client and the server is a current trend in the design of web and mobile applications. The API and services are reused in more than one application or component. These changes, in turn, require teams to test the API and services regardless of the application that uses them.

When the API and services are used in client applications and components, testing them is more effective and efficient than testing with the client. 

Having the right process, tool, and solution for API test automation is more important than ever. Therefore, experts advise training in the best API Testing tools .

Codeless Automation

With a greater awareness of the relevance of the IT industry, companies today seek to obtain maximum performance in a short time. This opens the door to no-code test automation for software testers . These tools are built with artificial intelligence and make it easy to run test cases quickly. This not only saves time, but also keeps resources free from handling other tasks.


As traditional QA teams expand to meet the growing demand for testing services, they must be more aligned with the development process to speed delivery times. This requires the coordination of automated and manual tests at scale. The most optimal way to do it is by implementing QAOps.

With more automation, testers can focus on more complex tasks instead of just doing routine activities. Also, it allows them to improve their skills and, consequently, do their job in the best possible way.


In the testing industry, the cybersecurity aspect has been gaining ground for both QA and testers. For its part, with the help of Risk Compliance, security is improved prior to the start of the software development cycle.

The biggest benefit is that it helps to understand gaps that can be addressed immediately, before cybercriminals take action. This is one of the main reasons why it plays such a crucial role in today’s world and it will become even more important in the near future.

IAC – Infrastructure as Code

In a heightened period of digital growth and with leading IT industries moving to the cloud to maintain flexibility and cost, virtualization has made dramatic changes to servers and the way they are used.

Thus, the general concept of IaC (Infrastructure as Code) is to manage the operations environment effectively.


Due to the pandemic, companies began to adopt chatbot support to provide a better user experience. Chatbots were not only helpful in resolving queries but also available 24/7 and greatly helped millions of users in different industries vital to this period, such as healthcare, e-commerce. , banking, retail, etc.

Therefore, it is likely that chatbots will not be a passing trend and will require more extensive testing in the long run.

Testing Centers of Excellence (TCoE)

Analysts and testing experts predict that the IT industry will see a progressive implementation of Testing Centers of Excellence, also known as TCoEs . Hubs that gather all the necessary resources (developers, testers, automation tools and clients) in one place where everyone can work together.

Testing is usually left to testers to run, so they need to understand how the process works and what to test. With TCoEs, they could introduce new ideas, instead of going 100% into testing, during sprints or at the end of the digital product launch. And it is that thanks to the significant progress of technologies such as AI and ML, quality engineers have a broader field of action.

The recent evolution of artificial intelligence (AI) technology, coupled with its increasing access to business and the general public, has led to further acceleration of digital transformation in various areas of our software industries. And the field of testing is no exception.

The application of AI in QA and testing has led to significant improvements in the efficiency, accuracy, and quality of testing processes . In this article, we’ll explore the lines Hiberus QA is currently following in their application. Starting initially from exposing what we understand are the advantages of using AI in QA and testing, the main uses and the most relevant tools and KPIs and exploring why we believe that the trends in software QA will be increasingly linked to AI.

Know more: QA and testing services

Advantages of using AI in QA processes

Intelligent automation 

 By using machine learning algorithms and natural language processing, AI can analyse large amounts of data and run tests faster and more accurately than traditional methods.

Early detection of defects 

AI algorithms can identify patterns and anomalies in complex data sets. This facilitates early detection of software defects during the development and test phases.

Improvement of the precision and coverage of the tests guaranteeing a higher quality of the software.

Continuous optimization of the testing process 

 At all times in the process we can use AI for continuous improvement. By using machine learning algorithms, AI can identify risk areas, prioritize testing, and optimize resource allocation.

Some uses of AI in QA and Testing

performance tests 

 AI can simulate the user load on a system and evaluate its performance under stress conditions. This makes it possible to identify bottlenecks and performance issues before the software is deployed to production.

Usability testing 

 AI can analyze user interaction data and provide valuable insight into an app’s usability. This helps QA teams identify areas for improvement and optimize the user experience .

Safety tests 

AI can detect security vulnerabilities in software through code analysis and threat modeling. This helps ensure that the software is protected against potential attacks and security breaches, providing greater confidence in the integrity and robustness of the system.

Current natural language AI tools

New tools emerge every day that have AI as their main engine, therefore, we are going to present some of the best-known applications currently in terms of natural language:


The GPT-3 generative language model, developed by OpenAI, is one of the most prominent tools in natural language processing. With 175 billion parameters, GPT-3 is known for its ability to generate coherent and contextually relevant text in response to different inputs. ChatGPT is a GPT-3 based system designed specifically for interactive conversations with users.

Azure AI 

Azure AI is an artificial intelligence platform developed by Microsoft. It includes a variety of services and tools, such as Azure Cognitive Services, that provide natural language processing capabilities. These services allow developers to integrate natural language features into applications, such as sentiment analysis, entity detection, and language generation.

Google BARD

 It was developed by Google. It is a conversational system that allows interaction through normal messages. Based on LaMDA, an experimental language model from Google, BARD has been in closed testing with limited access. Google, which has dominated the Internet search market for years, now faces competition from AI models capable of providing direct conversational responses, such as ChatGPT.

Author bio: 
Boopathi Krishnan is an executive of Marketing at Sparkout tech solutions. With 1+ years of experience in SEO and marketing, he loves talking about technology, SEO, and NFTs.