Employing Artificial Intelligence for Software Test Automation

Artificial intelligence (AI) is introducing automation to global business infrastructures and processes. From retail and banking to sales and marketing, AI and its underlying technologies such as machine learning are improving key operations effectively. In today’s article, we are exploring the benefits and applications of artificial intelligence for software test automation. As more enterprises are aspiring to develop advance systems, artificial intelligence services are gaining significant for infusing required computational power. 

Here’s a comprehensive guide for developers and businesses to harness AI technologies for software development and testing.                   

Benefits of Artificial Intelligence in software testing

Exploratory testing

AI has intelligent assistants which have been used as background tools to capture the test data by navigating through an application or a system.  These intelligent assistants are helpful in recording the default test cases. So, AI testing depends on the collective knowledge of all the people working in a particular team to identify various scenarios with lesser efforts.

Ability to find system errors and new patterns of failure: The AI-based analysis is best suited for analyzing logs. Data-mining logs for errors and performance, and identifying the main cause of problems have been made easier with AI. Each call can have multiple sub-calls, AI can easily track and identify the most time-consuming part. Al-based tools conduct performance analysis and ensure the security of applications or systems by identifying any potential attempts of unauthorized access to the system.

Regression Testing – visual UI testing and monitoring: It's so difficult to test all the functionalities to make sure that because of the newly added code does not affect the existing code each time. For a user backend code does not matter, the only thing which can ensure user satisfaction is the functionality of the application. Machines are more accurate and efficient than humans. Machines have the capability to perform regression testing accurately and effectively and analyze the outcomes.

Requirements gathering: The most challenging part of the process of software development and testing is that humans are not able to understand and review the requirements needed by the software completely. The intelligent assistants of AI have the capability to completely understand the requirements of complex systems and software. AI seems to have better requirements gathering ability than humans. AI also defines test requirements which are based on the latest marketing trends.

Write tests and Re-use test cases: AI-based tools help in writing tests automatically for an application by spidering. These tools collect data, capture screenshots and many more. Therefore, AI-based testing is both money and time-saving. 

AI-based automation also helps in reusing already created test cases since the AI or ML-based tool crawls the system or an application, collects its important data by capturing screenshots, measuring load time and analyzing basic UI elements.

Software Automation and AI Testing

1. Delivering Quality Product with accuracy and speed
   AI is reducing human intervention by limiting manual work that has been usually performed by humans. 
   AI is accountable for the activities that are complex and time-consuming such as:
     a. Identifying iterative test cases
     b. Detecting bugs from a thousand lines of code
     c. Programming regression test cases, etc.
   A combination of AI and humans is known as Intelligent Augmentation which helps the testers in delivering a high-quality product on time.
    
2. Agile Testing with proper Maintenance
   Maintaining test cases is a time-consuming manual task. These manual tasks can be automated with the help of machine learning development services using various algorithms.
   AI uses the stored data to understand the regular behavior of the test cycles. The current state of the product has been compared with the collected data when the test cases are executed. The test cases are updated accordingly if there are certain changes at any point of execution. This helps in maintaining test cases without manual intervention.
    
3. The self-recovery process to remove Unconventional Tasks
   AI performs has the ability to perform a self-recovery process. AI has a memory to store all the data for self-recovery. It can also mitigate risks and predict future processes. AI identifies and fixes the error before it occurs in the self-recovery process. AI continuously updates the algorithms by gathering the data which is helpful in detecting the behavior of the application and delivering efficient products.
    
4. Independent Tests
   AI gathers and stores responses from the servers after the execution of some manual tests. The gathered responses are used in the removal of dependencies on various modules and servers. This process enables higher test efficiency without any bugs and errors.
    
5. Quality management with regular Analysis
   Data analytics leads to quality management with AI and ML. Machine Learning helps in achieving test coverage in the analysis.  Customer's actions have been recorded with regular observations then this recorded data has been compared to provide quick feedback with lesser bugs and errors.
    

Applicable areas of Artificial Intelligence in Software Testing

Application Testing: An AI-based system builds test suites by analyzing the behavioral patterns of geography, devices, and demographics. This allows Testers to enhance the process of testing and correctness in the program.

Defect analysis: AI systems have been used to monitor and analyze data to compare them with prescribed parameters in order to detect errors. AI system generates a warning whenever it detects a problem or an error. AI system also performs a deep analysis of occurred errors, defining areas where defects can occur and provides possible solutions for further optimization.

Efficiency analysis: AI system provides valuable information to Testers by analyzing and summarizing relevant information from a wide range of sources, which gives a proper view of the alterations that need to be carried out. This information helps QAs to make more informed decisions.