Software Testing in the World of Next-Gen Technologies

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Key Takeaways

  • Digital transformation depends on next-gen technologies like AI, Big Data, Robotic Process Operation and more
  • Big data has a role in accelerating digitalization, and redefining testing procedures
  • Artificial intelligence is becoming essential for intelligent development and advanced software testing
  • IoT needs a more secure and connected environment and testing needs to ensure this 
  • Robotic Process Automation can compliment and improve software testing, and must itself be tested
  • Next-gen technologies have the potential to streamline testing and QA operations

Year over year, the entire concept of digitalization is revised, opening doors for new opportunities and better transformations. Especially when the world is rapidly adopting technologies like AI, AR, & ML into their operational practices, there is a constant need to monitor these technologies for compliance, standardization, security, and various other performance benchmarks.  

Gartner has predicted that seventy-five percent of the organizations are likely to go for operationalizing AI by the end of 2024. The change is likely to be very rapid, especially after all the unprecedented market shifts that happened during COVID-19. The business giants are now identifying the need for improved data analysis for better performance.  

Though there is no defined path to success in the future, the process is likely to involve next-gen technologies for the transformation process, facilitating excellence. However, one thing that is likely to define the ultimate impact would be software testing and QA to meet the goals aimed at creating value.   

This transformation will not be simple and small, allowing businesses to align with technologies like AI, Big Data, Smart Machines, IoT, 5G, and Robotics is a significant change. To leverage all these technologies, businesses need a confident adoption that is fostered through relevance, which could only be achieved when the solutions are mapped to objectives.  

Enterprises need to lean into quality assurance and software testing solutions that can help with agile development and add more value to digitization efforts. 

Let us dig into understanding these next-gen technologies, and explore how software testing and QA could lead to a productive and efficient future. 

Big Data 

Over the years, businesses and technology experts have realized the importance of data. And therefore, healthcare, manufacturing, telecommunication, and many other industries have started to lean on big data for improving customer service and meeting business goals. Gartner has predicted that 33 percent of large organizations will invest in decision modeling, implementing decision intelligence. This is because decision intelligence provides a framework to monitor and tune the decision process for profitable behavior.   

Since data is something that is constantly changing, it is crucial to embrace real-time information, amalgamate it with past records and make decisions that can create an impact. The core objective for big data is to achieve data completeness and foster transformations that are productive and are based on the right exchange of data. The potential of big data can only be achieved through connected systems that have the best of robotics, machine learning, IoT, 5G, and of course, big data.  

However, yielding the advantage of big data for business needs big data testing, ensuring that diverse datasets can be used to drive profitability. Also, the testing approach should involve market data and consumer information, which can be brought into light for creating Quality Assured solutions that will have the best of big data across industries.  

Big Data Testing Use Cases 

Functional Testing: data validation for the results produced by the application at the front-end in comparison to the expected results, in order to gain insights into the application framework and components. 

Performance Testing: big data automation testing could help you test the applications for variety and volume of data. Using big data test techniques could help achieve the defined goals related to processing and retrieval of data sets with storage efficiency. 

Data Processing Testing: data processing testing along with data ingestion testing could help you verify all that the data within the application is extracted and loaded correctly. Data processing can aid in validating the business logic for input and output files by comparing the information. 

Data Migration Testing: when an application moves to another technology or server, data migration testing can help validate that all the data from the old system is moved to the new system with zero loss and no downtime.  

Adopting Big Data Testing Strategy 

Based on my own experience and exposure to big data testing, adopting a big data testing strategy is about picking the right approach to implementation:  

  • It should start with initial actions on data ingested by the tester, that aims to verify extracted data for its accuracy. This might even involve the loading of test data across various locations. 
  • The next stage needs to take over validating the business logic on ingested data, usually done using tools like Hadoop or Hive.  
  • After successful validation, testers should take on the task of verifying output data to warehouse data. 
  • The fourth step is one of the most important parts of the testing strategy, where testers need to verify data migration testing, eliminating defects. This requires users to work through pre-migration testing, migration testing, and post-migration testing. 
  • The fifth stage of performance testing, testers need to work on data loading and throughput working on the rate of data created and consumed.  
  • Last but very important, the testers need to work on data processing speed, where sub-system performance is measured working across the workflow. 

Big Data Testing Example & Recommended Tools 

One of the most significant examples of big data testing in real-time situations is a case of pharmaceutical manufacturing, where genetically engineered live cells with 200 variables were tested for purity in the manufacturing process for blood components and vaccines. The problem turned out to be the yield variation of 50 to 100 percent, showing inconsistency in capacity and regulatory issues.  

The testing team segmented the process into activity clusters assessing process interdependencies and learning nine parameters with direct impact on vaccine yield. The testing helped to modify the target process to increase vaccine manufacturing to 150 percent, leading to an annual savings of around $5 to $10 million.  

Recommended Tools: HDFS (Hadoop Distributed File System), Hive, and HBase, while the data ingestion process could be effectively handled using Apache Zookeeper, Flume, Kafka, and Sqoop. 

AI 

If there is a technology that has gained momentum during the past decade, it is nothing other than artificial intelligence. AI offers the potential to mimic human tasks and improvise the operations through its own intellect, the logic it brings to business shows scope for productive inferences. However, the benefit of AI can only be achieved by feeding computers with data sets, and this needs the right QA and testing practices.  

As long as automation testing implementation needs to be done for deriving results, performance could only be achieved by using the right input data leading to effective processing. Moreover, the improvement of AI solutions is beneficial not only for other industries, but QA itself, since many of the testing and quality assurance processes depend on automation technology powered by artificial intelligence.  

The introduction of artificial intelligence into the testing process has the potential to enable smarter testing. So, the testing of AI solutions could enable software technologies to work on better reasoning and problem-solving capabilities. Moreover, AI can reduce time consuming manual operations, and time spent on initial testing of the devices could make complex tasks simpler and more efficient.  

AI Testing Use Cases 

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