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InfoQ Homepage Articles Software Testing in the World of Next-Gen Technologies

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.  

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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. 

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.  

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.  

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:  

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. 

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.  

Unit Tests: The most common case for using AI in software testing is to analyze unit tests. Since these tests do not have any setup requirements, using AI tools for creating unit tests is much easier. AI could help update unit tests for every change in source code, allowing developers to spend less time on maintenance. 

API Testing: AI technology can be used to identify relationships between different API calls defining the parameters to test. Moreover, AI could use user behavior data to define advanced testing patterns, improving the overall quality of the application. 

Continuous Testing: AI-enabled continuous testing could help detect altered controls since it has the capability to observe every minute change. Moreover, testers and developers could plan to run risk-based automation to determine which tests could give greater coverage. The data can be used to update test cases manually to locate controls, spot defects, and improve the related components. 

UI Testing: AI can also be used for object application classification since artificial intelligence could help work on the hierarchy of controls and define the technical map required to drive GUI.  

The present technologies due to lack of intelligence and untimely human intervention have to deal with incapable test runs. This simply leads to product bottlenecks with no insights on test errors, code issues, and other significant challenges within the testing environment. Artificial intelligence in testing allows users to get over the load and optimize the testing process for added efficiency. 

In regards to using AI as a cure for testing failures, here are a few ways, based on my personal experience, where AI can be applied:

If we look back a decade in time, connected devices seemed to be a mere concept only. However, the introduction of IoT enabled digital transformation. According to a report by Applause, 79 percent of US citizens use at least one IoT device, which can be anything from a thermostat to a refrigerator. Moreover, IoT technology, when paired with powerful 5G networks, has the potential to speed up operations and processes. It is predicted that the global population of IoT devices is likely to reach 125 billion by 2025.  

With all these growing figures, testing IoT software is vital to making the transformation. Especially when most consumer brands are offering IoT capabilities to innovate their business, testing IoT solutions could help improve the frequency of data exchange for effective operations. Moreover, efforts made on security, functionality, and performance analysis support the  the consumer lifecycle. The testing process could allow businesses to overcome the challenge of managing operations manually while shifting the dependability on IoT devices.  

Testing IoT technologies could help meet standards of performance expected by consumers. The early identification of any bugs within the system could help immediate rectification of the IoT device and software.. Since IoT is all about network and connected devices, locating any areas of fault in the interconnected system requires performance tracking of all devices.  

IoT testing has the potential to enable the confident use of all the devices for the performance and functionality outcomes through an improved IoT network. Performance, security, database, functionality, compatibility, usability, scalability, reliability, network, pilot, and regulatory testing all help to identify the explicit nature of IoT devices while improving the function of the application, data center, and sensors, as well as network communication.  

Performance Testing: The performance testing goals for IoT use case setups include monitoring of network communication models as well as internal computation capabilities of embedded systems. Most of the time, these include testing of Network and Gateway, as well as backend and application components.  

Security Testing: This phase of IoT testing is aimed at securing cloud services, the physical devices, and networks. 

Compatibility Testing: IoT devices have a varied range of software and hardware configurations, and compatibility testing supports developing stable solutions.  

Functionality & Usability: Testing of functional use cases and end-user experience in terms of reliability, durability, and ease of installation. Besides this, IoT testing includes other test approaches such as application localization, API, data integrity, compliance, connectivity, device interoperability, and scalability. 

Since security is one of the most important concerns associated with IoT testing, a tester has to deal with the challenge of securing a network and all internal communication. Also, the complexity of the IoT network analysis may let bugs hide in the system. The resource considerations related to bandwidth, IoT device battery, processing, and memory are a few things that might bother testers during the test process. 

IoT testing should be initiated with Gray Box testing that is meant to work on test case design. This approach helps you to learn about architecture, connectivity, hardware limitations, and third-party hardware. Real-time checks should be made on connectivity, modularity, security, and scalability of the system using an automated IoT testing system.  

For IoT Software Testing: CloudTest, Shodan, SOASTA, Tcpdump, & Wireshark 

For Hardware Testing: Digital Storage Oscilloscope, JTAG Dongle, & Software Defined Radio 

Testing is an important aspect of RPA. Performance, reliability, or functionality of the robots, testing and QA enables developers to meet the design goals of the end product. Effective testing allows scientists or engineers to identify any flaws within the robots since they are working on machines made to take over tasks that previously required human involvement.  

Working on robotics systems and their real-world applications requires users to think about experiment designs. All the operations in which robots are used are practiced within a controlled environment to get over any inconsistencies. QA and software testing for the robotics system allows identification of any necessary and random changes to an experimental setup to ensure systematic performance. 

For instance, RPA in the healthcare industry would help the testers simplify the endless paperwork and administration while taking off load. RPA in healthcare could improve the entire process for inventory management, data entry, data fetching, digitizing patient records, appointments, and billings. 

As long as it is concerned with the world of software testing, robotic process automation has the capability to allow organizations to practice the use of robots for working on transactional processes or work on data for process-defined operations. Robotics could even allow automation of performance and functionality requirements through strong authentication of the results while meeting future needs. 

Unit Testing: Unit tests in robotics allow the comparison of test results for every unit of code and functionality to confirm that end results are intact. 

Regression Testing: Regression testing can be put to use on a robotics system to validate the robustness of the robotic system for positive and productive outcomes. 

Integration Testing: The integration testing in robotics could help check for the system modules, ensuring all work well according to design and expectations. 

Robotics Testing: Challenge & Purpose 

When it comes to testing, robots are usually used for acceptance testing with the objective to automate test cases because of the extensive complexity involved with the process. Moreover, Robotics in testing could help testers to get over the challenge of interactions with the operating system and common assertions. Apart from this, the robot framework could help to increase the number of available libraries and resources for robot framework.  

The introduction of advanced technologies like AI, Robotics, 5G, and IoT all can be implemented within the enterprise environment, improving real-time decisions. These technologies can collate data from various sources, analyze the information, and streamline the system for efficiently tracking and utilizing the data to meet productivity goals associated with a decision. 

Added Productivity in Routine Operations 

The use of technologies like artificial intelligence and machine learning through IoT devices could help improve interdepartmental interactions, while fostering any complex tasks. Also, the use of smart devices could improve productivity within routine operations while elevating the entire experience for the internal teams, as well as end-users of the system.  

Better Interdepartmental Connectivity & Collaboration 

When it comes to meeting standards related to quality and performance, next-gen technologies like 5G allow improved connectivity within departments. Also, the use of artificial intelligence and IoT in the testing process could allow easier data integration, improving the connection between developers, consumers, and other units of the organization. In short, leveraging the aforementioned technologies into the testing process could help in effective communication between stakeholders.  

Accelerated and Precise Development  

The use of IoT technology in a development environment could allow industries to enjoy faster release and production. The use of smart machines that have the ability to read data and lead the process could help achieve the scope of the products while driving business decisions more quickly. Industry practices that involve using these technologies and tools could experience more accurate scope achievements while leading to better product development. 

Since the primary and most important goal associated with any product development operation is to meet the end-user requirements, leveraging big data into the development and software testing process could allow improved customer service. The failure data and the information related to experiments that have managed to meet the needs of end-users could all help develop products that are crafted to meet the customer's needs in the future. Moreover, the use of these technologies in the world of software testing could help tech giants to expand their customer base through effective management and deliveries.  

The entire idea of digital transformation in the upcoming years is about industry adoption of technologies like IoT, AI, 5G, and big data into business. With technology becoming more prominent, software testing could help create more stable and secure digital ecosystems that are seamless in their functionalities and performance. All in all, with digital ecosystems becoming more widespread, improving IoT testing, big data testing, AI testing, and other testing methodologies provides a solid base for the upcoming software and application needs of industry.  

Kanika Vatsyayan is vice-president, delivery, and operations at BugRaptors, a CMMi level 5 certified software testing and Quality Assurance company. She is a QA professional with a grip on several leadership positions such as test program planning, innovation, and process transformations. From quality control to test leadership, test practices, and assurance strategies, Vatsyayan is a seasoned expert with influential tech skills. Besides this, she has a knack for writing and therefore has published countless articles and blogs educating audiences across the software testing industry.  

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