Emerging Trends & Technologies in Software Testing
In recent years, there has been a great evolution in the field of the ICT industry. Hence, software testing which is an integral part of it also has been evolved with new trends of testing.
Commonly ICT professionals are familiar with the below trends of software testing. Most of these techniques and strategies came into play a few years back.
- Test Pyramid Approach which is a prevention-focused methodology. It promotes testers to prevent bugs as opposed to explicitly testing for bugs.
- Transfer from Quality Assurance to Quality Engineering where the tester can assess technical risks even before an app’s development begins.
- Be a Full-stack Quality Engineer who can test the system in both domain & quality aspects using manual techniques & test automation techniques as well.
With an eye towards of stay at the top of the industry & being competitive, delivering an excellent customer experience is a must. These include deploying superior quality software capable of performing well & the ability to implement quick updates of software based on market demand.
This is where proper software testing comes into play.
With the newly evolved technologies in software development, Testers face challenges such as ;
- Testing the software on multiple devices having different UI & hardware configurations
- Adherence to the growing industry
- Testing the softwares which performs frequent updates
- Up to date with technology amendments & select tools of testing wisely
- Ensure faster release cycles
To overcome these challenges, organizations need to reconsider their test methodologies and strategies.
This is where new technologies of software testing come to play & these emerging technologies can reshape the future of software testing.
Let’s look into the overview of the below-emerging trends & technologies in software testing.
- Artificial Intelligence (AI) and Machine Learning (ML)
- Big Data Testing
- Robotic Process Automation (RPA)
- QAOps or DevTestOps
- Internet of Thing (IoT)
Artificial Intelligence (AI) and Machine Learning (ML)
Artificial intelligence describes the concept of machines being able to be intelligent and complete “smart” tasks that were originally thought to require human intelligence.
Machine learning is a form or subset of artificial intelligence where machines are given data and then allowed to make sense of it. Over time, algorithms improve through an experience similar to human development (learning patterns) & take decisions with minimal or no human intervention.
As the first step, we must identify the areas within the testing process that can be optimized with AI(Artificial Intelligence) and apply the ML(Machine Learning) and DL(Deep Learning) algorithms.
AI and ML plays a vital role in driving quality engineering related to the following aspects;
- Do some predictive analysis on customers’ data to generate target reports/outcomes
- Optimize the testing process by gaining data-driven insights via AI & MI tools. i.e. Analyze the test results to predict vulnerabilities, optimize code, and report defects
- Visual validation & Spidering i.e. ML-based visual validation tools which can create a simple machine learning test that automatically detects all the visual & performance-related bugs in the software according to the pattern analysis
- Test Automation (Unit/API/UI) i.e. Generate & execute tests over more expansive areas. ML learns patterns and uses the data to improve the reliability of the tests.
AI-based testing tools that are quite popular in the market are as follows;
Smart algorithms can optimize the testing process & support testers to find the maximum number of bugs in less time. It will make the application more reliable & accurate.
The outcomes of AI & ML testing of testers can be used to refine the product by developers & it will result in making a smarter and more productive software for the end-user.
Big Data Testing
Simply big data means a large volume of data.
Gartner defines Big Data as, “Big data is high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation”.
Therefore to be accurate in the business decisions and other activities, it is essential for any software company to strengthen big data testing and deliver valuable insights about various products and services.
According to Evans Data Corporation, “19.2% of big data app developers say quality of data is the biggest problem they consistently face”.
There are several types of testing available for testing each characteristic of big data. At a glance, you can get a big picture of it via the below diagram
Let’s discuss some of the challenges in Big Data Testing
- Data Harnessing (Cleansing)
Challenge: Dealing with both structured and unstructured data which makes sampling strategy very difficult.
Solution: Perform in-depth analysis of structured and unstructured data to convert them into a valuable format.
- Data Quality & Completeness
Challenge: It is difficult to make sure that complete data is pulled into the system pulled from various sources like RDBMS, weblogs, social media, etc
Solution: Use tools like Presto, Talend and Datameer to verify the completeness of the data in HDFS.
- Displaying Meaningful Results
Challenge: Creating the BI reports from Big Data becomes difficult when dealing with extremely large amounts and diverse data.
Solution: One way to resolve this is to cluster data into a higher-level view where smaller groups of data become visible.
- Test Environment Setup
Challenge: Creating an effective test environment, multiple testing nodes for Big Data testing.
Solution: Take care of the environment to handle the Big Data effectively and efficiently.
- Performance Testing
Challenge:Faster data processing, work load, and network load balancing to ensure real time data synchronization.
Solution:Have good infrastructure to store and process large amount of data in given time intervals to meet the performance.
Big Data Testing Tools that are quite popular in the market are as follows;
Insights of big data testing can be used to improve the quality of products and services, hence the organizations can deliver a better customer experience.
Robotic Process Automation (RPA)
RPA is the technology that creates a virtual workforce (or we can call software bots) to imitate human actions and automate repetitive and rule-based processes.
Customers demand faster & better output, hence organizations tend to implement RPA to automate the processes to optimize them with minimum errors.
Throughout the software testing activities, we can use RPA to optimize the repetitive testing activities to automate large and complex data sets through an easy-to-use interface. RPA has the capability to perform all types of testing that use automation testing tools, thereby eliminating the need to write test scripts.
Demanding features of RPA in Test Automation
- Code-less: No programming skills required
- Adaptable: Possible to schedule monitoring of work and can operate 24*7
- Cost Saving: Huge cost reduction as a very minimal workforce is required
- Accuracy: Bots governed by algorithms accurately perform actions
- Flexibility: Platform independent. Supports a web-based, legacy, desktop, or mobile application
- Scalability: Virtual workforce can be assigned anytime, and allows parallel execution
- Data Migration and Change Management: Robots that work on legacy systems can easily be used to preserve the application’s data and integrity and they can also re-use the existing application logic, databases, and validation without high re-structuring or maintenance costs.
- Cognitive Automation: RPA integrates structured and unstructured data using different AI features and extends the automation to more processes using cognitive capabilities for predictive analysis.
Let’s have a look at the differences between Test Automation & Robotic Process Automation.
Due to the outstanding features of RPA, organizations return the following benefits;
- Cost Reduction: Bots are cost-effective since they save time and generate output with precision
- Reduce errors & Enhance accuracy: RPA reduces the rate of human errors as mundane manual tasks are automated
- Increased throughput and output: RPA testing supports the throughput of Big Data Testing that enhances the overall quality and maintenance of your end product even after it hits the market.
RPA tools that are quite popular in the market are as follows;
- Blue Prism
- Contextor (now part of SAP):
- Power Automate
RPA enables work to go on 24/7 without any break and with precision. This, in return, guarantees a superior customer experience.
QAOps or DevTestOps
DevOps as a term originated in 2009 following a talk at the O’Reilly Velocity Conference titled “10+ Deploys per Day: Dev and Ops Cooperation at Flickr.”
DevOps is a set of practices that combines software development and IT operations intending to provide fast and continuous delivery by adhering to the DevOps key principles.
QA is playing a crucial part in the complete software development intending to deliver a better quality product.
And the combination of these two methodologies, i.e. QA and DevOps brings a new practice called QAOps or DevTestOps.
QAOps is the practice of integrating quality assurance (QA) into the CI/CD pipeline. This means that the software testing process should be integrated into the CI/CD pipeline rather than being an isolated process.
There are several ways to approach the implementation of QAOps.
- Smoke Testing: Smoke Testing is used to check the stability of the product. QAOps leverages smoke testing for verification testing.
- Automated Testing: Automated Testing enables to testing of the software at a faster pace. This is leveraged by the QAOps framework by executing the automated codes as a part of the QAOps pipeline.
- Regression Testing: Regression Testing allows checking the reliability of the product when it is updated with a new feature or an existing feature is enhanced. QAOps checks if the quality of the software is maintained with the introduction of the new features or modifying the code.
- Parallel Testing: Parallel Testing allows running multiple test cases on an application and its subcomponents across operating systems and browsers for a product simultaneously which reduces the overall testing time. QAOps uses CI/CD pipeline for parallel testing which instantly enables faster testing.
- Scalability Testing: Scalability Testing depicts how the software will behave when the usage will increase or decrease in the longer run. The tests should be able to synchronize with the data and the process during scale up or down and we can achieve it via QAOps by leveraging scalability testing in the pipeline.
QAOps is a useful set of practices/framework that automates the processes among software development, operations and QA to deliver a software faster and more reliably by identifying bugs at an earlier stages.
Internet of Thing (IoT)
The term “Internet of Things” or IoT was first coined by Kevin Ashton in 1999. But it was only when Gartner added IoT to its list of new emerging technologies in 2011
The Internet of Things (IoT) deals with millions of devices by providing easy controls and remote access to information and services via a smartphone, smartwatch, or tablet from anywhere in the world.
It connects different objects that are provided with unique identifiers and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction.
Any physical object can be transformed into an IoT device if it can be connected to the internet to be controlled or communicate information.
The main types of IoT devices are as follows;
- Consumer IoT: Primarily for everyday use.
Eg: home appliances, voice assistance, and light fixtures.
- Commercial IoT: Primarily used in the healthcare and transport industries.
Eg: smart pacemakers and monitoring systems.
- Military Things (IoMT): Primarily used for the application of IoT technologies in the military field.
Eg: surveillance robots and human-wearable biometrics for combat.
- Industrial Internet of Things (IIoT): Primarily used with industrial applications, such as in the manufacturing and energy sectors.
Eg: Digital control systems, smart agriculture, and industrial big data.
- Infrastructure IoT: Primarily used for connectivity in smart cities.
Eg: infrastructure sensors and management systems.
Nowadays, different sets of products enter the IoT market. IoT testing involves real-time intelligence and communication to ensure perfect harmony between hardware and software throughout the connected network. Due to this, IoT testing is undergoing changes in terms of approaches and scenarios.
A typical IoT platform has four basic components: application, sensors, backend database (data center), and network communication.
Each component of the IoT needs different testing procedures. Testers need to consider these elements to formulate a comprehensive test strategy.
Let’s discuss some of the popular trends and scenarios of IoT testing:
- Artificial Intelligence (AI): The effect of AI in IoT testing can reduce extensive human hours and inaccuracies/discrepancies which results in ensuring a more reliable product. Testers can consider it to drive automation, expand coverage and enhance efficiency in all the areas of IoT testing.
- IoT Network Security Challenges: Securing the data and its privacy is very critical and authorizations play a very important role in IoT data streaming and transfer.
It might be difficult to secure all the connected devices since the components or modules of IoT are not as simple as that of PCs and smartphones. Considering these security challenges in IoT, QA professionals must assess a full range of potential vulnerabilities in IoT products and services.
- Big Data Testing: The volume, value, variety, and variability of data generated by IoT systems and devices can be a huge challenge in IoT testing soon. Each type of data set requires different types of testing techniques and testers will have to adapt their testing skills accordingly.
- Micro Services Test Automation: Microservice architecture involves developing a single application that can work together as a suite of small services. When it comes to IoT, each of these services can run as an individual process and communicate with lightweight mechanisms such as an HTTP resource API.
Utilizing Microservices for IoT testing will also offer the benefits of testing the extensibility, scalability, and integrations of the IoT system.
Microservices test automation in IoT will reduce the complexity of testing a huge architecture as each microservice can be tested as a separate process.
- Testing For a Wide Range of Interfaces: Depending upon the type and number of connected IoT devices, it is necessary to test the integrations and interfaces to ensure appropriate coverage and universal functionality across all the touchpoints.
If we consider the video streaming sites such as Netflix and Amazon Prime can be accessed on a variety of devices from HDTVs, smartphones to a gaming console.
Therefore it is important to test the video streaming functionalities on all the platforms and connected devices are required to test on acceleration, vibration, stability, repetition, stress, memory, or endurance factors.
- Testing Wireless Connectivity Scenarios: Connectivity throughout the IoT network is dependent on many wireless standards like Wi-Fi, ZigBee, 4G LTE, etc. This will result in different wireless testing scenarios.
Sometimes IoT devices could be out of range and so they will have to monitor the condition continuously. Therefore testing of various wireless scenarios will be an essential area to focus on IoT testing.
Top 5 Most Popular IoT Devices
- Google Home Voice Controller
- Amazon Echo Plus Voice Controller
- August Doorbell Cam
- August Smart Lock
As of 2021, there are 21.7 billion active connected devices in the world today, out of which more than 11.7 billion (54 percent) are IoT devices. This means that there are more IoT devices in the world than there are non-IoT devices.
- Lots of new trends & technologies including above what I have discussed have already come to the party of the IT industry and are evolving day-by- day by reshaping with lots of new trends.
- Organizations use these technologies to provide fast and reliable products to customers.
- Software testers play an integral part in delivering a better quality software product to the customers, thus they should always be up to date with these new trends and technologies.
- Learning & being armful with trends would critically affect the software testers and will help yourself ready for the game.
- As we all know, change is the law of life and it is going nowhere. So it is crucial to stay ahead of it. If you don’t want to get left behind, it’s essential to embrace these new trends & technologies.
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