Due to the pandemic, the adoption of digital technologies has been phenomenal across the verticals and horizontals.
As per a survey conducted by McKinsey and company, responses to Covid-19 have only speeded the adoption of digital technologies by several years- and that many of these changes could be here for the long haul.
There has been a culture shift, which has been well supported by these digital technologies. Work from home, door-step service delivery, digital payments are some changes that have been made easily possible with the adoption of these digital technologies.
Every business understands that the increase in digitization has led to an exponential rise in customer expectations. In the banking sector, customers want to complete their banking needs on mobile in seconds and with no interruption or outages. They need payment applications that are safe and easy to use. Customers also expect the services to be available at any time of the day and accessible from anywhere in the world through the internet. This delivery model is called a SaaS (Software, Anything as a Service, or XaaS). Recently, the SaaS model of delivery has witnessed an exponential rise in demand for application delivery.
To meet business demand, IT organizations are looking to build faster, intelligent, scalable, and connected applications to serve their customers better. The need has brought a significant impact on the software testing landscape. Let us inspect the drivers in IT organizations that are contributing to the changing landscape.
Drivers leading the change in Software Testing in 2022
Figure : Key Drivers for Software Testing Trends in 2022
1. Building a faster application with Agile and DevOps culture
Most IT organizations are adopting Agile and DevOps practices for their software delivery because they want to deploy and deliver better software faster.
The Agile approach involves a continuous process of requirement discovery and solution improvement through collaboration among self-organizing cross-functional teams. Most of the quality assurance practices involve an agile way of delivery. DevOps methodology breaks siloes between software development and operations help deliver software faster, safer, and at scale.
Traditionally, testing teams would be working on a build that is deployed in a development environment and would manually perform regression testing. However, with the new culture delivering application faster to the market, the demand for testing applications to find risks and bugs, in a shorter duration and with limited cost will be only rise.
2. Building an intelligent application with AI/ML
AI/ML technologies are being leveraged to provide advanced customer experience through curated recommendations, ease of resolving grievances, and much more. Natural Language Processing (NLP) another arm of AI/ML has transcended experience by understanding human demands through a spoken language. B2C and B2B organization across domain has adopted and embedded AI/ML into their application and they are accelerating their innovation to serve their customer in a better way.
As the usage of AI/ML software increases, organizations are supposed to focus more on testing of these apps using some automation tools.
3. Building Scalable applications with Containerization and Microservices
All organizations are architecting distributed systems using microservice architecture and containers; primary reason is to scale as per business demand. Containerized apps are favorite to DevOps and Site Reliability Engineering (SREs) because they provide scalable computing environment and quickly and reliably run applications as a form of package (code along with all the dependencies, also called artifacts). Other benefits like containers require fewer resources, containerized apps can be rapidly deployed, patched or scaled into any cloud.
Containerized apps and microservices are good, but they bring their own set of challenges such as they are prone to attack; since services are loosely coupled and talk to each other or are dependent, failure of one service mean failure of the whole application.
There is a need for a proper strategy to test the complete containerized application and verify all the interactions between these microservices using sanity testing and smoke testing before the releasing a new feature to production.
4. Building connected applications with APIs
API is the middle layer between the UI and back-end (presentation layer and data layer). APIs connect distributed systems and transfer data from one system to another. With the increased adoption of microservices, usage of APIs has also increased exponentially.
For e.g., a retail website can have many small services such as catalog, product search, order submissions, inventory recommendations, etc., are connected using APIs. While APIs hold the key for digital transformation, they also open the door for potential security threats.
To make an application free from all vulnerabilities, testing APIs is necessary. API testing needs to do more than just validate the optimal implementation of APIs. QA team should also assess APIs for security risks and threats, and help business to offer a secure and reliable platform to their customers and partners.
5. Building secured applications
There is a growing demand for application security with the advent of digitization and increased adoption of technology. The world has become more mobile, and business groups are investing in their users to interact with their services through an app on various devices, such as Microsoft Teams, Zoom, WebEx, and so on.
Any loopholes pose a threat to businesses. Weak server-side controls, unsafe data storage, broken cryptography, and other problems open the door for external attackers to steal confidential and sensitive data. The cases of SolarWinds Orion supply chain attack and Log4j2 bugs are so fresh and they remind us of what could be the potential loss of security threats.
Now that we have read about the drivers let us quickly see how the software testing landscape is shaping up in 2022.
Top 8 Software Testing Trends in 2022
Software testing is a critical piece of the puzzle in creating faster, intelligent, scalable, connected and secured application. After all, it just takes a few seconds for customers to form an opinion about a brand after they browse through an application or website.
However, legacy processes or technologies in software testing will be a hindrance for IT organizations to scale. CIOs, Enterprise Architect and QA managers need to take preemptive action and adopt a new strategy in quality assurance to meet the growing business demands.
There are eight trends that business executives, CIO and platform architects should look at:
- Autonomous testing will be at the heart of adoption
- Consolidation of tools for test design, test execution, and test planning Model-based, architecture-based software testing
- Shift left testing for faster and intelligent applications
- Organizational mandate on Security testing for risk-free applications
- API Testing
- Mobile Testing
- AI/ML-model Testing
- Security Testing
TREND 1: AUTONOMOUS TESTING WILL BE AT THE HEART OF ADOPTION
Applications are rolled out faster to serve the end customers and testing is expected to happen on time. However, QA engineers although use automated testing tool, still rely on generating automated test scenarios. Hence, organizations need to generate test cases faster, execute them and finally analyze test results without human intervention. This process is called Autonomous testing, and every organization will require this.
Autonomous testing ensures software testing platforms have a unified, 360-degree view of the test application. Autonomous testing platforms use AI and ML to be contextually aware and automatically detect defects within the test application. They can further execute test scenarios with minimal or no human intervention.
Click to read more why Autonomous testing will be a game changing strategy for IT organization.
Autonomous testing has gained interest among the testing community and will grow in the coming years. As per Omdia Report 2021 on autonomous testing, most organizations will adopt autonomous testing in 2022 (you refer the figure below). And almost 90% of all surveyed companies will have fully deployed autonomous testing by 2024. You can read the whole report here.
Figure : Maturity of Autonomous Testing
We provide ignio AI.Assurance to help QA engineers to test software autonomously. ignio AI.Assurance plays a pivotal role in enabling continuous testing right from the inception of software development. Using ignio AI.Assurance enterprises can generate test scripts and test scenarios at the click of a button.
TREND 2: CONVERGENCE OF DIFFERENT TOOLS FOR TEST DESIGN, TEST EXECUTION AND TEST PLANNING
With more focus to accelerate testing for many applications rolling out everyday, tester would have to think for ways to optimize the software testing process and reduce the time
Test management process involves managing the testing activities end-to-end. This process involves test planning, test design, and test execution. When managed and coordinated effectively, these activities discipline the software testing process.
- Test Planning stage: Consists of a document that involves a detailed report about the scope and approach for testing activities.
- Test Design stage: Consists of creating the suitable test suite (test scenarios and test cases) for testing the application.
- Test Execution stage: Test execution involves execution on time and within budget, and monitoring results to verify all the metrics.
Most of the testing tools cater to the execution and planning stages of test management. Multiple tools are being used to provide test design, execution or planning. This will concern the QA and Finance team regarding resources to manage those tools and costs. Hence, we feel there will be a convergence across tools for various stages in software testing.
ignio AI.Assurance has the latest AI / ML technologies and capabilities to automate all the three stages of test management activities – test planning, design, and execution, thus removing the multiple uses of testing products and simplifying the testing process.
TREND 3: ARCHITECTURE-BASED SOFTWARE TESTING TO INCREASE TEST COVERAGE
Test scenario creation can be done in multiple ways by QA engineers. But one of the most optimized ways if generating test is Model Driven Architecture Based Testing (MDABT). MDABT considers the applications architecture under test and creates the test suite and application blueprint. Model Based Testing (MBT) automates the generation and execution of test scenarios and test automation scripts using models-based techniques on application requirements and behavior (refer the image below). A model is generated for the application under test. Based on this model, abstract tests are generated, and an executable test script is created which helps in testing the application.
Figure : Model Driven Architecture Based Testing
The significant benefits of MBT include:
- Improved test coverage
- Reduced testing time, and increased reliability
- Reusability of tests
- Less human effort
- Reduction in cost
- Improved fault detection
- Improved product quality
TREND 4: SHIFT-LEFT TESTING
To improve the confidence on a software release, it has to testing has to carried out from the very beginning, also known as shift-left testing.
Shift-left testing is an approach to software testing and system testing in which testing is pushed towards the early stages of software development (refer the image below). Organizations can detect issues early in the software development lifecycle. The developers and testers have a similar day in this kind of testing, where software development and software testing move in parallel.
Shift-left testing approach helps enterprises achieve a quality-first mindset. This testing mechanism helps them track performance metrics right from the inception of software development.
The shift-left approach, along with DevOps and automated tests, has unlocked the possibilities of continuous testing while ensuring lesser turnaround time, efficient detection of defects at earlier stages of development, lesser manual intervention, and shorter execution and release cycles.
Figure : Shift Left Testing Flowchart
The Software testers need not be restricted or limited to testing alone. Developers too can test in parallel to development and testers can develop and test in parallel.
As per David Moss,
“A leading software testing consultant, shift-left testing can be done even before there’s a code. At the beginning of application development, there is no software application. A quality assurance function can be performed by participating in requirement and design reviews, in hope of driving defects earlier in the process” (click here for complete video).
ignio AI.Assurance enables you to test the application event when it is not developed correctly. Even when a developer has the UI ready, you can use ignio AI.Assurance to test the UI page for correct dependencies with other pages, seamless user experience flow, and business logic.
TREND 5: MORE STRESS ON SECURITY TESTING TO MITIGATING RISKS
Building an intelligent and scalable system using a multi-cloud approach is not enough if the application is vulnerable to security risks and cyber-attacks. Rampant security attacks makes headlines and also reminds us that just rolling out applications faster to the cloud is not enough.
Every application must go through rigorous security testing to free it from potential risks, threats, and vulnerabilities, thereby reducing any possibility for loss of information, revenue, or reputation. Organization has to use methodologies such as Static application security testing (SAST) to examine the code to find software flaws and weaknesses such as SQL injection, and Dynamic application security testing (DAST) to examine an application as it’s running to find vulnerabilities that an attacker could exploit.
Certain tools help in SAST and DAST scanning are HP Appscanner and JFrog Xray, SonarQube.
According to Gartner, with the increased adoption of digitization and multi-cloud, the IT spend in security testing will grow CAGR of 22.3 percent till 2025.
Figure : Security and Risk Management Spending
TREND 6: API TESTING FOR CHECKING APIS AND INTEGRATIONS:
API testing is a type of software testing that involves testing application programming interfaces directly and as part of integration testing to determine if it meets the required criteria for functionality, reliability, performance, and security.
The rapid adoption to cloud computing has highlighted the importance of Application Programming Interfaces (APIs). With the rise in cloud applications, microservice architecture and connected platforms, API testing is a necessity.
Data Bridge Market Research estimates a splurge in the adoption of API technology by both small and mid-sized enterprises and large enterprises. Rising complexities in the information technology sector and increased focus on the implementation of advanced technologies for software development will likely drive the growth of the API testing market.
ignio AI.Assurance provides QA testers the ability to perform manual and autonomous API Testing across applications and multi-cloud using Swagger.
TREND 7: MOBILE APPLICATION TESTING IS ON RISE
With the need to serve customers using apps also brings the apps in various devices such as a laptops, and smartphones. And as more and more users spend an increasing amount of time on their mobile devices, it becomes essential for B2C and B2B enterprises to deliver a quality experience for mobile applications. If these applications fail to work or do not provide the desired user satisfaction, it will result in irate customers and negative feedbacks on different social media channels. Ensuring these applications provide the best mobile experience through continuous mobile testing will be crucial for companies offering smartphone apps to deliver services.
Mobile testing is a process by which application software developed for mobile devices is tested for its functionality, usability, load, and consistency.
According to a comprehensive research report on mobile testing by Market Research Future (MRFR), the number of enterprises adopting mobile application testing is supposed to grow at a CAGR of 20.3%.
ignio AI.Assurance helps QA analysts and mobile app developers autonomously scan through the mobile applications and perform end-to-end testing. Detect bugs, anomalies related to data flows and criticality of various bugs, thereby providing an enhanced customer experience.
TREND 8: TESTING OF AI/ML MODELS IN APPLICATION:
Usage of AI/ML will continue to grow in software development. Many arms of AI such as Machine Learning (ML), Natural Language Processing (NLP), Natural Language Understanding (NLU), Optical Character Recognition (OCR), Deep Learning, and so on have been used profusely.
The primary purpose of testing AI models is to determine the accuracy. Testing with AI / ML involves testing the models with a massive set of data divided into training and testing sets. Testing these models becomes challenging as test cases depend on the AI model and the creation of massive sets of data. Testing all these models might require testers to understand the AI technology, at least the basics. (Testers usually refer to them as black-box AI model).
Based on the discussion with QA analysts and engineering heads of many Fortune 500 companies, I have concluded- although the AI / ML testing is picking up, it is still being carried out by developers or the analytics team themselves. Once the momentum picks up in AI / ML development in the next 3-4 years, testers will be perhaps involved in testing these technologies with the help of new tools that can generate massive data sets.
How ignio AI.Assurance is leading the Software Testing Trend in 2022 ?
If you are a QA engineer or architect who wants to achieve the perfect cost, quality, and time equilibrium in the SDLC process, then you can lead the change in software testing with ignio AI.Assurance.
ignio AI.Assurance is an autonomous assurance product that leverages AI/ML capabilities to deliver better software faster. Its unique approach of creating a nodal structure under test helps provide a 360-degree overview of the application. It provides the highest level of automation with end-to-end testing, autonomous generation of test scenarios and test cases along with impact-based analysis and smart test data generation. Intelligent test selection feature selects test suite for execution based on time and quality parameters to perform fit-for-purpose testing. Its autonomous RCA detects problems automatically while the self-healing aspect rectifies the application blueprint and test suite autonomously based on application changes.
It helps QA testers with:
1. Increased Simplicity in the Testing process
2. Improved Software Quality
3. Enhanced Customer Experience
ignio AI.Assurance is helping some Fortune 500 companies (including European retailing giants) achieve over 87% increased productivity gains, 10X enhanced agility in IT development and improved product stability.
