The horrifying pandemic and its unprecedented impacts have turned the world upside-down! The workplace and patterns, which we witness today, are completely different from those that used to be just a couple of years ago.
Not just a cultural shift, all this has greatly emphasized the adoption of digital technologies around nooks and corners.
The increased digitization in all aspects of life has enhanced user expectations considerably. Clients now want all services at their fingertips round the clock without any interruption – as this is what the new work norm demands. Getting everything through a few taps on smartphones has been possible only through the Software as a Service business model, which has seen exponential growth recently.
To serve the users better and meet the ever-increasing business demand the organizations are focusing their attention on developing speedy, smart, scalable, and ‘social’ applications. Not just the app development but their software testing methodologies have also been greatly impacted.
In this changing software scenario, there are some key points to ponder for a better experience. And these are also the considerations that have contributed to different software testing trends. So let’s delve into their details:
Responsible Factors Driving Change Across Software Testing Patterns
The lesser the time to market, the faster your application creates a greater user base! So, for deploying & delivering better software in lesser time, developers are focusing more on Agile and DevOps tactics. The Agile approach is a continuous process of discovering the needs and product improvement through the collaborative efforts of a self-organized team working across different functional units. Breaking the siloes in software development and other related units like governing management, DevOps help bring software to the table fast and at scale.
On the other hand, the conventional development culture calls for manual regression testing, which is not only time-wasting but also finds risks and bugs at later development stages.
Need for Machine Learning
For the personalized experience of auto-curated recommendations and another universe of limitless possibilities, the applications are being powered with machine learning codes. All the organizations, for the better user experience and highly selective targeting, are adopting AI/ML for better innovations.
As the app is powered to learn from the previous usage experience, the testing gets more crucial.
Scalability as per the Business Need
With the aim of scaling the application service spectrum as per the need, applications are being architected over distribution systems with microservices and containers. Such containerized apps are good but come with their own set of security challenges.
With containerized apps, a proper testing methodology is a must and the interactions between microservices must be verified before releasing any new feature.
Need for connected apps with APIs
APIs- the middle layer between UI & Back End, connecting the data transfer and distribution systems from one unit to another; have seen an increased adoption with more and more microservices.
But to eliminate the vulnerabilities, these APIs must be tested.
With digitization and increased adoption of IT in all spheres, there is a greater need for secured applications. Whether it is for meetings, for financial transactions, or for getting any other service one needs to share private information, which can be leveraged for unethical practices. A single loophole is a threat to the whole organization. So there is a need for intense investigation in this regard as well.
These were the needs, and also the drivers for change in testing patterns. Now let’s have a look at the current trends in software testing.
Latest Software Testing Trends
There is an urgent need of developing test cases faster, execute the tests, and examine the results with minimal or no manual intervention. This process, termed Autonomous Testing, is required by all organizations.
Autonomous testing, facilitating a 360-degree view of the application, with its AI power automatically detects the faults. For efficiency and ease of workflow, most organizations are willing to adopt autonomous testing, and there are estimates that by 2024, 90% of the organization would have implemented this.
Converging Various Test Tools
With the pressing need to accelerate the test process, the professionals require an innovative approach that can optimize the testing within the stipulations. Managing the test process involves end-to-end activities, which can be divided into three main steps:
Most of the test tools tackle one or two steps, and for the complete process, multiple tools are deployed. As necessity is the mother of innovation, now the trend is converging the tools for centralized and faster task execution.
Model Driven Architecture Based Testing takes the application architecture into the consideration and creates a test suite along with the application blueprint. Model-Based Testing automates the test scenario generation and the related actions to be taken. So for the application to be tested, a model is generated, which helps in generating abstract tests and creates an executable script.
This technique renders great benefits in software testing as there is better test coverage in lesser time, reusable components, improved fault detection at lower costs, and lesser manual efforts. This help in getting a better turnkey product.
If testing is done from the earlier development stage of an application then it greatly enhances the confidence as almost all vulnerabilities are eliminated.
As in a linear flow diagram of app development, the early stages are to the left side so shifting attention to early stages interprets shifting attention to the left; and this is where the methodology got the name.
As this testing mechanism helps track performance metrics from the beginning, it also helps attain a quality-first mindset.
Coupled with DevOps the shift-left approach has paved way for continuous testing in the least possible time.
Testing the Security
The smartness, the scalability, and the multi-cloud approach all will go in vain if the application is not secured enough. All the applications require a rigorous security test as the absence could render a great loss for both the organization and the user.
Static Application Security Testing is conducted to find flaws in an application through a code and SQL injection, and DAST to examine the running application for potential loopholes.
With the increased digitization, corporate spending on security tests is on the rise and is going to witness a CAGR of 22.3% by 2025.
API testing involves the direct test of programming interfaces to check their functionality, performance, reliability, and security. With the uprise in cloud-based applications and the microservice architecture, API testing has become indispensable.
Test of Machine-Learning Model
More and more apps and devices are being AI-powered, and the techniques like Natural Language Processing, Natural Language Understanding, Optical Character Recognition, Deep Learning, and many more are being used profoundly.
Testing AI models means testing their accuracy. A such test involves the division of massive data sets into training and testing sets. The challenge lies in the creation of massive data set differences in test cases for different AI models.
AI testing has gained greater momentum and in a short time span, the testers might have access to advanced testing technologies and dedicated tools generating massive data sets.