“Every Code is Guilty until Tested” – Every Conovo SQA Ever
You can validate this with an experienced tester. In this fast-evolving world, modern clients and customers expect a frictionless software experience on their terms and through their preferred channels. To meet these increasing demands, testers are under immense pressure to continuously adapt, improve, and deliver competitive customer-centric solutions.
From Artificial Intelligence (AI) to facing issues in several next-gen technologies like cloud computing, automation, and mobility, 2021 has brought new standards and demands, deeming testers to use early adoption methods to meet user requirements.
This article outlines three key trends that help companies optimize their testing and release cycles and deliver tried and tested products.
The Use of AI and ML in Testing
The application of AI and ML in software testing focuses on reducing time for software release cycles, but the concept is still in its infancy.
Our internal research and experience suggest that AI will dominate SQA testing, through:
- Using artificial intelligence algorithms to identify test scenarios that necessitate both manual and automated testing;
- Optimizing automated tests by identifying and removing superfluous test scenarios; ensuring optimal testing coverage to discover critical keywords from the Traceability Matrix;
- Forecasting important parameters and indicators that define end-user behavior and indicate areas for emphasis;
- Discovering potential uses and any faults linked with business operations.
As AI transfuses within society, it has become increasingly important to verify that these technologies are useful, trustworthy, private, highly functional, accessible, and robust. In other terms, AI must be tested. Regrettably, there haven't been many advancements in the evaluation of AI-based systems. To automate tasks such as application identification, parameterization, test creation, and fault diagnosis, the present state of the field employs autonomous and intelligent entities known as "testing bots". These bots are developed by using an integrated mix of different techniques such as the decision tree learning model, neural networks, and using reinforced learning.
Moreover, these technologies help QA teams across the world to design specific and tailored tests, fix issues and bugs, and decrease the time required for human intervention in the test generation and maintenance cycles.
Integration of QA and DevOps
DevOps is a culture change that brings together formerly separate development and operations departments. Furthermore, in a DevOps system, testers and programmers have similar tasks and functions. In DevOps, the lines between a programmer as well as a tester are frequently crossed. Automated testing is a must-have for enhancing an organization's delivery frequency and coding standards. Nevertheless, despite its important position in DevOps as well as the significance of pushing releases as rapidly as possible through the CI/CD process, automation testing has limitations. In truth, it does not eliminate the need for humans in research or operational procedures. The reason for this is quality assurance monitoring, which guarantees that no customer experience concerns are shipped to production by mistake.
We believe the integration of QA and DevOps has two important principles:
- Frequent testing and quality assurance processes within the CI/CD pipeline convey high coding standards, quality, and faster release cycles.
- Working side-by-side with both QA and developers to achieve an optimized workflow and testing cycle.
As smartphones become more sophisticated, the popularity of mobile app development keeps increasing. Mobile test automation will be included in DevOps toolchains to support multiple DevOps. Nevertheless, leading to a shortage of methodologies and resources, mobile test automation is currently underutilized. The use of automated testing for mobile apps is becoming more popular. This trend is being pushed by the desire to reduce time-to-market as well as the development of more complex tools and methodologies for seamless application performance.
These shifting and rapidly evolving trends require companies to conduct in-depth research and integrate testing and development departments. This integration allows efficient mitigation of bottlenecks, faster release cycles, and overall improved performance.