Today, customers have become a choosy lot and businesses are churning out new and better-quality products at the drop of a hat to entice them. Thus, faster time to market with quality products is the new mantra that enterprises ought to embrace to stay competitive. Besides, quality is not something to be glossed over or treated as an afterthought but should be made an integral part of the whole value chain. Many enterprises are aware of this but find it too complex, cost-intensive, or time-consuming to implement. However, the only way to retain customers’ loyalty and stay competitive is to deliver applications that are qualitatively superior but reasonably priced at the same time.
With DevOps coming into the picture and enterprises looking at eliminating the possibility of glitches getting into the software, they started adopting a robust quality engineering strategy. This was about making changes in the whole SDLC such that the software application remains glitch-free at the time of delivery. At the same time, it gets updated while complying with regulations and protocols. The whole idea is to create an application that addresses customers’ needs, complies with regulations, remains competitive, and can be scaled or updated when needed.
Why quality engineering?
It has been observed that during the development of software application, some glitches inadvertently become part of the code. This is despite the best efforts of developers. Also, the glitches can often remain undetected during testing and reach the end-customers causing poor experience. To avoid such a scenario, a robust AI-driven software quality engineering process can predict glitches after studying past patterns of coding or real-time data received from logs and reports. Also, a quality engineering strategy involves the development, operations, and management of IT infrastructure by following a high-quality standard. Thus, the overall impact of implementing digital quality engineering is delivering glitch-free applications and their updates within short turnarounds.
Reasons why software QE services are needed
Enterprises are wont to look at the bottom lines with quick releases more than investing in the process to improve the quality of applications. The reasons why most enterprises failed to improve customer experience are:
- Application releases are mostly based on timelines and not quality. In most cases, teams have been found to release untested codes into production.
- Since testing was done manually, the time taken was often more than the development time. This led enterprises to miss turnarounds.
- Teams lacked an overall coherent picture even when they monitored metrics such as performance, availability, and time to deploy, among others.
- The infrastructure running SDLC does not support test automation or DevOps led CI/CD initiatives.
Best practices to implement a quality engineering strategy
Instead of thinking about quality as an afterthought, enterprises should embrace Agile and DevOps led development practices. Such practices bring more collaboration, cohesion, and communication thereby blurring the lines among development, QA, deployment, and operations. To ensure that quality is not compromised in any way, every quality engineering company should follow certain best practices while implementing digital quality engineering.
- Imbibe quality from the beginning: Since quality engineering services entail the changing of workflows, processes, teams, tools, and methodologies, they need buy-in from the management. The management, on its part, can address the resistance to change by explaining the benefits of adopting the new system.
- Implement test automation: Quality engineering is also all about conducting continuous testing with people, technologies, and processes in place. Test automation would ensure repetitive processes are checked for glitches. However, to implement the same, the workforce may need to be reskilled in a continuous environment. Further, when new changes are introduced, automation can provide a safety net to the team and lessen the effort of conducting repeated tests.
- Include cross-functional teams: In future, there would be a need for cross-functional teams where developers and testers would be skilled in each other’s roles. However, this requires the selection of the right technologies and tools.
- Reduce effort in automation: Test automation means one needs to write the right test script to conduct testing and identifying glitches. However, this needs the setting up of an end-to-end automation process using the right tools.
- Integrate AI and ML into automation: By integrating AI and ML into the QE process, teams can analyze and predict the presence of glitches. These can be done by studying real-time data coming with logs, reports, metrics, and events.
Quality engineering needs to be adopted by enterprises as it can predict glitches and help in streamlining the entire process end-to-end. Also, the success of such implementation depends on the culture change wherein the workforce becomes accountable. In a fast-changing business landscape, the time is ripe to adopt quality engineering services and deliver quality products to the customers.
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