Business enterprises the world over are grappling with a host of challenges in software development, testing, and delivery. These include meeting rising customer expectations, accelerating to market, keeping up with technological evolution, adhering to stricter regulatory norms, and others. With the advent of Agile, DevOps, and intelligent automation, delivery timelines have shrunk sharply – from months to days. Besides, testing as a continuous activity a la Agile and DevOps has shifted both left and right in the SDLC. Thus, software development, instead of being a QA-driven activity (Quality Assurance), has become a QE-focused (Quality Engineering) one.
Digital quality engineering begins at the planning stage of conceiving an application and creates a continuous feedback loop. This way, it can anticipate challenges and address the unknown. Today, quality is not limited to the mere functioning of a software application alone but to providing differentiated and superior user experiences across the value chain. Moreover, with the emergence of new technologies, the complexity of software applications has risen sharply.
Hence, software quality engineering services must focus on monitoring applications continuously and consistently across scalable platforms. Also, technology abstraction needs to be achieved to move users away from the complex layers underneath the software. Enterprise quality engineering must adapt to frequent shifts in technologies. These may include edge computing, machine-to-machine communication, intelligent automation, and IoT-based datasets, among others.
Key enablers for quality engineering solutions
The success of any quality engineering service depends on a few key enablers as mentioned below:
Shift-left paradigm: The paradigm underlines moving all testing activities to the beginning of the SDLC, especially during conceptualization. It includes writing testable codes that are unit verified, and then quality is built into the code right up to the integration level. This way, localization of an issue is achieved and ensures the smooth working of individual components before a large software suite is integrated. With a robust quality engineering strategy, the emphasis is on developing several automated test cases to enable quicker validation of the code. This is in sharp contrast to traditional QA, where there is more focus on manual testing.
CI/CD Infrastructure: Businesses can establish Continuous Integration and Continuous Delivery (CI & CD) by incorporating end-to-end testing and integration in the SDLC. Further, to be effective in achieving CI/CD, businesses need testability features to be built into their architecture. The CI/CD infrastructure must encompass implementing code base improvements, managing environments, managing features, and adapting processes.
Measuring predictive analysis through metrics: There should be a dashboard containing metrics to measure the results of predictive analysis. The metrics give insights into areas such as productivity, quality of the test system, or a team’s test progress, among others. They cover aspects such as the process for development, source code, test coverage, and others. The dashboard for predictive analysis should cover the following aspects:
- Collecting details on the development, requirements, and QA
- Use of relevant internal and external data
- Predictive models for risks, impact, and defects
- Use a dashboard to reflect the above across the SDLC
Shift from QA to QE: With faster time to market being the norm, software applications need to achieve zero defects. In the traditional scheme of things, this is quite challenging to achieve. So, the approach for software quality engineering services is to incorporate quality engineering in the conceptual stage of design. QE can help applications to be of superior quality even when they are scaled rapidly and cater to many users.
Build orchestration with continuous integration: This would include managing four key components across the SDLC.
- Data and configuration
- Interfaces
- Application stack
Once developers commit a code to a source repository, testing should begin in the right earnest. It means ensuring the code passes unit tests, covering code at an acceptable level, and others. A proper feedback loop should be set up to ensure the code does what is expected. Continuous integration should include API validation, static or dynamic analysis, and security checks. The team should implement quality engineering solutions comprising simulation, virtualization, and emulation to perform optimal testing and achieve quicker development cycles. Integrated coverage helps to improve the efficiency of the entire testing process by managing all enablers in the SDLC. A QE-enabled lifecycle can be achieved by enhancing traditional QA methodologies.
Conclusion
When it comes to ensuring quality for rapidly scaling software applications, traditional quality assurance can come a cropper. Quality engineering encompasses restructuring and rethinking all tools, frameworks, and reports. In the fast-paced testing environments, QE will focus more on achieving alignment with business purposes. This would mean building self-adapting and self-learning systems backed by advanced analytics and machine learning.
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