The Need for Quality Engineering Services
With organizations grappling for ways to fulfill the dynamic expectations of customers, and at the same time deal with changes in market dynamics, constant evolution of technology, and increasingly stricter regulations, the expectations from IT teams are being reshaped. With agile strategies and DevOps principles, leading the way, there is also a move to remodel the typical quality assurance (QA)-driven function to one that's Quality Engineering (QE)-focused.
QE not only embeds quality in development of product and processes but also enables effective testing in parallel. The idea is that in the SDLC, the test automation strategy is designed beforehand and then the developers, operations and QE teams, all work together as a single unit.
Pre-automation of test cases à Scripting à Make the executables part of the development process.
Quality engineering drives development of quality in the product as well as processes. This includes quality, maturity of the quality team itself, but it’s also a cultural shift within the teams. A concept that requires cultural shift within teams, Quality Engineers focus on the quality right from the requirements stage by integrating agile as well as Test-Driven Development (TDD) and Behavior-Driven Development (BDD) processes to define requirements that are meaningful to the business developers and the testing teams.
Pillars of QE transformation
QE across the Lifecycle
Quality Engineering across the IT lifecycle helps deliver the committed business benefits across various technology initiatives. Re-engineering QA function into an integrated unit with development enables to deliver sustainable quality and improved velocity, in line with business requirements by shrinking the post development test phase and release cycles.
This software paradigm, called shift-left emphasizes on moving software quality activities to the beginning of the software development life cycle during conceptualization and development.
A crucial norm for “Shift Left” is to write testable code that is unit verified, and build quality at a component level up to integration level. Essentially this enables efficient localization of a problem and ensures that individual elements are working before a large amount of software is integrated.
With QE, the emphasis is on creating a lot of automated test cases closer to the development code. This enables rapid development or verification very easily in a heightened manner, in order to complete validations very quickly unlike legacy software development lifecycles, where there is more manual testing done and less emphasis on unit tests.
In the new model, as a lot less time is spent on manual testing, it provides tighter integration of automated test cases closer to development code.
Adding integration and end-to-end testing to a pipeline can enable the leap from Continuous Integration to Continuous Delivery (CD). Being effective, long term, with Continuous Delivery requires an entirely new set of features -- testability features -- built into the architecture itself, along with other changes to the way software is built. Without these changes, organizations typically struggle to see the benefits that Continuous Deployment and Delivery promise. Organizations looking to move to Continuous Delivery would do well to consider improvements in the following categories, looking for “missing features”, anticipating the cost of building those features, and the consequences of leaving them blank.
Comprehensive management of four key components across the IT lifecycle
Once processes and policies have been configured to adopt this movement to CI/CD-driven Quality Engineering, integrated coverage is essential in order to improve efficiency of the entire QE process through comprehensive management of all the enablers in the SDLC. Traditional QA methodologies are enhanced and utilized to improve efficiency of a QE-enabled lifecycle. For example, full-stack quality engineering teams are being groomed by organizations to ensure quality across all layers of the application using assurance techniques; interface management typically involves effective communication across now-integrated teams on all aspects concerning an interface including requirements, technical specifics, as well as potential problems; data management is often seen practiced in the form of utilization of data to prevent problems and further improve quality, which also leads to the need for sound configuration management to maintain system integrity in the light of changes wrought forth through implementation of application stacks, large data-sets, and multiple interfaces.
Dashboard for Predictive Analytics
Metrics are a way to measure and monitor test activities. More importantly, they give insights into the team’s test progress, productivity, and the quality of the system under test. The metrics need to cover various aspects such as the source code used, the process that is put in place for the custom development, test coverage and the defect removal efficiency. The dashboard for predictive analysis should cover the following:
Changing Mindsets from Quality Assurance to Quality Engineering
Over the years, it has become evident that the old methods were not able to support rapid, zero-defect feature releases on time, and for banking applications which are created to serve thousands of users. And for banking applications which are created to serve thousands of users, it is essential to manage the overall of quality effort for these rapidly scaling applications.
There is an important transition of mindset from Quality Assurance to Quality Engineering to manage the overall of quality effort for these rapidly scaling applications. It means that there is a brand new approach to software testing in the making. Some of the frameworks have made development and quality teams dysfunctional. There are many rejects to QA that have caused a lot of development rework. Result – very few software releases, huge overhead, and loss in market shares. So there’s an immense rethinking and restructuring of all the frameworks, reports, tools, and iterative frameworks.