“Never stop until the very end.”
The above statement encapsulates the essence of Shift Left Testing.
Quality Assurance should keep up the momentum of testing during the end-to-end flow. This will ensure Quicker Delivery, Quality Product, and Increased Revenue with higher Profitability. This will help transform the software development process. Let me elucidate how it helps.
Traditional Testing vs Shift Left Testing
For several decades, Software Development followed the Waterfall Model. In this method, each phase depends on the deliverables of the previous phase. But over time, the Agile method provided a much better delivery pattern and reduced the delivery timelines for projects. In this Software Development model, testing is a continuous process that starts at the beginning of a project and reduces the timelines. If we follow the traditional way of testing after development, it eventually results in a longer timeline than we imagined.
Hence, it is important to start the testing process parallel to the development cycle by using techniques such as ‘Business-Driven Development’ to make it more effective and reduce the timeline of delivery. To ensure Shift Left Testing is intact, AUT (Application Under Test) should be tested in an automated way. There are many proven Automation Testing software available in the current world of Information Technology which help better address this purpose.
End-to-End Testing Applied over Shifting Left!
Software Testing can be predominantly classified in 3 categories – Unit, Integration and End-to-End Testing. Not all testing correspondingly shifts left from Unit test to System test. But this approach is revolutionized by Shift Left Testing. Unit Testing is straightforward to test basic units of code, End-to-End Testing is based on the customer / user for the final product. But if we bring the End-to-End testing to the left, that will result in better visibility of the code and its impact on the entire product during the development cycle itself.
The best way we could leverage ML (Machine Learning) and achieve a Shift-Left towards design and development with testing is indicated by continuous testing, visual testing, API coverage, scalable tests and extendable coverage, predictive analytics, and code-less automation.
First Time Right & Quality on Time Shift Left Testing not only reduces the timeline of deliveries, but it also ensures the last minute defects are ruled out and we get to identify the software flaws and conditions during the development cycle and fix them, which eventually results in “First Time Right”. The chance of leaking a defect is very less and the time spent by development and testing teams towards fixing and retesting the software product is also reduced, thereby increasing the productivity for “Quality on Time” aspects.
I would like to refer to a research finding by the Ponemon Institute. It found that if vulnerabilities are detected in the early development process, they may cost around $80 on average. But the same vulnerabilities may cost around $7,600 to fix if detected after they have moved into production.
The Shift left approach emphasizes the need for developers to concentrate on quality from the early stages of a software build, rather than waiting for errors and bugs to be found late in the SDLC.
Machine Learning vs AI vs Shift Left Testing There are opportunities to leverage ML methods to optimize continuous integration of an application under test (AUT) which begins almost instantaneously. Making machine learning work is a comparatively smaller feat but feeding the right data and right algorithm into it is a tough task. In our evolving AI world, gathering data from testing is straightforward. Eventually making practical use of all this data within a reasonable time is what remains intangible. A specific instance is the ability to recognize patterns formed within test automation cycles. Why is this important? Well, patterns are present in the way design specifications change and, in the methods, programmers use to implement those specifications. Patterns follow in the results of load testing, performance testing, and functional testing.
ML algorithms are great at pattern recognition. But to make pattern recognition possible, human developers must determine which features in the data might be used to express valuable patterns. Collecting and wrangling the data into a solid form and knowing which of the many ML algorithms to inject data into, is very critical to success.
Many organizations are striving towards inducting shift left in their development process; testing and automation are no longer just QA activities. This certainly indicates that the terms of dedicated developers or testers are fading away. Change is eventually challenging but there are few aspects that every team can work towards to prepare to make this shift very effective. It might include training developers to become responsible for testing, code review quality checks, making testers aware of code, start using the same tools, and always beginning with testability in mind.
Shifting left gives a greater ability to automate testing. Test automation provides some critical benefits;
- Fewer human errors
- Improvised test coverage (running multiple tests at same time)
- Involvement and innovative focus of QA engineers apart from day to day activities
- Lesser or no production defects.
- Seamless product development and testing model
Introducing and practicing Shift Left Testing will improve the Efficiency, Effectiveness and the Coverage of testing scope in the software product which helps in delivery and productivity.
About the Author –
Riyaz heads the QA Function for all the IP Projects in GAVS. He has vast experience in managing teams across different domains such as Telecom, Banking, Insurance, Retail, Enterprise, Healthcare etc.
Outside of his professional role, Riyaz enjoys playing cricket and is interested in traveling and exploring things. He is passionate about fitness and bodybuilding and is fascinated by technology.