Is Traditional QA causing bottlenecks?

Is Traditional QA causing bottlenecks
  • Posted By: admin
  • Posted On: April 7, 2020

Quality is the key-word for gaining customer approval and satisfaction and is hugely emphasized by experts in the world of software development. To meet the requirements of the current age and maintain a top-notch quality and ROI, organizations need to upgrade their software testing tools and QA practices to a whole new path in terms of market expectations, such as the implementation of complete test automation in a DevOps-based end-to-end testing scenario.  

Companies aim to gain the market value for their brand by agility in delivering the product rapidly and providing the same amount of after-support. They are always eager to gain a competitive advantage in terms of continuous delivery and innovation. This is where the idea of eradicating the existing QA challenges by upgrading the methods to provide faster time-to-market stems from.

Customers of this age have gotten used to technology based on AI and data science so much that they expect the provided solutions to be nothing less than perfect in terms of fast results, frequent updates, and availability as per requirements. Testing and development methods cannot be compromised if organizations aim to exceed expectations by achieving continuous delivery. The solution: include newer trends and methods in development and QA phases and be updated in the industry.

How is Traditional QA a Bottleneck?

‘Speed’ has always been a bottleneck in the software testing scenario, and the QA teams have been integrated with DevOps and Agile to intensify the test approaches and implement CI/CD, but according to statistics, the adoption of QA with DevOps is still slower among many companies. The World Quality Report 2017-2018 by Capgemini, says that the rate of QA adaptation to DevOps and Agile among companies is only 45%, while almost one-fifth are still underway in grasping the methodology in their process. The remaining 35% have not considered changing their old methodologies yet.  

As the traditional QA methods aren’t adoptable in coordinating with all the other stakeholders and to end-to-end testing cycles, which helps in the elimination of root causes of errors right from initial stages, there tend to be shortcomings with delivering according to the current market speed and adaptability to latest trends.

Challenges Faced by QA Practices

Reusing the functional test data and preparing test cases can be of more use to streamline the QA process than just using different testing techniques over various end phases after product development. Quality Assurance is a quality process that was previously denied the attention it required until recently over the last few years and now it’s being used as a key to faster time-to-market and higher ROI

Let’s looks at some of the main challenges QA teams face today:

Test Automation

Putting manual effort into testing from one stage to another will employ a lot of resources and time, which becomes a hardship for the delivery pipeline to stay in the market delivery performance level. Automation testing uses fewer proper scripts, preplanned cases, and resources to reduce effort, cost, and time. Automation of the entire quality is still impossible, but organizations are setting it a goal to automate at least half of the entire process to reduce manual involvement to a minimum which reduces time-consumption and human errors.

Scalable Testing

Scalability is a popular issue, with the unfathomable amount of collected data involved. With billions of people using every single software and application available in the market, the amount of programming and coding for every scenario and the amount of algorithms involved are humongous, based on the inputs that are gathered often. Scaling the product to go up is a big challenge as most of the softwares are unable to handle the expectations of large data. Rigorous testing with frequent recycling of cases and automating most of the testing requirement is much needed to tone the process to meet the level of coding that can handle this huge amount of data. This is based hugely on the method of trial-and-error and can be adopted by turning quality assurance to quality engineering, which is designed to test hundreds of scenarios.


The fate of a product depends on the time-to-market, like continuous delivery and the need to produce the product in the market in a shorter period with support post-release with lesser turnaround time is a performance factor. To increase the turnaround speed and product release and to improve the detection of errors before production, end-to-end testing cycles with complete automation boosted by frameworks have to be in place. Methods of end-to-end testing like quality engineering use tools like Selenium and cloud testing integrated with other stakeholders in a product development cycle to complete time-consuming testing phases in a shorter period, by testing early and often.


When agility and quality become a main concern in the industry, it’s better to deliver a product as per expectations without escaping bugs and in a shorter time. The product performance after its release into the market is based solely on the amount of scalability testing and performance testing it has gone through, and quality engineering takes it to the expected level by continuously performing end-to-end testing using proper software testing tools.