According to a Harvard Business Review survey among 250 business leaders, 66% of respondents felt that their company's future depends on software quality. As per Nagarro's internal surveys, 78% of enterprises strongly believe they need more efficient QA organizations to respond to disruption.
Quite evidently, Quality Assurance (QA) is an essential part of the enterprise software lifecycle. But what does the future hold for QA? Today, QA needs significant development to meet the evolving requirements in the following aspects:
Gartner has predicted AI engineering technologies and hyper-automation to be the major technology trends for 2022. Many organizations are still cocooned and traditional in their automation approach. We must explore how best we can use hyper-automation for testing.
2. Design thinking
Most QA organizations or teams lack "design thinking" as they typically follow a functionality-centric approach instead of a user-centric one. It is not uncommon for even products with good functionality to not manage decent app store ratings. What can we do to develop a user-centric approach to quality engineering?
3. Lean QA
There is an absence of lean QA. Too much time is spent in the testing phase of the development lifecycle without enough tangible or measurable impact. Testing and development are still highly decoupled, leading to a lot of avoidable practices such as handover and unnecessary alignments.
4. Proper, process-based knowledge transfer
In many enterprises, QA is people-dependent, with the critical know-how or intelligence about any aspect remaining concentrated among a select few individuals. The organization can lose crucial information in the process as such knowledge is often shared at an interpersonal level than at a process level. Knowledge transfer is a key area that must be improved as individuals leave or take on other roles.
5. Continuous improvement
Perhaps the biggest hurdle in modern QA teams is the lack of continuous improvement. The secret behind any world-class software is a continuously monitored and adaptive quality assurance system backed by robust standard operating procedures. Yes, most enterprises typically have their SOPs, but there is always room for improvement, especially ensuring that the recommended practices are adopted and monitored correctly.
Given these challenges, how can QA grow its prominence in today's rapidly changing software ecosystem? Let us find out!
Testing the uncertainty: How will AI and autonomous systems impact QA?
Gartner predicts that more than 50% of organizations will compete using advanced analytics algorithms globally, with many of them being AI-based.
Traditional QA methods and approaches are aligned with - surprise, surprise - traditional applications, which are deterministic logic-driven. This means that for each determined input, there is a determined output. So, one can predict the output for any given input.
On the other hand, AI-based models are probabilistic logic-driven. This means that for a given input, the output is unpredictable. The output of an AI-based model depends on how the model was trained. What makes AI testing special (and even fascinating!) is that engineers generally know how to build or train an AI model but cannot predict the output. The QA teams need to apply new methods and approaches to test this probabilistic logic.
What does all this imply for you? The above points bring up a few questions that you must consider:
- Does your team have a "design thinking" mindset?
- Is your testing fully optimized and lean?
- Is your testing technically focusing on the future and not just the present?
- Do you have an insufficient balance between manual QA and test automation?
- Do you have a traditional approach to automation?
- How can you always see the current proven progress and quality level?
All these are very pertinent queries. Asking these questions can provide high-level pointers to develop your QA readiness for the future. Nagarro's QA experts can share their insights on such queries and provide a 30,000-foot view of the next quality assurance level integral to every modern enterprise.
Next level QA: What is it?
The next level of quality assurance combines the best of "Design Thinking," "Mercurial working," "Hyper-automation for testing" and "Information security" by underlining processes in each of these areas as a mix of being reactive, organized, and adaptive. Let us look at each aspect in detail.
Hyper-automation for testing – Intelligence enabling
How can AI support automation beyond the classical automation of test case execution? We can make QA-related processes quicker, more comprehensive, and more scalable by using intelligent solutions.
Examples are self-healing automation frameworks, intelligent analysis of application logs, automatic root cause-based clustering of failed test cases, and test case writing using AI. Nagarro's AI4T framework takes test automation to a higher and more intelligent level, immensely boosting your team's productivity.
Customer experience – Design thinking
Design thinking is about how a customer uses and perceives the product. Their expectations drive customers' perception of quality based on their experience. User experience is a perception and can be managed by a quality engineering approach that focuses on the end-user.
A user-centric approach can never deliver a functionally buggy system because this would also fail to meet user expectations. User-centric testing ensures that testing is done keeping the end-user experience in mind and that the requirements are validated to meet user expectations. Instead of product functionality, user experience drives testing and test design and prioritizes the overall quality engineering thought process. This approach is also known as total experience QA.
Connected enterprise – Integrated working
Business models are constantly changing to seize new opportunities, supported by new technologies such as:
- intelligent devices – sensors are a good example
- autonomous components or smart glasses connected via different protocols
- performing a business function, such as training via AR, guiding using VR including streaming and transfer to remote guides, experts, or assessors.
Quality engineering of the future will take a dramatic shift to cater to such testing needs.
Cloud adoption – Scalable solutions
On-prem solutions are expensive, less scalable, and complex to maintain. Replicating on-prem infra or environment is also quite expensive. This is where the cloud comes in, providing a cost-effective solution to such bottlenecks. The approach to quality engineering will need to shift by focusing on scalability, availability, and integration with automation solutions.
Besides the above-mentioned changes will become the pillars of the next level of quality assurance, some things will never go out of fashion. It will always be important to remain:
- adaptive and lean
- responsive to dynamic needs
- aligned with fast iterative development and releases.
Creating a lean testing process starts with discovering waste in the current QA and testing lifecycle and eliminating or minimizing them. Anything that does not add value to business/customers and quality is a "waste." The most obvious example is replicated or indirect communication of topics.
Securing customer data and applications is crucial, especially where data leaks or modifications are unacceptable. Data privacy practices and policies will also be essential to secure customer data and systems. Given the distributed nature of enterprises and their customer interactions nowadays, how can you ensure that the product being developed is secure? Many organizations are already building teams focused on security consulting accompanying the development process, security & penetration testing, and information security teams to fight attacks. Gartner predicts that "Cyber Security mesh" will be among the key technology trends of 2022.
Achieving the next level: How to get there?
The recommendations we discussed are from Nagarro's 360-degree QA approach. The 360-degree approach is an agile and pragmatic method to deliver measurable improvements in design thinking, faster time-to-market, cost reduction, agility, and efficient IT operational processes, helping businesses achieve quality transformation. Our proven approach has been developed and improved over the last 5 years and has been successfully implemented over 50 times.
Another basis for this approach is the industry's leading testing maturity & process models TMMI®, TestSPICE, and TPI®. Our 360-degree approach includes:
- Comprehensive assessment checklist to evaluate the existing level of quality engineering
- Future-of-QA best practice catalog to assess the readiness for short & mid-term horizon's topics
- State-of-the-art model, based on TMMI and TPI to determine the QA organization test maturity level
- Recommendations for continuous improvement and raising the level of the test organization
- If urgent topics occur, we usually structure the recommendations into two backlogs for a fast lane and sustainable lane