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Quality Engineering (QE) for continuous delivery through integration

Quality Engineering (QE) for continuous delivery through integration

The blog article elaborates on the DevOps edge and how its outcome oriented approach enables Continuous Quality Engineering to meet the growing demand for quality software applications in shorter time

DevOps is an umbrella term for a combination of cultural philosophies, practices and tools that increases an organization’s ability to deliver applications and services at high velocity. It manages to provide continuous delivery with high software quality, something it does by evolving and improving products, faster than the traditional software development and infrastructure management processes.

DevOps at its core delivers quantum results because of the frictionless collaboration between Developers and operations teams.

DevOps breaks down silos that arise out of hierarchical organizational models. In a typical IT team, the certified professionals would range from Linux/Unix, Windows, Networking, Storage, Security and Databases. In a DevOps set up, the barriers between such teams, Operations, and Testing teams does not arise, as the linear and discrete method of passing over tasks is eliminated. One happy outcome of this closer IT- Business collaboration is the shared empathy for the end customer. Additionally, the shorter feedback loops mean higher efficiencies, thereby freeing up resources and time for other innovations.

As the rate of change of business accelerates, companies are less able to predict the directions of change. In this unnerving climate, DevOps brings an agility that responds to the industry-market changes on the fly. In fact, the legendary DevOps velocity specifically emphasises reduced ‘time-to-market’. Be it APM, build automation, configuration management, release automation, or service virtualization among other DevOps tools – all are focussed in bringing new revenue generating services online faster.

In times where software industry lives by the adage: ‘release fast or die’, it is DevOps that accelerates the speed of software delivery by an end-to-end automation.

It is a prudent thought to consider: In response to the DevOps edge, how is Quality Engineering as an industry discipline recasting itself?  

Let’s answer that with some context first.

As Digital revolution continues to push organizations to reinvent their business models, it also causes the rise of distributed technology, adding in layers of complexity. Businesses today understand that for unmatched customer experience ‘superior quality at speed’ is key.

Hence it is imperative for leaders in Quality Engineering to bring a double edge to their Digital-age QE competency: Cutting edge Dev-Test-Ops approach and real time insight driven dashboards, that uniquely anticipates typical pitfalls and proactively manages the choke points through various contextual solutions. In this regard, the DevOps edge helps tremendously. For strategically inclined organizations, it makes possible by adopting continuous quality techniques, a culture of “95% or more QE automation”.

Quality Engineering (QE) for continuous delivery through integration

Knowing fully well, that Quality Engineering’s primary focus is continuous quality, how do organizations across industries bring this to reality?

We have spoken about DevOps contribution earlier and will now expand on the specific “how’s”

  • Enabling Agile/DevOps adoption through continuous testing(the ‘Big Tech’ way)
    • Facilitate Agile/DevOps based approaches, so as to offer early engineering proficiency to detect pitfalls/defects
    • Plug in tools and accelerators across the CI/CD pipeline in a DevOps scenario
    • Enable Dev-QA integration through right set of development tester talents (SDET’s based propositions).
    • Institute production and test failure feedback mechanisms into cycle at every stage in order to know the code quality and functionalities meet business requirements.

Next, the ability to consciously contextualize the customer’s need in relation to the market speeds, is as critical as the customer and product journeys that is based on test design and execution accelerators.

No matter the execution approach, the end goal remains: Zero Defects. These are achieved by:

  • Reinforcing forces of early engineering across the entire phase of customer and product journeys
  • Maintaining code quality by reducing defects/ leakages using proven tools, accelerators and frameworks across the phases of code design, development, solution prototyping and launch

A best-in-breed QE organization prides in its ability to deliver ‘QE at Scale’.

This is made possible by integrating QE platform with real time insight driven dashboards for effective decision making. The augmentation of this platform is through DevOps enablement.

What does a QE-at-scale philosophy entail?

Along with customers receiving complete engagement transparency, the QE at scale principle, assists in resolving pitfalls earlier in the development cycle. Additionally, the continuous quality rigor necessitates repeated in-sprint testing across the whole yards of functionality, digital, security, performance, compatibility and usability. Finally, the QE for continuous delivery model relies heavily on the organizational ability to work through ready-to-deploy API/micro service models, framework, and reusable test libraries etc.

In sum, today Quality Engineering engine delivers if it is successfully integrated on the twin premise of – engineered for speed and powering QE at scale.

And how does continuous delivery happen through this integration?

The answer is that customer journeys are orchestrated by conscious contextualization enabled through the organization’s comprehensive competencies. The journey is then steered with cognitive computing methods (AI, ML, NLP, RPA etc.), in the shape of tools, accelerators and frameworks.

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The 3-Way Cognitive Quality Engineering Eco-system

The 3-Way Cognitive Quality Engineering Eco-system

Evolving distributed digital technologies have raised the expectation levels of quality assurance (QA). The QA role has significantly shifted towards early quality engineering (QE), aided with blazing speed at an unprecedented scale.

As banking industry and services keep pace with the digital revolution, the need for cognitive ways of maintaining Continuous Quality is of paramount essence. In order to create competitive advantage, and provide specific services as per customers’ expectations, banks have to maneuver their testing and QA towards Cognitive Quality Engineering. Intelligent automation with the forces of Artificial Intelligence (AI) technologies such as SR, ML, DL, NLP etc, can be a significant game changer for accelerating business growth and service delivery. These technologies live and feed on the right volumes of data to reflect the right results. As you feed more, the accuracy levels become better and sharper.

According to the World Quality Report 2018 (WQR2018), for the first time ever the top objective of QA and testing strategy is “end-user satisfaction”. Traditional approaches of test automation will soon perish as cognitive-led automation, with its superior quality performance, takes over.

Infusing Cognitive QE

The 3-Way Cognitive Quality Engineering Eco-system for Improving design, development and operations
The 3-Way Cognitive QE Eco-system for Improving design, development and operations

It is needless to say that low levels of automation adversely affect quality engineering and software testing efficiencies. And, banks are well aware of this. Historical insights drawn from customers’ transactions, domains, service feature categories, commonly seen scenarios, occurrences, usage and patterns, can formulate an intelligent algorithm to predict and resolve recurring pitfalls much faster.

Banks could infuse these intelligent algorithms at a continuous pace, for assuring their software releases and future initiatives. The shift-left (i.e requirement and design stages) and shift-right (i.e. development and post go-live stages) scenarios can be engineered using Cognitive QE techniques for better efficiency. This will help maintain continuous quality across the entire software lifecycle.

The 3-way Cognitive QE ecosystem is built to offer an insights- led, proactive, decision-making intelligence to resolve pitfalls in the smartest ways.

  • The first of these is the Customer: Start capturing your target customer requirements a bit deeper using their historical data transactions to understand their needs, patterns of usage and failure points. This could vary across every geography and types of product. Look at the customer segments and study the customer journey and relationships, transaction volumes/logs, learn the channels and touchpoints for connecting and transacting and pay close attention to customer experience levels. This in-depth comprehensive study would fetch tons of meaningful insights for designing a user friendly and sustainable system. Now that we have studied the customer, the next step is to place these meaningful insights in the right buckets of cognizance.
  • The knowledge base would be the key theme for setting the right accuracy standard. The data analysis drawn out of the customer study, requires its placement in the right domain groups. The real-time data sets and logs including functional defects and system failures, provides the scenarios for testing. These data sets are to be maintained periodically or else it can adversely impact the target outcome.
  • The third component is Modeling Algorithms. The data collected as part of knowledge base is fed into modelling algorithms to analyze pass/fail scenarios, relationships and usage patterns. It paves the way to build predictable models for testing the system based on the insights gathered and learning over a period of time.

The 3-Way Cognitive QE ecosystem could be run over a continuous loop, to test and validate software across the various stages of design, development and support. Functional and non-functional validations can be much more efficient, with self-based learning and healing tactics. With a real-time monitoring dashboard in place, typical pitfalls can be arrested much earlier before they manifest. Also, continuous improvement measures could be added for enhancing your cognitive QE approach. Banks can expect benefits of increased operational efficiency and better productivity rates. Pushing features will also help with faster time to market. In addition, better accuracy and customer satisfaction are bonuses not to be underestimated.

Cognitive QE techniques today can not only gather and analyze data but also use push technology to offer customers what they are looking for – based on their history. To make the customer’s experience smoother and error-free, cognitive quality engineering is the way banks need to go. It’s smart, intuitive, fast and like the human brain, learns and adapts as it gathers more information and insights.

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Top 9 things to follow for Customer Experience Testing

Top 9 things to follow for Customer Experience Testing

The evolution of technology has bought huge transformation among organizations as well as raised the bar of customer expectations.

And in this digital era, wherein everything is changing rapidly, organizations need to have a firm grasp on how to utilize the digital universe to reap maximum benefits. And, when it comes to the banking sector the expectation and experience go hand in hand.

This is one of the primary reason, which triggers banks and other financial institutions to look for innovative ways to improve customer experience and in turn improve the overall conversion.

A Walker study found that by the end of 2020, customer experience will overtake price and product features, as the key brand differentiator. So, it is now widely acknowledged that customer experience truly has the potential to make or break.

Banks are heavily focusing in streamlining customer journey’s. So, where do you begin is the next big question?

Expectation Mapping

Today’s banking industry is exploring ways to get into the minds of their customers by introducing various channels and apps which in turn help them in getting one step closer to their target customers.

They shifted their focus on unfolding all touching points that are necessary to create the ultimate customer experience. Starting from the website, customer support to apps, these are areas where real opportunity exists.

Working on similar lines, “Gartner predicts that by 2021, 75% of software providers will rely on insights embedded from software usage analytics, to aid major product decisions and measure customer delight”.

But the question still remains “Is there a way to understand and measure customer experiences”? Or How do you test customer experiences (CX)?

All these questions have a single approach. It requires a deeper understanding of the organization processes, customer expectations, and product lifecycles.

We have seen the way digital products have evolved immensely, therefore your focus should be towards building the “direct-to-customer” model. To achieve this, an effective customer experience testing strategy should be designed, in line with user goals and business objectives. Let us understand it in detail.

Top 9 things to follow for CX testing

  1. Gather insights through usage patterns from Production Environment: The whole point of gathering data is to create usable insights that drive changes. To begin with, customer experience architects should create the entire customer journey blueprint. They need to study the end-to-end experience the customer has and accordingly streamline the internal process.
  2. Functionalities easy to understand and access: Before going live it is important to undergo for usability testing to find out whether your application is customer- friendly or not. Organizations need to tap into real-time data to understand the consumers ever-changing behaviors and incorporate the same for streamlining their CX journey.
  3. Validate critical functionalities in multi-platforms and cross-browsers: To make sure that your customer has a great experience while using your application without encountering any issue, it is important to do cross browser testing. It helps in verifying whether the application is compatible across different platforms and browsers.
  4. Involve and test scenarios for different customer segments: To have an effective CX strategy, organizations must test different customer behavior which includes functional and non-functional requirements and aspects. In addition to this, it is also essential to address human behavioral elements such as culture, psychology and emotions It will help in prioritizing the different testing needs- functional, performance, security, access, etc.
  5. Monitor and test the App/System behavior under peak load scenarios: No matter how many tests you have run, once your application is ready to go live, your application should be analyzed further, under peak load scenario’s. And this can only be done through Load Testing. It is a process of putting stimulated demand on application to check its behavior under various conditions.
  6. Provisions to capture user/customer feedback in regular intervals and on periodic manner- As the digital world evolves with the advent of new channels or devices, so does customer preferences. That’s why it is important to capture customer changing preferences and implement the same on regular intervals. So, that your application performance meets expectation.
  7. Interfaces with other social apps and extract/download capabilities: A/B testing, also known as bucket testing, is a standard way to evaluate user engagement or satisfaction. It is widely used to check compatibility with social network sites such as Facebook, LinkedIn, and Twitter to make data-driven decisions. So, before going live, make sure that your front-end and back-end systems are well integrated with seamless coding for better user experience.
  8. Availability of features across all channels/customer touchpoints: Your application will be only successful if it touches all customer touchpoints. These are integral points, which customers use to interact with your brand, product, service, etc. Carefully studying these touchpoints can help an organization in designing and giving better user and customer experiences.
  9. Accessibility to Support & Help functions – Lastly, you should pay high dividend towards testing their focal point of customer connect such as chatbots, digital customer support assistants, online help options etc. These platforms need to go through a comprehensive course of usability testing.

The Road Ahead

The digital transformation in the BFSI segment is bound to hit $121.7 billion by 2025 as per Adroit Market research. This means the digital world is going to evolve with the advent of new channels and devices. Therefore, the criticality of customer experience testing becomes a prominent subject with a continuous rigor of quality testing. Once you know your customers well enough, you can use that knowledge to personalize every interaction

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