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5 key Practices to Transform and Align Quality Engineering Capabilities

The last two years have shown a dramatic acceleration in digital adoption and the way technology can be leveraged by business for growth and transformation. For fast paced and integrated organizations, technology holds a pivotal position in defining new ways of IT delivery with superior customer experience. Quality expectations of modern IT delivery are making testing strategies more technology focused, and engineering driven. Modern Application Testing and Quality Engineering has greater focus on automation, next generation technology and non-functional aspects. Testing and quality engineering today is becoming very tightly integrated with the development process, enabling visibility to additional opportunities to accelerate the development cycle through AI and other technologies.

Following are the 5 key practices that have and can transform quality engineering capabilities:

1)   Continuous Automation

Continuous quality demands automation to be well integrated and far reaching extending across upstream and downstream phases. Integrated automation coupled with continuous monitoring of quality parameters across the lifecycle and corelating them intelligently enable proactive investigation and remediation advisory. There are 3 fundamental strategies that have matured automation to the next level -

  • Strong automation foundation across IT delivery cycle covering traditional testing, code quality, technical debt analysis, environment readiness, service virtualization, test data management, security validations, performance engineering and monitoring.
  • Democratizing the consumption of automation by introducing modularization, reusable scripts & components reducing the efforts on script creation and maintenance by multiple teams, leveraging automation marketplace and self-service BOTs enabling faster feedback on quality.
  • Intelligence in Automation to amplify automation benefits such as improved code quality, advanced defect analysis, bi-directional issue tracing for early prediction and remediation.

2)   Predictive Insights using AI/ML principles

Technology advancements in AI has enabled computer vision-based image classification, object detection, semantic segmentation, OCR, image analysis, etc. AI can be leveraged in quality engineering strategies to assure infrastructure connectivity and scalability, data persistency, deployment quality, agility, resilience, observability and control and security validations. Enabling quick and reliable quality assurance of such varied, distributed, and integrated systems that is based on real world modelling requirements, building extensive training and validation datasets, hyperparameter optimization & continuous monitoring is essential.

3)   Non-functional engineering focus

To build and deliver high performant, scalable, resilient & reliable applications, a holistic performance and reliability engineering methodology needs be established. This enables smart balancing of shift-left and shift-right engineering interventions. A proactive approach that brings performance and chaos engineering strategies as part of the delivery pipeline needs to be established. Observability and chaos engineering tools combined with cloud-based performance testing tools will help in ensuring enterprise applications are validated and certified for its performance, scalability & reliability characteristics. A smart balancing of shift-left and shift-right engineering interventions supported by fail-fast culture will ensure an early feedback loop to the developers.

Engineering strategies are required to prevent business disruptions due to IT failures by entrenching strong capabilities in DevSecOps and site reliability engineering (SRE) practices. Establishing single pane of quality with automated correlation insights and hotspot trends helps in effective root cause analysis & failure impact analysis. This in turn reduces the MTTR and increasing system uptime. AI-fuelled infrastructure capacity prediction dashboards & SLO/SLI dashboards facilitates effective capacity management & SLA adherence.

4)   Boundary-less collaboration  

Democratized platforms, processes and open-source models coupled with globally distributed teams and federated ways of working have given boost to cross functional and real-time collaboration. This promotes transparency, helps in developing modern and quality techniques, re-usable assets, code snippets, and plug-in utilities. Open-source technology has a key role to play in this transformation. One of the classic advantages of boundaryless collaboration is to enable crowd participation across the globe bringing regional contextualities, persona diversity and realistic environmental variations. Quality engineering architects can better strategize through communities with SME feedback, near-real data samples.  

5)   Workforce transformation

Skill composition strategies are being heavily revamped to uniquely align with new ways of working, securing right mix of skilled workforce (T-shaped, Pi-shaped or Comb-shaped). A Tester is now a full stack Quality Engineer with an optimal mix of competencies suiting to the context. Some of the competencies that are shaping modern quality engineering includes automation, Java/python development, service virtualization, cloud, performance, resiliency, and vulnerability. New methods such as site reliability engineering, AI model testing have also become integral part of skill taxonomy.


Software Testing has undergone a fundamental change by evolving into an engineering function and is now an integral part of the ‘Organization for the Future'. Modern Quality Engineering offers a great potential for leveraging next-generation technologies and engineering practices to meet the IT demands of highly integrated and fast-paced organizations. Key practices that will transform quality engineering capabilities include intelligent, continuous, and automation-led quality that exploits cognitive and self-learning capabilities, an early focus on non-functional aspects, organic boundaryless collaboration, and securing the right skills at the right time.



Manish Potdar, Head of Quality Engineering, Larsen & Toubro Infotech (LTI)


Published Friday, June 10, 2022 7:31 AM by David Marshall
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