Creating AI-Enhanced Test Systems: A Scaleble and Reliable Cloud Quality Solution by Jessy Christadoss

With the increase in cloud applications and services, organisations need to validate systems safely and at scale and be successful with QA, quality and compliance – without compromising any quality standards. Jessy Christadoss, a senior quality engineer with more than 15 years of experience, has leveraged research frameworks as an influence to support engineering practices. Considering her work towards automation, AI-enabled frameworks, and intelligent orchestration capabilities, Jessy has developed real and feasible quality engineering solutions.

AI-Agent Driven Test Environment Setup & Teardown for Scalable Cloud Applications

"AI-Agent Driven Test Environment Setup and Teardown for Scalable Cloud Applications", published in Vol. 4 No. 3 (2025): Digital Transformation in Education: Emerging Technologies and Pedagogical Shifts, discusses how intelligent agents can automate the whole lifecycle of test environments completely. The paper covers provisioning, monitoring and tearing down environments on demand, based on workload and waste absorbed, while drastically reducing human interaction.

The framework supports ephemeral environments in which, once the tasks are completed, the environment is removed (or destroyed). In the past, environments were painstakingly constructed as test processes relied heavily on human intervention and centralised test labs. Now, clients are using a staging framework along with business process management.

Jessy recalls meetings with clients where Jessy would explain the need to have governance controls built into the life lifecycle stages of environments to show that environments can operate barrier-less from policy and achieve velocity. This brings about economic, speed, and agility benefits to engineering team members. Jessy emphasised that team members can test new features in parallel amidst the existing constraints. Modern companies can now dynamically change the infrastructure with high-quality, where quality has now evolved from being a benefit to a necessity; Jessy's research paper is an interesting roadmap to fully achieve this opportunity.

AI-Based Automated Load Testing and Resource Scaling in Cloud Environments Using Self-Learning Agents

"AI-Based Automated Load Testing and Resource Scaling in Cloud Environments Using Self-Learning Agents", published in 2024, Journal of Artificial Intelligence General Science. This document addresses a longstanding challenge in managing cloud environments, especially the requirement to manage performance against unpredictable usage. Instead of simple predefined static load scenarios as part of the testing approach, this new method uses self-learning agents that are able to develop realistic usage patterns, identify bottlenecks, and scale resources in real time. The interesting thing about this research is the focus on predictive compared to reactive scaling. In the research study, the system is using reinforcement learning, and therefore, agents are constantly adapting their learning and becoming good at predicting the conditions where performance might degrade.

Jessy applied approaches from quality assurance, such as continuous assurance, risk-based validation, and regression tests to ensure that the system not only scaled effectively but did so without compromising system integrity. The implications of AI-enabled adaptability and quality assurance amortisation are significant, as this approach typically reduces development costs and integration efforts to achieve better quality in large-scale projects like SAP implementation.

Risk-Based Test-Case Prioritisation Using PageRank on Requirement Dependency Graphs

Jessy built on previous studies and envisioned future work by writing a research paper entitled “Risk-Based Test-Case Prioritisation Using PageRank on Requirement Dependency Graphs”, published in Vol. 7 (2023): Journal of Artificial Intelligence & Machine Learning Studies. The study introduced a new method to address the challenge of efficiently testing large test suites when committing to automate every test case is not practical. By leveraging PageRank, the reference algorithm for ranking web pages, the study showed a way to discover the most critical requirements at the "top" of the corresponding dependency graph, and reorder test cases based on the rank scores derived from its PageRank taxonomy.

What's exciting about this work is the potential to shorten test cycles and improve efficiency. Jessy has shown that test priority can be ranked mathematically, which would lead the QA teams to discover high-value defects more quickly and safeguard features that impact businesses at a greater level. The paper identified key metrics and documented cases where regression timelines were significantly reduced and organisations reported significant improvement in discovering high-severity defects earlier. In Jessy’s study, the challenge lies in how the academic model is balanced with practical application, shares how it transforms QA practices and demonstrates how it can enhance the existing business processes, tools & techniques, and enterprise-wide QA pipelines.

Bridging Research with Practice

These three papers bring Jessy Christadoss's research journey to a full circle. Each article highlights quality assurance as a more proactive and adaptable practice, being a blend of academic insight and engineering practice. Jessy's publications are not only academic studies; they are practical implementations based on real-world experiences and ready to be used in Production.

For organisations wrestling with the complexities of cloud platforms, Jessy's research addresses the common hurdles. Intelligent agents that can set up environments eliminate operational friction, self-learning systems for load testing create resiliency, and risk-based prioritisation guarantees high-value validations first. This relationship between Jessy the practitioner and Jessy the thought leader in quality engineering continues to highlight their unique position as a valuable nexus of innovation and a strong voice in quality engineering.

About Jessy Christadoss

Jessy Christadoss is a senior quality engineer, researcher and thought leader with over 15 years of experience across industries like mobile, web, OTT, automotive, and cloud. Jessy has experience in automation frameworks & tooling, AI-enabled test implementations, and CI/CD integrations, effectively leading quality assurance organisations with a focus on scale, compliance, and efficiency. Jessy's peer-reviewed work includes researching intelligent test environment orchestration, automated load scaling, and risk-based test prioritisation. Jessy holds certifications such as PMP, PSM I, Certified Mobile Tester, Certified Automation Tester, and has completed leadership training with McKinsey & Company. By synthesising research insights with practical engineering experience, Jessy continues to advance and elevate quality engineering worldwide.