Makram Hanin

Co-Founder

Biography.

Passionate about engineering solutions for complex challenges, Makram Hanin has dedicated his career to sustainability, creativity, and social impact through specialized consulting ventures.
Makram’s extensive experience in service level management, performance engineering, and monitoring has laid the foundation for his recent contribution to developing and implementing a generic blueprint, known as the Digital Highway, to ensure the delivery of reliable digital solutions.
In collaboration with three Swiss universities from Zurich, Geneva, and Neuchâtel, Makram has been leading efforts on AI-SQUARE, an Innosuisse-supported project that pioneers AI-powered solutions for software QA and reliability engineering. This initiative introduces “Effective Continuous Verification,” a cutting-edge methodology incorporating machine learning (ML) and knowledge graphs to automate decision-making within DevOps pipelines. By designing dynamic smart quality gates, AI-SQUARE aligns with modern DevOps practices, enabling automated verification of quality gates related to performance, reliability, and functional requirements.
This expertise has been instrumental in shaping Makram’s approach to building sustainable, innovative, and effective digital solutions within the Swiss Digital Network.

Talk.

A Case Study of AI-Powered Software QA & Reliability Engineering

While most of us intuitively agree on the need for more agility and speed, traditional decision-making methods like Change Advisory Boards (CABs) often become bottlenecks. These human-centric steps, valuable in the past, now hinder the DevOps flow.
To effectively automate the decision-making process within the DevOps pipelines, we need to invest into the transition towards smart quality gates that operate based on insights generated from machine learning (ML). This requires a shift in quality assurance practices. Within the Swiss Digital Network, we call this new approach “Effective Continuous Verification” and we are conducting a Innosuisse (Swiss Innovation Agency, https://www.innosuisse.admin.ch/en) project with three Swiss universities from Zurich, Geneva and Neuchatel, called AI-SQUARE.
This talk presents the design patterns behind AI-SQUARE, an AI-driven solution for assessing software the quality and maturity. We address the challenges of integrating dynamic smart quality gates within the CI/CD pipeline and show how these checkpoints improve alignment with current DevOps practices. Further, we illustrate the concept of knowledge graphs and how it can be used to capture and store additional QA context parameters in addition to the standard observability data coming from various test results and system behavior data sources. We share how the enriched data in such knowledge graph is used as input for ML models to analyze the data to detect anomalies and assess quality gates related to performance, reliability, and functional requirements.
Finally, we present a concrete customer environment where we explain:
• what are the main requirements of automated performance quality gates verification within the pipelines?
• why we need a new AI-driven paradigm instead of human-centric one to automate quality gates verification in this customer context?
• to which extent the target AI-SQUARE capabilities are addressing the above introduced requirements and are compliance with the new paradigm

Get in Touch

We would love to speak with you.
Feel free to reach out using the below details.