Lilia
Gargouri
Head of QA at mgm technology partners gmbh
"Towards Sustainable Software Testing: Balancing Quality, Efficiency, and Environmental Responsibility in the AI Era"
"Towards Sustainable Software Testing: Balancing Quality, Efficiency, and Environmental Responsibility in the AI Era"
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Session: Towards Sustainable Software Testing: Balancing Quality, Efficiency, and Environmental Responsibility in the AI Era
Sustainability is emerging as a critical dimension of software quality, yet its implications for software testing remain largely underexplored. Testing ensures reliability, robustness, and compliance, but it is also resource-intensive, consuming significant computational power, infrastructure, and human effort. The increasing use of artificial intelligence (AI) in testing amplifies this paradox: AI promises efficiency in test generation, prioritization, and defect detection, yet its high computational demands contribute to substantial energy use and CO₂ emissions.This contribution introduces a framework for sustainable software testing that aligns testing practices with ecological responsibility and corporate sustainability goals. The framework builds on three principles: optimizing test processes to eliminate redundancy and reduce energy consumption; introducing sustainability-aware metrics that make the ecological footprint of testing and AI workloads visible; and raising organizational awareness to ensure AI and automation are applied purposefully rather than indiscriminately.
Case examples and empirical insights illustrate how approaches such as risk-based test selection, energy-efficient infrastructure, and calibrated AI usage can lower resource consumption while safeguarding software quality. Sustainable testing is positioned not only as a technical enhancement but as a strategic imperative for responsible digital transformation.
Key Takeaways
- Sustainable testing is a strategic necessity: Optimizing testing processes reduces not only costs and execution time but also environmental impact, aligning software quality assurance with corporate sustainability goals.
- AI is a double-edged sword: While AI can improve efficiency in test design and execution, its computational intensity creates a substantial carbon footprint that must be consciously managed.
- Awareness enables action: Introducing sustainability metrics and transparent reporting empowers teams and organizations to make informed decisions about when, how, and to what extent AI and automation should be applied.
- Actionable practices for greener QA – Approaches such as risk-based test selection, energy-efficient infrastructure, and calibrated AI usage can reduce resource consumption while maintaining reliability and effectiveness.
Bio
Lilia Gargouri stands for sustainable, scalable, and efficient quality assurance in complex eGovernment projects. She is committed to accessibility, trains career changers, and passionately advocates for greater visibility and equal opportunities for women in IT. As the inventor of a model-based, low-code test automation tool, she has significantly simplified and accelerated quality assurance in critical eGovernment projects. A member of the German Testing Board, she actively drives the development of industry quality standards. Lilia is also co-author of the book “Paradigmenwechsel Low-Code” (Springer Nature) and a sought-after speaker both nationally and internationally.