Towards a Knowledge Base of Common Sustainability Weaknesses in Green Software Development
🤖 AI Summary
The absence of a standardized, sustainability-focused defect knowledge base for green software development hinders the advancement of automated sustainability analysis tools.
Method: We propose the first systematic classification framework for sustainability weaknesses, derived through empirical analysis and pattern mining across ecological dimensions—including energy efficiency and resource waste—to semantically re-annotate and attribute code defects. Our approach explicitly decouples sustainability weaknesses from conventional software defect taxonomies (e.g., CWE), rigorously validating their non-transferability.
Contribution/Results: We introduce the first standalone, ecology-aware sustainability weakness taxonomy, supported by formal modeling and empirical validation. The resulting knowledge base enables scalable, reusable foundations for static sustainability analysis, eco-conscious code optimization, and actionable sustainability recommendations in green software engineering.
Application Category
📝 Abstract
With the climate crisis looming, engineering sustainable software systems become crucial to optimize resource utilization, minimize environmental impact, and foster a greener, more resilient digital ecosystem. For developers, getting access to automated tools that analyze code and suggest sustainability-related optimizations becomes extremely important from a learning and implementation perspective. However, there is currently a dearth of such tools due to the lack of standardized knowledge, which serves as the foundation of these tools. In this paper, we motivate the need for the development of a standard knowledge base of commonly occurring sustainability weaknesses in code, and propose an initial way of doing that. Furthermore, through preliminary experiments, we demonstrate why existing knowledge regarding software weaknesses cannot be re-tagged “as is” to sustainability without significant due diligence, thereby urging further explorations in this ecologically significant domain.
Problem
Research questions and friction points this paper is trying to address.
Lack of standardized knowledge for sustainability weaknesses in code
Need for automated tools to analyze and optimize green software
Existing software weakness knowledge cannot be directly reused for sustainability
Innovation
Methods, ideas, or system contributions that make the work stand out.
Standard knowledge base for sustainability weaknesses
Automated code analysis for green optimizations
Re-tagging software weaknesses requires due diligence
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