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A Context-Aware Semantic Genetic Algorithm for Requirements Prioritization
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DOI：10.3390/math14111868.png)
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Abstract
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In contemporary software development, user satisfaction plays a central role in guiding software evolution, particularly in domains driven by continuous user feedback. Such feedback contains valuable signals about functional priorities, quality concerns, and emerging risks; however, its unstructured nature makes requirements prioritization for releasing planning complex and error-prone. This challenge is further amplified by differences in perceived value, risk severity, and semantic interdependencies among requirements. This paper proposes a Context-Aware Semantic Genetic Algorithm (CA-SGA) that formulates requirements prioritization and release planning as a unified optimization problem that jointly considers user satisfaction and dependency-based cohesion. The approach is built upon a predefined Context-Aware Semantic Requirement Matrix (CASRM), which encodes Kano-based functional importance, risk impact levels inferred from user feedback, and semantic dependencies derived from an ontology-driven Dependency structure matrix. These components are integrated into a composite fitness function that maximizes satisfaction while accounting for contextual dependencies and feasibility constraints. The proposed method is evaluated on a large-scale real-world mobile banking dataset and compared against multiple baseline approaches. The results demonstrate that CA-SGA consistently generates more coherent and effective release plans, achieving higher fitness values and improved convergence stability.
Keywords:
requirements prioritization
user feedback analysis
context awareness
genetic algorithm
release planning
semantic analysis
agile development
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