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In the field of requirement document quality assessment, existing methods mainly focused on textual patterns of requirements. Actually, the cognitive process that experts read and qualitatively measure a requirement document is from outward appearance to inner essence. Inspired by this intuition, this paper proposed a Multimodal Requirement Document Quality Analyzer (MRDQA), a neural model which combines the textual content with the visual rendering of requirement documents for quality assessing. MRDQA can capture implicit quality indicators which do not exist in requirement text, such as tables, diagrams, and visual layout. We evaluated MRDQA on the requirement documents collected from ZTE and achieved 81.3% accuracy in classifying their quality into three levels (high, medium, and low). We have successfully applied MRDQA as a pre-filter in ZTE’s requirement review system. It identifies low and medium quality requirements, thereby allows review experts to focus only on high-quality requirements. With this mechanism, the workload can be greatly reduced and the requirement review process can be accelerated. |
