The representation of requirements plays a critical role in the accuracy of requirements inspection. While visual representations, such as UML diagrams, are widely used alongside text-based requirements, their effectiveness in supporting inspection is still debated. Cognitive abilities, such as working memory and mental rotation skills, may also influence inspection accuracy. This study aims to evaluate whether the use of UML sequence diagrams alongside text-based requirements improves the accuracy of requirements inspection compared to text-based requirements alone and to explore whether cognitive abilities are associated with differences in performance across the two treatments (text vs text with UML support). We conducted a crossover experiment with 38 participants to assess the accuracy of requirements inspection under the two treatments in terms of issues found and justifications provided. Linear mixed-effects and generalized linear models were used to analyse the effects of treatment, period, sequence, and cognitive abilities. The results indicate a significant three-way interaction between representation type, working memory capacity, and mental rotation ability. This finding suggests that the effectiveness of UML support is not uniform across individuals: participants with high scores in both cognitive abilities experienced reduced performance when using UML for violation detection. Conversely, the same cognitive profile was associated with improved justification accuracy under UML-aided inspection, indicating that higher cognitive abilities may support deeper reasoning processes when dealing with multi-modal information, i.e., diagrams and text.

翻译：需求表示形式对需求审查的准确性起着关键作用。尽管视觉表示形式（如UML图）与基于文本的需求规范被广泛结合使用，但其在支持审查方面的有效性仍存在争议。认知能力（如工作记忆和心理旋转技能）也可能影响审查的准确性。本研究旨在评估：相较于单独使用基于文本的需求规范，结合使用UML序列图与文本需求是否能提高需求审查的准确性；并探讨认知能力是否与两种处理条件（纯文本 vs. 文本结合UML支持）下的性能差异相关。我们开展了一项交叉实验，涉及38名参与者，以评估两种处理条件下需求审查在发现问题数量和提供合理理由方面的准确性。研究采用线性混合效应模型和广义线性模型来分析处理条件、实验阶段、顺序效应及认知能力的影响。结果表明，表示形式类型、工作记忆容量与心理旋转能力之间存在显著的三重交互效应。这一发现表明，UML支持的效果并非对所有个体一致：在两项认知能力上均得高分的参与者，使用UML进行违规检测时表现反而下降。相反，相同的认知特征与UML辅助审查下更高的理由准确性相关，这表明在处理多模态信息（即图表与文本）时，较高的认知能力可能支持更深层次的推理过程。