Using neural networks to assess design systems

Integrating dataviz in design thinking process to assist managers with performance reviews and team decisions.

The best part: Having legal compliance insights.

Year:

2022

Tools:

Tensorflow, python , cursor

Duration:

3 months

Overview:

The significance of this research lies in its potential to revolutionize the way UI design is evaluated and improved.


By automating the compliance for Web Content Accessibility Guidelines, designers can save time, mitigate subjective biases, and enhance the overall quality and effectiveness of the final interface.

Method:

The integration of CNNs in wireframe testing offers promising solutions to enhance the evaluation process. CNNs excel at image analysis and feature extraction, making them well-suited for assessing visual elements in wireframes (Xu et al., 2019). By training CNN models on annotated datasets of wireframes and UI guidelines, researchers have successfully automated the detection of guideline violations (Fan et al., 2018; Zhang et al., 2020). The use of CNNs improves evaluation efficiency, reduces subjectivity, and enables objective assessments of compliance.

Dataset:

To validate the proposed approach, a comprehensive dataset is utilized from TNS, a reputable software company with over 30 years of experience in developing websites and mobile apps. The dataset covers diverse industries and design requirements, ensuring the validity and applicability of the approach. Extensive experimentation and evaluation will be conducted to assess the effectiveness of CNN-based wireframe testing in accurately identifying guideline violations and providing actionable insights for design refinement.

Testing other CNN architectures

One area of future research is the exploration and development of more advanced CNN architectures. Researchers can investigate novel architectures, such as recurrent neural networks (RNNs) or attention-based models, to capture temporal and contextual information in wireframes. These advanced architectures can potentially improve the accuracy and efficiency of wireframe testing, providing more nuanced feedback on the usability and effectiveness of UI design.

Implications and future research

Further research is needed to explore and refine the integration of CNN-based wireframe testing into the design thinking process. Areas of investigation include expanding the dataset to cover a wider range of components and design requirements, incorporating additional technologies such as Natural Language Processing (NLP) to analyze textual content, and exploring advanced visualization techniques to communicate insights effectively. Ongoing research and advancements in the field of data science and UX design will continue to shape and enhance the application of data-driven decision making in usability testing.

Testing other CNN architectures

One area of future research is the exploration and development of more advanced CNN architectures. Researchers can investigate novel architectures, such as recurrent neural networks (RNNs) or attention-based models, to capture temporal and contextual information in wireframes. These advanced architectures can potentially improve the accuracy and efficiency of wireframe testing, providing more nuanced feedback on the usability and effectiveness of UI design.