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 wireframe testing process, designers can save time, mitigate subjective biases, and enhance the overall quality and effectiveness of the final interface. The findings of this study will not only empower designers but also contribute to improving user experiences and driving innovation in UI design. The results will have practical implications for various industries, including mobile applications, websites, software interfaces, and interactive systems.

Method:

What this thesis aims is to propose a partial automation: This level of automation includes tools that can perform some usability evaluation tasks independently but still require human involvement for more complex or subjective tasks. An example is automated tools that can identify basic usability problems in a user interface, such as missing labels or broken links, but require human evaluators to identify and interpret more subtle or complex usability issues (Ivory, M. Y., & Hearst, M. A., 2001).

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.