Automating heuristic evaluation of website interfaces using convolutional neural networks
Heuristic evaluation is an important phase in both quality assurance and UX de- sign. This ensures that a user interface that is being tested adheres to usabilitystandards and can be used by any user with ease. Heuristic evaluation typicallytakes a long time since it involves consolidating opinions...
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| Format: | text |
| Language: | English |
| Published: |
Animo Repository
2019
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| Subjects: | |
| Online Access: | https://animorepository.dlsu.edu.ph/etd_masteral/7188 |
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| Institution: | De La Salle University |
| Language: | English |
| Summary: | Heuristic evaluation is an important phase in both quality assurance and UX de- sign. This ensures that a user interface that is being tested adheres to usabilitystandards and can be used by any user with ease. Heuristic evaluation typicallytakes a long time since it involves consolidating opinions of multiple design ex- perts. This study attempts to automate the detection of usability issues in a givenuser interface design to lessen the expense and thereby time needed to hire pro- fessionals and to focus on development-review-revision cycles. The method usedwas a data-driven approach through the usage of convolutional neural networks (CNN). A computational model using CNNs to determine whether an interface is good or bad is made from a dataset of screenshots of user interfaces, with a higheraccuracy of that of a simple multilayer perceptron. By comparing the model out- puts to evaluator annotations, several insights regarding the design of e-commercewebsites were also gathered, specifically which heuristics are more important tothe models as compared with which are not as important. The highest perform- ing model yielded 70% accuracy. Further research can lead to fine tuning with alarger dataset. |
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