Assessing the quality of mobile graphical user interfaces using multi-objective optimization

Soui, Makram; Chouchane, Mabrouka; Mkaouer, Mohamed Wiem; Kessentini, Marouane; Ghédira, Khaled

doi:10.1007/s00500-019-04391-8

Cited by 21 publications

(7 citation statements)

References 86 publications

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“…In another study, Ismail [ 23 ] discovered that unattractive interfaces confuse, obscure intent, and slow down users’ actions. Soui et al [ 24 ] emphasized that the interface design, apart from following the general rules, should be meaningful to the user. The aesthetics of user interfaces was widely discussed by Schlatter and Levinson [ 25 ], who reported that the interface as well as order and consistency of the grouping of elements has a significant influence on user perception.…”

Section: Related Workmentioning

confidence: 99%

Online Store Aesthetics Impact Efficacy of Product Recommendations and Highlighting

Sulikowski

Kucznerowicz

Bąk

et al. 2022

Sensors

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Owing to high competition in e-commerce, customers may prefer sites that ensure that good user experience (UX) and website aesthetics are one of its qualities. The method of presenting items seems crucial for gaining and maintaining user attention. We conducted a task-based user eye-tracking study with n = 30 participants to examine two variants of an online fashion store: one based on aesthetic rules and one defying them. The following aspects of item presentation were considered: height and width the ratio of product photos, website colors, rounded borders, text visibility, spacing between elements, and smooth animation. We investigated their relationship to user attention by analyzing gaze fixation, tracking user interest, and conducting a supplementary survey. Experimental results showed that owing to following the rules of aesthetics in interface design in the presented fashion shopping scenario, elements such as the recommendation area and product highlights had a significant positive impact on customer attention.

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Section: Related Workmentioning

confidence: 99%

Online Store Aesthetics Impact Efficacy of Product Recommendations and Highlighting

Sulikowski

Kucznerowicz

Bąk

et al. 2022

Sensors

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“…The saturated UI is overloaded MUI, when the user found that the interface is too dense, not easy to read and very tedious to understand [18]. This charged UI can reflect the troubles of synthesis and understandable for user because of the more density of MUI (Firmenich et al, 2018;[4, 19].…”

Section: Saturated Muimentioning

confidence: 99%

Fixing usability smells of mobile apps: A multi-objective early refactoring recommendation approach

Soui

Chouchane

Mansouri

2022

Preprint

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Usability is crucial for mobile app success. Usability evaluation and correction of Mobile User Interface (MUI) is fundamental phase to discover the usability smells and improve its quality. However, it continues to be a hard, expensive, and generally it is ignored. Several existing works depend on a subjective assessment of usability defects which relies on users’ feedback, that leads to time-consuming, human-centric, and error-prone task. Hence, recent works proposed tools for collection and analysis log user interaction when using the mobile application to understand user behavior and identify usability smells. But, these tools treat the defects with the same importance and rarely propose recommendations to help evaluator to improve their MUIs. It still also needed to identify concrete usability defects and fixed riskiest usability smells. In addition, most existing methods proposed to assess the usability of once the applications has been developed, which led to a lot of reworking by the designer because these changes can affect the development phases.In this paper, we propose an automated approach based on correction rules which combine literature evaluation metrics with usability smells to define recommended refactoring of MUI.To this end, we consider this refactoring recommendation as a multi-objective optimization problem. The purpose of this optimization is to maximize the number of refactoring per individual while minimizing the number of features per rule, the number of rules by solution, the misclassification rate, the information gain and improving the MUI quality in terms of guidance and cohesion coverage and minimizing the complexity of MUI. Moreover, we leaded a comparative study of four evolutionary algorithms to solve this optimization problem. The results prove the efficiency of the IBEA in terms of generating the most accurate refactoring rules with an average of 0.92 of AUC.This paper applied an evolutionary algorithm to recommend the best refactoring operations to enhance the quality of MUI. The results are promising. Our search-based algorithm successfully searches the best trade-off between eight objectives and recommending the most accurate refactoring rules compared with other studied algorithms.

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“…After the concept of visual complexity has been described, various studies have been carried out to measure this concept in predefined metric based manner [4][5][6]. Typically, there are studies in which the quality of the mobile UIs was measured by transforming it into a multi-objective optimization problem [7]. Lastly, UI evaluation with machine learning based methods is being conducted [8].…”

Section: Related Workmentioning

confidence: 99%

“…Complexity analysis of visual interface and its understandability has long been the focus of attention of researchers. While in earlier studies [1][2][3], perceptual visual complexity had been tried to be defined based on perceptual attributes, in later studies, visual complexity of UIs has been measured by user evaluation with surveys, developing formal metrics and models [4][5][6], heuristics [7] to machine learning based [8] methods. Although formal metric and model based methods are mostly used, due the need of a blend of different metrics for different attributes and their potential complex interaction this problem is still not fully resolved.…”

Section: Introductionmentioning

confidence: 99%

A Deep Transfer Learning Based Visual Complexity Evaluation Approach to Mobile User Interfaces

Akça¹,

Tanrıöver²

2022

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Visual complexity is an important factor affecting the efficiency and functionality of user interfaces. Its impact on the user's impression and the usability is significant, especially for mobile applications with constraints such as layout size, on screen keys and small input fields. Conventional approaches for visual complexity evaluation of user interfaces are either based on user evaluations with surveys or based on pre-specified formal metrics or on heuristics. Alternatively, in this study, we have explored the effectiveness of deep learning models for visual complexity evaluation, specifically, of mobile user interfaces. We have experimented with five state of the art pre-trained deep learning models known to be effective for computer vision tasks, namely, VGG16, DenseNet121, MobileNetv2, GoogleNet and ResNet152 were trained with 3635 different mobile user interface images as login, menu, search and settings. Furthermore, in order to validate the effectiveness of this approach, a new validation dataset and survey application was developed and an evaluation study was conducted with 98 participants where 7309 comparison result were obtained from the study. It was found that the agreement rate between the results of deep learning models and the user evaluations was up to 78% and 74% on the average. The high to moderate agreement rate between the results of deep learning models and the user evaluations reveals that this approach can be useful for designers in visual complexity evaluation of mobile user interfaces.

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Assessing the quality of mobile graphical user interfaces using multi-objective optimization

Cited by 21 publications

References 86 publications

Online Store Aesthetics Impact Efficacy of Product Recommendations and Highlighting

Online Store Aesthetics Impact Efficacy of Product Recommendations and Highlighting

Fixing usability smells of mobile apps: A multi-objective early refactoring recommendation approach

A Deep Transfer Learning Based Visual Complexity Evaluation Approach to Mobile User Interfaces

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