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Localizing Non-functional Code Bugs in User Interfaces Using Deep Learning Techniques

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Model and Data Engineering (MEDI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14396))

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Abstract

In the context of modern business digitization, non-functional code issues in User Interface (UI) exert a substantial impact on User Experience(UX). UX-related problems can generate annoyance and potentially result in revenue erosion. However, conventional approaches for detecting and resolving these defects tend to be prolonged, mandate a substantial degree of expertise, and divert precious time and resources away from vital software development tasks, ultimately impairing business operations. A deep learning-based approach automatically detects and localizes non-functional code flaws in user interface (UI) screens. The automated approach reduces the time and effort required to fix non-functional code bugs in UI screens, improving overall quality control and testing and decreasing overall UI testing time. Our model can identify non-functional code flaws by analyzing many UI screens and pinpointing their location on UI screens. We evaluate our proposed approach on a variety of UI screens. The proposed approach achieves better performance in classification by 4% and in localization by 16%.

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Acknowledgment

This paper is based upon work supported by The Academy of Scientific Technology (ASRT), Egypt. Scientists of Next Generation Scholarship Cycle (8).

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Authors and Affiliations

  1. Center for Informatics Science, School of Information Technology and Computer Science (ITCS), Nile University, Giza, Egypt

    Arwa Ahmed, Ahmed Tamer Salah, Ghada Khoriba & Tamer Arafa

Authors
  1. Arwa Ahmed
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  2. Ahmed Tamer Salah
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  3. Ghada Khoriba
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  4. Tamer Arafa
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Corresponding author

Correspondence to Arwa Ahmed .

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Editors and Affiliations

  1. Bordeaux INP, Talence, France

    Mohamed Mosbah

  2. Dublin City University, Dublin, Ireland

    Tahar Kechadi

  3. ENSMA, Poitiers, France

    Ladjel Bellatreche

  4. University of Sfax, Sfax, Tunisia

    Faiez Gargouri

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Ahmed, A., Salah, A.T., Khoriba, G., Arafa, T. (2024). Localizing Non-functional Code Bugs in User Interfaces Using Deep Learning Techniques. In: Mosbah, M., Kechadi, T., Bellatreche, L., Gargouri, F. (eds) Model and Data Engineering. MEDI 2023. Lecture Notes in Computer Science, vol 14396. Springer, Cham. https://doi.org/10.1007/978-3-031-49333-1_27

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  • DOI: https://doi.org/10.1007/978-3-031-49333-1_27

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