Cite this DOI

Lakshmi Bolla, R., & Bobba, J. (2020). Enhancing Usability Testing Through A/B Testing, AI-Driven Contextual Testing, and Codeless Automation Tools. *Journal of Science & Technology*, *05*(05), 237–252. https://doi.org/10.46243/jst.2020.v5.i5.pp237-252

@article{lakshmibolla2020enhancing,
  author    = {Lakshmi Bolla, Ramya and Bobba, Jyothi},
  title     = {{Enhancing Usability Testing Through A/B Testing, AI-Driven Contextual Testing, and Codeless Automation Tools}},
  journal   = {Journal of Science \& Technology},
  year      = {2020},
  month     = {oct},
  volume    = {05},
  number    = {05},
  pages     = {237--252},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jst.2020.v5.i5.pp237-252},
  url       = {https://doi.org/10.46243/jst.2020.v5.i5.pp237-252},
  language  = {en},
  abstract  = {Background Information: Usability testing is essential for ensuring a seamless user experience in modern applications. Traditional methods often lack scalability and adaptability to real-world scenarios. By integrating A/B testing, AI-driven contextual testing, and codeless automation tools, testing efficiency improves, enabling dynamic UI evaluation, real-world usability assessments, and streamlined automation for comprehensive, data-driven usability testing. Objectives: This study aims to enhance usability testing by leveraging A/B testing for UI optimization, AI- driven contextual testing for real-world adaptation, and codeless automation tools for efficiency. The goal is to increase accuracy, improve test scalability, and streamline the usability evaluation process for faster, more reliable application development. Methods: A/B testing analyzes multiple UI versions for user engagement effectiveness. AI-driven contextual testing simulates real-world user interactions to detect usability issues dynamically. Codeless automation enables no-code usability test execution, ensuring broader test coverage and increased efficiency while reducing manual intervention in the usability evaluation process.}
}

TY  - JOUR
TI  - Enhancing Usability Testing Through A/B Testing, AI-Driven Contextual Testing, and Codeless Automation Tools
AU  - Lakshmi Bolla, Ramya
AU  - Bobba, Jyothi
JO  - Journal of Science & Technology
PY  - 2020
DA  - 2020/10/29/
VL  - 05
IS  - 05
SP  - 237
EP  - 252
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - Background Information: Usability testing is essential for ensuring a seamless user experience in modern applications. Traditional methods often lack scalability and adaptability to real-world scenarios. By integrating A/B testing, AI-driven contextual testing, and codeless automation tools, testing efficiency improves, enabling dynamic UI evaluation, real-world usability assessments, and streamlined automation for comprehensive, data-driven usability testing. Objectives: This study aims to enhance usability testing by leveraging A/B testing for UI optimization, AI- driven contextual testing for real-world adaptation, and codeless automation tools for efficiency. The goal is to increase accuracy, improve test scalability, and streamline the usability evaluation process for faster, more reliable application development. Methods: A/B testing analyzes multiple UI versions for user engagement effectiveness. AI-driven contextual testing simulates real-world user interactions to detect usability issues dynamically. Codeless automation enables no-code usability test execution, ensuring broader test coverage and increased efficiency while reducing manual intervention in the usability evaluation process.
DO  - 10.46243/jst.2020.v5.i5.pp237-252
UR  - https://doi.org/10.46243/jst.2020.v5.i5.pp237-252
ER  -

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    "URL": "https://doi.org/10.46243/jst.2020.v5.i5.pp237-252",
    "title": "Enhancing Usability Testing Through A/B Testing, AI-Driven Contextual Testing, and Codeless Automation Tools",
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    "author": [
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    "publisher": "Longman Publishers",
    "language": "en",
    "abstract": "Background Information: Usability testing is essential for ensuring a seamless user experience in modern applications. Traditional methods often lack scalability and adaptability to real-world scenarios. By integrating A/B testing, AI-driven contextual testing, and codeless automation tools, testing efficiency improves, enabling dynamic UI evaluation, real-world usability assessments, and streamlined automation for comprehensive, data-driven usability testing. Objectives: This study aims to enhance usability testing by leveraging A/B testing for UI optimization, AI- driven contextual testing for real-world adaptation, and codeless automation tools for efficiency. The goal is to increase accuracy, improve test scalability, and streamline the usability evaluation process for faster, more reliable application development. Methods: A/B testing analyzes multiple UI versions for user engagement effectiveness. AI-driven contextual testing simulates real-world user interactions to detect usability issues dynamically. Codeless automation enables no-code usability test execution, ensuring broader test coverage and increased efficiency while reducing manual intervention in the usability evaluation process.",
    "ISSN": "2456-5660"
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From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjst.2020.v5.i5.pp237-252 gives all four in one JSON answer.