Authors :
Yogeshwar Kulkarni
Volume/Issue :
Volume 9 - 2024, Issue 6 - June
Google Scholar :
https://tinyurl.com/45pb8ad7
Scribd :
https://tinyurl.com/3zy7b9ts
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24JUN606
Abstract :
The integration of Artificial Intelligence (AI)
in software testing is revolutionizing the field by
enhancing efficiency, accuracy, and coverage. This
paper explores the impact of AI-driven techniques on
software testing, contrasting them with traditional
methods. AI advancements such as machine learning,
deep learning, and predictive analytics facilitate
automated decision-making, intelligent test case
generation, and adaptive test execution, streamlining
routine tasks and reducing human intervention. Despite
these benefits, challenges such as data quality,
algorithmic bias, and ethical concerns need to be
addressed. The paper also discusses the potential for
human-AI collaboration, leveraging the strengths of
both to improve testing practices. By examining
empirical evidence and case studies, this research aims
to provide valuable insights for industry professionals,
researchers, and stakeholders, highlighting future
research avenues and opportunities in AI-driven
software testing. --- This abstract synthesizes the key
points from the provided document, presenting a
concise overview of the study's objectives, findings, and
implications.
Keywords :
Artificial Intelligence (AI) in Software Testing, Revolutionizing, Machine Learning, Deep Learning, Predictive Analytics, Data Quality, Algorithmic Bias, Ethical Concerns, AI-Driven Software Testin.
References :
- Role of AI In Functional Testing – QualiZeal
- 6 Benefits of AI Testing - Melillo Consulting
- Guide for AI in Software Testing – Parasoft
- Artificial Intelligence in Software Testing: Impact, Problems – arXiv
- AI Based Testing: The Future of Test Automation - Testim.io
The integration of Artificial Intelligence (AI)
in software testing is revolutionizing the field by
enhancing efficiency, accuracy, and coverage. This
paper explores the impact of AI-driven techniques on
software testing, contrasting them with traditional
methods. AI advancements such as machine learning,
deep learning, and predictive analytics facilitate
automated decision-making, intelligent test case
generation, and adaptive test execution, streamlining
routine tasks and reducing human intervention. Despite
these benefits, challenges such as data quality,
algorithmic bias, and ethical concerns need to be
addressed. The paper also discusses the potential for
human-AI collaboration, leveraging the strengths of
both to improve testing practices. By examining
empirical evidence and case studies, this research aims
to provide valuable insights for industry professionals,
researchers, and stakeholders, highlighting future
research avenues and opportunities in AI-driven
software testing. --- This abstract synthesizes the key
points from the provided document, presenting a
concise overview of the study's objectives, findings, and
implications.
Keywords :
Artificial Intelligence (AI) in Software Testing, Revolutionizing, Machine Learning, Deep Learning, Predictive Analytics, Data Quality, Algorithmic Bias, Ethical Concerns, AI-Driven Software Testin.
