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Research article review: Software testing in the era of artificial intelligence

230816-research-article-review--software-testing-techniques-and-tools-in-the-era-of-artificial-intelligence-1692200359

Estimated reading time: 2 minutes

Here we present an overview of a recently published research article “A decade of intelligent software testing research: a bibliometric analysis: A comprehensive study on software testing techniques and tools in the era of artificial intelligence”.

The paper, authored by Mohamed Boukhlif [pictured] et al. and published in the 12th edition of the Electronics journal (5th May 2023), offers a deep-dive into software testing in the context of the recent and rapid advancements in Artificial Intelligence (AI). The authors emphasise the increasing significance of AI-driven testing tools and techniques, highlighting their potential to revolutionise traditional software testing.

The publication begins with an overview of the conventional software testing methodologies and their limitations in the face of complex, dynamic, and large-scale software systems. It then transitions into discussing the role of AI in addressing these challenges. The authors present various AI-driven testing techniques, such as predictive testing, adaptive testing, and intelligent test case generation. These techniques leverage machine learning, deep learning, and other AI algorithms to enhance the efficiency, accuracy, and coverage of software testing processes.

The article showcases a comparative analysis of traditional compared to AI-driven testing tools, underscoring the advantages of the latter. AI-powered tools, as the authors note, are capable of self-learning, adapting to changes, and providing real-time feedback, making them indispensable in the modern software development lifecycle.

The research also highlights the challenges associated with integrating AI into software testing, including the need for specialised training, potential biases in AI models, and the ethical considerations of automating testing processes.

[Image Source: ResearchGate]

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