Artificial Intelligence (AI) can enable the development of next-generation autonomous safety-
critical systems in which Machine Learning (ML) algorithms learn optimized and safe …

Context: Artificial intelligence (AI) methods and models have extensively been applied to
support different phases of the software development lifecycle, including software testing …

The advancements in machine learning techniques have encouraged researchers to apply
these techniques to a myriad of software engineering tasks that use source code analysis …

The advancements in machine learning techniques have encouraged researchers to apply
these techniques to a myriad of software engineering tasks that use source code analysis …

It gets harder and harder to guarantee the quality of software systems due to their increasing
complexity and fast development. Because it helps spot errors and gaps during the first …

Context: Test Automation (TA) techniques are crucial for quality assurance in software
engineering but face limitations such as high test suite maintenance costs and the need for …

This paper provides the results of a survey of the grey literature concerning the use of
artificial intelligence to improve test automation practices. We surveyed more than 1,200 …

The goal of the testing process is to find errors and defects in the software being developed
so that they can be fixed and corrected before they are delivered to the customer …

This paper presents a novel approach to product design optimization through the synergy of
Genetic Algorithms (GA) and specific AI techniques. By iteratively evolving solutions inspired …

The use of automated methods for log analysis is unavoidable in any large company;
therefore, it has attracted attention from engineers and researchers. As a result, the number …