  The advent of automation in particular Software Engineering (SE) tasks has
transitioned from theory to reality. Numerous scholarly articles have
documented the successful application of Artificial Intelligence to address
issues in areas such as project management, modeling, testing, and development.
A recent innovation is the introduction of ChatGPT, an ML-infused chatbot,
touted as a resource proficient in generating programming codes and formulating
software testing strategies for developers and testers respectively. Although
there is speculation that AI-based computation can increase productivity and
even substitute software engineers in software development, there is currently
a lack of empirical evidence to verify this. Moreover, despite the primary
focus on enhancing the accuracy of AI systems, non-functional requirements
including energy efficiency, vulnerability, fairness (i.e., human bias), and
safety frequently receive insufficient attention. This paper posits that a
comprehensive comparison of software engineers and AI-based solutions,
considering various evaluation criteria, is pivotal in fostering human-machine
collaboration, enhancing the reliability of AI-based methods, and understanding
task suitability for humans or AI. Furthermore, it facilitates the effective
implementation of cooperative work structures and human-in-the-loop processes.
This paper conducts an empirical investigation, contrasting the performance of
software engineers and AI systems, like ChatGPT, across different evaluation
metrics. The empirical study includes a case of assessing ChatGPT-generated
code versus code produced by developers and uploaded in Leetcode.
