  The automated code evaluation system (AES) is mainly designed to reliably
assess user-submitted code. Due to their extensive range of applications and
the accumulation of valuable resources, AESs are becoming increasingly popular.
Research on the application of AES and their real-world resource exploration
for diverse coding tasks is still lacking. In this study, we conducted a
comprehensive survey on AESs and their resources. This survey explores the
application areas of AESs, available resources, and resource utilization for
coding tasks. AESs are categorized into programming contests, programming
learning and education, recruitment, online compilers, and additional modules,
depending on their application. We explore the available datasets and other
resources of these systems for research, analysis, and coding tasks. Moreover,
we provide an overview of machine learning-driven coding tasks, such as bug
detection, code review, comprehension, refactoring, search, representation, and
repair. These tasks are performed using real-life datasets. In addition, we
briefly discuss the Aizu Online Judge platform as a real example of an AES from
the perspectives of system design (hardware and software), operation
(competition and education), and research. This is due to the scalability of
the AOJ platform (programming education, competitions, and practice), open
internal features (hardware and software), attention from the research
community, open source data (e.g., solution codes and submission documents),
and transparency. We also analyze the overall performance of this system and
the perceived challenges over the years.

Software Engineering;Computational Engineering, Finance, and Science