(Science and Technology Publications, 2021) Kuruppu, T; Tharmaseelan, J; Silva, C; Samaratunge Arachchillage, U. S. S; Manathunga, K; Reyal, S; Kodagoda, N
With the embarkment of this technological era, a significant demand over programming modules can be
observed among university students in larger volume. When figures grow exponentially, manual assessments
and evaluations would be a tedious and error-prone activity, thus marking automation has become fast
growing necessity. To fulfil this objective, in this review paper, authors present literature on automated
assessment of coding exercises, analyse the literature from four dimensions as Machine Learning approaches,
Source Graph Generation, Domain Specific Languages, and Static Code Analysis. These approaches are
reviewed on three main aspects: accuracy, efficiency, and user-experience. The paper finally describes a series
of recommendations for standardizing the evaluation and benchmarking of marking automation tools for
future researchers to obtain a strong empirical footing on the domain, thereby leading to further advancements
in the field.
