Authors:
Thilmi Kuruppu
;
Janani Tharmaseelan
;
Chamari Silva
;
Udara Srimath S. Samaratunge Arachchillage
;
Kalpani Manathunga
;
Shyam Reyal
;
Nuwan Kodagoda
and
Thilini Jayalath
Affiliation:
Faculty of Computing, Sri Lanka Institute of Information Technology (SLIIT), Malabe, Sri Lanka
Keyword(s):
Automated Assessments, Whitebox, Machine Learning, Syntax Graph, Static Tools, Literature Review, Programming.
Abstract:
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.