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Modern software systems consist of many software components; the source code of modern software systems is hard to understand and maintain for new developers. Aiming to simplify the readability and understandability of source code, companies that specialize in software development adopt programming standards, software design patterns, and static analyzers with the aim of decreasing the complexity of software. Recent research introduced a number of code metrics allowing the numerical characterization of the maintainability of code snippets. Cyclomatic Complexity (CycC) is one widely used metric for measuring the complexity of software. The value of CycC is equal to the number of decision points in a program plus one. However, CycC does not take into account the nesting levels of the syntactic structures that break the linear control flow in a program. Aiming to resolve this, the Cognitive Complexity (CogC) metric was proposed as a successor to CycC. In this paper, we describe a rule-based algorithm and its specializations for measuring the complexity of programs. We express the CycC and CogC metrics by means of the described algorithm and propose a new complexity metric named Educational Complexity (EduC) for use in educational digital environments. EduC is at least as strict as CycC and CogC are and includes additional checks that are based on definition-use graph analysis of a program. We evaluate the CycC, CogC, and EduC metrics using the source code of programs submitted to a Digital Teaching Assistant (DTA) system that automates a university programming course. The obtained results confirm that EduC rejects more overcomplicated and difficult-to-understand programs in solving unique programming exercises generated by the DTA system when compared to CycC and CogC. [ABSTRACT FROM AUTHOR]

This paper presents a dataset containing automatically collected source codes solving unique programming exercises of different types. The programming exercises were automatically generated by the Digital Teaching Assistant (DTA) system that automates a massive Python programming course at MIREA—Russian Technological University (RTU MIREA). Source codes of the small programs grouped by the type of the solved task can be used for benchmarking source code classification and clustering algorithms. Moreover, the data can be used for training intelligent program synthesizers or benchmarking mutation testing frameworks, and more applications are yet to be discovered. We describe the architecture of the DTA system, aiming to provide detailed insight regarding how and why the dataset was collected. In addition, we describe the algorithms responsible for source code analysis in the DTA system. These algorithms use vector representations of programs based on Markov chains, compute pairwise Jensen–Shannon divergences of programs, and apply hierarchical clustering algorithms in order to automatically discover high-level concepts used by students while solving unique tasks. The proposed approach can be incorporated into massive programming courses when there is a need to identify approaches implemented by students. Dataset: The data presented in this study are openly available in Zenodo at https://doi.org/10.5281/zenodo.7799971 (accessed on 10 June 2023). Dataset License: CC-BY-4.0 [ABSTRACT FROM AUTHOR]