A Survey on Software Complexity Metrics in the Context of Their Application in Educational Environment

Stankov, Emil; Bogdanova, Ana Madevska; Ilijoski, Bojan; Jovanov, Mile

doi:10.21125/inted.2018.2325

Cited by 2 publications

(2 citation statements)

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“…The proposed metric captures the complexity that arises both from the control flow and the data flow, and resolves all of the issues with the other metrics that we have identified previously [40]. Based on the way it is defined and the above discussion, it can be concluded that the metric is suitable for the problem under consideration.…”

Section: Description Of An Appropriate Source Code Complexity Metricmentioning

confidence: 95%

“…Examples include computational complexity (algorithmic complexity of software), software size (symbolic complexity), cyclomatic complexity (control flow complexity), data flow complexity, and cognitive/psychological complexity, as some of the most popular ones [37]. Although several surveys on software complexity metrics have been done [37][38][39], the study by Stankov et al [40] is the only one that presents an analysis of the metrics' potential for application in educational environment, in the context of the described problem.…”

Section: Source Code Complexity Metrics For Automatic Measurement Of ...mentioning

confidence: 99%

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Smart generation of code tracing questions for assessment in introductory programming

Stankov

Jovanov

Bogdanova

2022

Comp Applic In Engineering

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Teaching programming is a challenging activity nowadays, especially in introductory programming courses, which are typically massively attended. Writing functional programs is a cognitive skill, which many students, novices in programming, find it difficult to master. It is equally challenging to assess this ability of students. Research has shown that students need to learn how to read and understand, before they can learn to write programs. Code tracing questions are a suitable way to assess the knowledge of semantics of programming constructs (understanding what programs do). However, when designing these types of questions, teachers need to be aware of the complexity of the code and must always try to create questions with code snippets of same or similar complexity, especially when preparing different versions of the same test (for large student groups). In this paper we propose a new model for smart automatic generation of code tracing questions. The model uses a suitable source code metric to measure the code complexity, and to enable generation of questions containing code snippets of consistent complexity. We also introduce CodeCPP: a newly developed Moodle plugin, which is an implementation of the proposed model. The evaluation of CodeCPP, through a year‐long use of the system in teaching programming as well as feedback from selected teachers with many years of experience in teaching programming courses, shows positive and promising results. The system utilization confirms that the application of the proposed model enables a fair and objective knowledge assessment. Furthermore, the feedback provided by teachers and students is highly positive.

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Section: Description Of An Appropriate Source Code Complexity Metricmentioning

confidence: 95%

Section: Source Code Complexity Metrics For Automatic Measurement Of ...mentioning

confidence: 99%

Smart generation of code tracing questions for assessment in introductory programming

Stankov

Jovanov

Bogdanova

2022

Comp Applic In Engineering

Self Cite

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Automating Fault Prediction in Software Testing Using Machine Learning Techniques: A Real-World Applications

Panda,

Sahoo,

Sahoo

2024

2024 2nd International Conference on Sustainable Computing and Smart Systems (ICSCSS)

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A Survey on Software Complexity Metrics in the Context of Their Application in Educational Environment

Cited by 2 publications

References 0 publications

Smart generation of code tracing questions for assessment in introductory programming

Smart generation of code tracing questions for assessment in introductory programming

Automating Fault Prediction in Software Testing Using Machine Learning Techniques: A Real-World Applications

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