Discovering Common Features in Software Code Using Self-Organizing Maps

Chan, Alvin T. S.; Spracklen, Tim

doi:10.1007/978-3-7908-1844-4_1

Cited by 3 publications

(3 citation statements)

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“…In the domain of education, SOMs have been used in intelligent tutoring systems to provide tailored tuition to learners based on their preferred learning styles [11]. Furthermore, SOMs have demonstrated the potential to identify important elements of object-oriented programming, such as classes and objects, by detecting common features in software code [12]. SOMs can also aid in the analysis of completed programming code, such as computer games, to identify the development of computational thinking skills [13].…”

Section: Literature Reviewmentioning

confidence: 99%

“…SOMs have also been used in different fields, such as student clustering based on academic grades [14] and course recommendations for e-learning systems [15]. Additionally, SOMs have been used to group procedures with similar properties by identifying common features in software code [12,16]. Furthermore, a study demonstrated that the variation of SOMs can identify algorithms implemented as programs by converting source code into syntax trees and computing similarities between them [17].…”

Section: Literature Reviewmentioning

confidence: 99%

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Source Code Analysis in Programming Education: Evaluating Learning Content with Self-Organizing Maps

2023

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Due to the everchanging and evergrowing nature of programming technologies, the gap between the programming industry’s needs and the educational capabilities of both formal and informal educational environments has never been wider. However, the need to learn computer programming has never been greater, regardless of the motivation behind it. The number of programming concepts to be taught is increasing over time, while the amount of time available for education and training usually remains the same. The objective of this research was to analyze the source codes used in many educational systems to teach fundamental programming concepts to learners, regardless of their prior experience in programming. A total of 25 repositories containing 3882 Python modules were collected for the analysis. Through self-organization of the collected content, we obtained very compelling results about code structure, distribution, and differences. Based on those results, we concluded that Self-Organizing Maps are a powerful tool for both content and knowledge management, because they can highlight problems in the curriculum’s density as well as transparently indicate which programming concepts it has successfully observed and learned to recognize. Based on the level of transparency exhibited by Self-Organizing Maps, it is safe to say that they could be used in future research to enhance both human and machine learning of computer programming. By achieving this level of transparency, such an Artificial Intelligence system would be able to assist in overall computer programming education by communicating what should be taught, what needs to be learned, and what is known.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Source Code Analysis in Programming Education: Evaluating Learning Content with Self-Organizing Maps

2023

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“…Such software clustering methods use Latent Semantic Analysis [18], Self-Organizing Map [4], file structure and file names [2] or structure of program like call graph [5,16]. They divide one software systems into some "component" (i.e.…”

Section: Related Workmentioning

confidence: 99%