Source Code Assessment and Classification Based on Estimated Error Probability Using Attentive LSTM Language Model and Its Application in Programming Education

Source Code Assessment and Classification Based on Estimated Error Probability Using Attentive LSTM Language Model and Its Application in Programming Education

The rate of software development has increased dramatically. Conventional compilers cannot assess and detect all source code errors. Software may thus contain errors, negatively affecting end-users. It is also difficult to assess and detect source code logic errors using traditional compilers, resul...

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Bibliografiska uppgifter
Huvudupphovsmän: Md. Mostafizer Rahman, Yutaka Watanobe, Keita Nakamura
Materialtyp: Artikel
Språk:English
Publicerad: MDPI AG 2020-04-01
Serie:Applied Sciences
Ämnen:
language modeling
classification
error probability
error assessment
logic error
neural network
Länkar:https://www.mdpi.com/2076-3417/10/8/2973

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https://www.mdpi.com/2076-3417/10/8/2973

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