Online Judge System: Requirements, Architecture, and Experiences

Watanobe, Yutaka; Rahman, Md. Mostafizer; Matsumoto, Taku; Kiran, R. Uday; Ravikumar, Penugonda

doi:10.1142/s0218194022500346

Cited by 27 publications

(4 citation statements)

References 16 publications

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“…Therefore, we experimented to evaluate the performance of ChatGPT in generating code based on problem descriptions. In the experiment, we used the "Algorithms and Data Structures" problem description from the Aizu Online Judge (AOJ) system [56] and verified the correctness of the codes generated by ChatGPT on the same platform and in a basic compiler. We leveraged the problem descriptions of eight random problems and generated codes three times for each problem on the basis of the same description.…”

Section: Solution Code Generationmentioning

confidence: 99%

ChatGPT for Education and Research: Opportunities, Threats, and Strategies

2023

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In recent years, the rise of advanced artificial intelligence technologies has had a profound impact on many fields, including education and research. One such technology is ChatGPT, a powerful large language model developed by OpenAI. This technology offers exciting opportunities for students and educators, including personalized feedback, increased accessibility, interactive conversations, lesson preparation, evaluation, and new ways to teach complex concepts. However, ChatGPT poses different threats to the traditional education and research system, including the possibility of cheating on online exams, human-like text generation, diminished critical thinking skills, and difficulties in evaluating information generated by ChatGPT. This study explores the potential opportunities and threats that ChatGPT poses to overall education from the perspective of students and educators. Furthermore, for programming learning, we explore how ChatGPT helps students improve their programming skills. To demonstrate this, we conducted different coding-related experiments with ChatGPT, including code generation from problem descriptions, pseudocode generation of algorithms from texts, and code correction. The generated codes are validated with an online judge system to evaluate their accuracy. In addition, we conducted several surveys with students and teachers to find out how ChatGPT supports programming learning and teaching. Finally, we present the survey results and analysis.

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Section: Solution Code Generationmentioning

confidence: 99%

ChatGPT for Education and Research: Opportunities, Threats, and Strategies

2023

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“…The datasets used in our experiments are extracted from the Aizu Online Judge (AOJ) system [55], [56]. The growing resources of the AOJ system have been used by various ML/AI-based projects in recent years.…”

Section: A Datasetsmentioning

confidence: 99%

Multilingual Program Code Classification Using $n$-Layered Bi-LSTM Model With Optimized Hyperparameters

Rahman,

Watanobe

2024

IEEE Trans. Emerg. Top. Comput. Intell.

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Programmers are allowed to solve problems using multiple programming languages, resulting in the accumulation of a huge number of multilingual solution codes. Consequently, identifying codes from this vast archive of multilingual codes is a challenging and non-trivial task. Considering the codes' complexity compared to natural languages, conventional language models have had limited success. Deep neural network models have achieved state-of-the-art performance in programming-related tasks. However, the multilingual code classification based on the problem name or algorithm remains an open problem. This paper presents a novel multilingual program code classification model for the code classification task based on algorithms and problem names. First, a layered bidirectional long short-term memory model is designed to better understand the complex code context. Second, preprocessing, tokenization, and encoding processes are performed on real-life datasets. Next, clean and trainable formatted data are prepared. Finally, experiments are conducted on real-life datasets (e.g., sorting, searching, graphs and trees, numerical computations, basic data structures, and their combinations) with optimized hyperparameter settings. The results show that the proposed model can effectively improve the code classification accuracy compared to other baseline models.

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“…Therefore, we experimented to evaluate the performance of ChatGPT in generating codes based on problem descriptions. In the experiment, we used the "Algorithms and Data Structures" problem description from the Aizu Online Judge (AOJ) system [48] and verified the correctness of the codes generated by ChatGPT on the same platform and in a basic compiler. We leveraged the problem descriptions of eight random problems and generated codes three times for each problem on the basis of the same description.…”

Section: Solution Code Generationmentioning

confidence: 99%

ChatGPT for Education and Research: Opportunities, Threats, and Strategies

Rahman¹,

Watanobe²

2023

Preprint

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In recent years, the rise of advanced artificial intelligence technologies has had a profound impact on many fields, including education and research. One such technology is ChatGPT, a powerful large language model developed by OpenAI. This technology offers exciting opportunities for students and educators, including personalized feedback, increased accessibility, interactive conversations, lesson preparation, evaluation, and new ways to teach complex concepts. However, ChatGPT poses different threats to the traditional education and research system, including the possibility of cheating on online exams, human-like text generation, diminished critical thinking skills, and difficulties in evaluating information generated by ChatGPT. This study explores potential opportunities and threats that ChatGPT poses to overall education from the perspective of students and educators. Furthermore, for programming learning, we explore how ChatGPT helps students improve their programming skills. To demonstrate this, we conducted different coding-related experiments with ChatGPT, including code generation from problem descriptions, pseudocode generation of algorithms from texts, and code correction. We also verified the generated codes with an online judge system to evaluate their accuracy.

show abstract

Online Judge System: Requirements, Architecture, and Experiences

Cited by 27 publications

References 16 publications

ChatGPT for Education and Research: Opportunities, Threats, and Strategies

ChatGPT for Education and Research: Opportunities, Threats, and Strategies

Multilingual Program Code Classification Using $n$-Layered Bi-LSTM Model With Optimized Hyperparameters

ChatGPT for Education and Research: Opportunities, Threats, and Strategies

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