The evolution of artificial intelligence has thrust the Online Judge(OJ)systems into the forefront of research,particularly within programming education,with a focus on enhancing performance and efficiency.Addressing ...The evolution of artificial intelligence has thrust the Online Judge(OJ)systems into the forefront of research,particularly within programming education,with a focus on enhancing performance and efficiency.Addressing the shortcomings of the current OJ systems in coarse defect localization granularity and heavy task scheduling architecture,this paper introduces an innovative Integrated In-telligent Defect Localization and Lightweight Task Scheduling Online Judge(IDL-LTSOJ)system.Firstly,to achieve token-level fine-grained defect localization,a Deep Fine-Grained Defect Localization(Deep-FGDL)deep neural network model is developed.By integrating Bidirectional Long Short-Term Memory(BiLSTM)and Bidirectional Gated Recurrent Unit(BiGRU),this model extracts fine-grained information from the abstract syntax tree(AST)of code,enabling more accurate defect localization.Subsequently,we propose a lightweight task scheduling architecture to tackle issues,such as limited concurrency in task evaluation and high equipment costs.This architecture integrates a Kafka messaging system with an optimized task distribution strategy to enable concurrent execution of evaluation tasks,substantially enhancing system evaluation efficiency.The experimental results demonstrate that the Deep-FGDL model improves the accuracy by 35.9%in the Top-20 rank compared to traditional machine learning benchmark methods for fine-grained defect localization tasks.Moreover,the lightweight task scheduling strategy notably reduces response time by nearly 6000ms when handling 120 task volumes,which represents a significant improvement in evaluation efficiency over centralized evaluation methods.展开更多
基金supported in part by the Beijing Natural Science Foundation(4212018)the National Key R&D Program of China(2024YFE200500),awarded to Lihua Song.
文摘The evolution of artificial intelligence has thrust the Online Judge(OJ)systems into the forefront of research,particularly within programming education,with a focus on enhancing performance and efficiency.Addressing the shortcomings of the current OJ systems in coarse defect localization granularity and heavy task scheduling architecture,this paper introduces an innovative Integrated In-telligent Defect Localization and Lightweight Task Scheduling Online Judge(IDL-LTSOJ)system.Firstly,to achieve token-level fine-grained defect localization,a Deep Fine-Grained Defect Localization(Deep-FGDL)deep neural network model is developed.By integrating Bidirectional Long Short-Term Memory(BiLSTM)and Bidirectional Gated Recurrent Unit(BiGRU),this model extracts fine-grained information from the abstract syntax tree(AST)of code,enabling more accurate defect localization.Subsequently,we propose a lightweight task scheduling architecture to tackle issues,such as limited concurrency in task evaluation and high equipment costs.This architecture integrates a Kafka messaging system with an optimized task distribution strategy to enable concurrent execution of evaluation tasks,substantially enhancing system evaluation efficiency.The experimental results demonstrate that the Deep-FGDL model improves the accuracy by 35.9%in the Top-20 rank compared to traditional machine learning benchmark methods for fine-grained defect localization tasks.Moreover,the lightweight task scheduling strategy notably reduces response time by nearly 6000ms when handling 120 task volumes,which represents a significant improvement in evaluation efficiency over centralized evaluation methods.
