A Bidirectional LSTM Language Model for Code Evaluation and Repair

Rahman, Md. Mostafizer; Watanobe, Yutaka; Nakamura, Keita

doi:10.3390/sym13020247

Cited by 77 publications

(34 citation statements)

References 31 publications

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“…The huge volume of data stored in these OJ systems helps researchers find shortcomings in students' programming and identify areas for improvement. As a result, a lot of research is being conducted using these rich resources to identify and solve various programming-related problems [38], [41]- [44].…”

Section: Oj Systemsmentioning

confidence: 99%

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Educational Data Mining to Support Programming Learning Using Problem-Solving Data

et al. 2022

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Computer programming has attracted a lot of attention in the development of information and communication technologies in the real world. Meeting the growing demand for highly skilled programmers in the ICT industry is one of the major challenges. In this point, online judge (OJ) systems enhance programming learning and practice opportunities in addition to classroom-based learning. Consequently, OJ systems have created a large number of problem-solving data (solution codes, logs, and scores) archives that can be valuable raw materials for programming education research. In this paper, we propose an educational data mining framework to support programming learning using unsupervised algorithms. The framework includes the following sequence of steps: (i) problem-solving data collection (logs and scores are collected from the OJ) and preprocessing; (ii) MK-means clustering algorithm is used for data clustering in Euclidean space; (iii) statistical features are extracted from each cluster; (iv) frequent pattern (FP)-growth algorithm is applied to each cluster to mine data patterns and association rules; (v) a set of suggestions are provided on the basis of the extracted features, data patterns, and rules. Different parameters are adjusted to achieve the best results for clustering and association rule mining algorithms. For the experiment, approximately 70,000 realworld problem-solving data from 537 students of a programming course (Algorithm and Data Structures) were used. In addition, synthetic data have leveraged for experiments to demonstrate the performance of MK-means algorithm. The experimental results show that the proposed framework effectively extracts useful features, patterns, and rules from problem-solving data. Moreover, these extracted features, patterns, and rules highlight the weaknesses and the scope of possible improvements in programming learning.

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Section: Oj Systemsmentioning

confidence: 99%

“…Recently, the source codes of the AOJ system have been used in IBM's research project "Project CodeNet" [59]. In addition, the submission logs, code archives, and problem sets of the AOJ system have been used for many research and educational purposes [41]- [44].…”

Section: A Aoj Platformmentioning

confidence: 99%

Educational Data Mining to Support Programming Learning Using Problem-Solving Data

et al. 2022

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“…LSTM is a type of RNN, which is explicitly designed to avoid a long dependency period, allowing the model to remember information over a long period of time on its own. Although it is a chain structure such as RNN, each iteration module has a different structure [31]. Instead of a simple natural network layer, four layers are supposed to exchange information with each other in a special way.…”

Section: Bi-lstmmentioning

confidence: 99%

Design and Implementation of an Explainable Bidirectional LSTM Model Based on Transition System Approach for Cooperative AI-Workers

et al. 2022

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Recently, interest in the Cyber-Physical System (CPS) has been increasing in the manufacturing industry environment. Various manufacturing intelligence studies are being conducted to enable faster decision-making through various reliable indicators collected from the manufacturing process. Artificial intelligence (AI) and Machine Learning (ML) have advanced enough to give various possibilities of predicting manufacturing time, which can help implement CPS in manufacturing environments, but it is difficult to secure reliability because it is difficult to understand how AI works, and although it can offer good results, it is often not applied to industries. In this paper, Bidirectional Long Short Term Memory (BI-LSTM) is used to predict process execution time, which is an indicator that can be used as a basis for CPS in the manufacturing process, and the Shapley Additive Explanations (SHAP) algorithm is used to explain how artificial intelligence works. The experimental results of this paper, applying manufacturing data, prove that the results derived from SHAP are effective for workers and AI to collaborate.

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“…The tests indicated this method is effectively applicable to quick chargers. In addition in 2021, Rahman et al [21] proposed a language model for evaluating and finding logical errors in source codes using a bidirectional LSTM neural network, training the BiLSTM with a large number of source codes, outperforming other state-of-the-art models with recurrent neural networks (RNNs), and achieving an F-score of approximately 97%. Finally, He et al [22] proposed the use of LSTM layers with additional convolutional layers to increase the performance of prediction models aiming to offer insights into gold price fluctuations.…”

Section: Related Workmentioning

confidence: 99%

hLSTM-Aging: A Hybrid LSTM Model for Software Aging Forecast

et al. 2022

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Long-running software, such as cloud computing services, is now widely used in modern applications. As a result, the demand for high availability and performance has grown. However, these applications are more vulnerable to software aging issues and are more likely to fail due to the accumulation of mistakes in the system. One popular strategy for dealing with such aging-related problems is to plan prediction-based software rejuvenation activities based on previously obtained data from long-running software. Prediction algorithms enable the activation of a mitigation mechanism before the problem occurs. The long short-term memory (LSTM) neural network, the present state of the art in temporal series prediction, has demonstrated promising results when applied to software aging concerns. This study aims to anticipate software aging failures using a hybrid prediction model integrating long short-term memory models and statistical approaches. We emphasize the capabilities of each strategy in various long-running software scenarios and provide an untried hybrid model (hLSTM-aging) based on the union of Conv-LSTM networks and probabilistic methodologies, attempting to combine the strengths of both approaches for software aging forecasts. The hLSTM-aging prediction results revealed how hybrid models are a compelling solution for software-aging prediction. Experiments showed that hLSTM-aging increased MSE criteria by 8.54% to 50% and MAE criteria by 3.53% to 14.29% when compared to Conv-LSTM, boosting the model’s initial performance.

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A Bidirectional LSTM Language Model for Code Evaluation and Repair

Cited by 77 publications

References 31 publications

Educational Data Mining to Support Programming Learning Using Problem-Solving Data

Educational Data Mining to Support Programming Learning Using Problem-Solving Data

Design and Implementation of an Explainable Bidirectional LSTM Model Based on Transition System Approach for Cooperative AI-Workers

hLSTM-Aging: A Hybrid LSTM Model for Software Aging Forecast

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