Logic Error Detection System based on Structure Pattern and Error Degree

Yoshizawa, Yuto; Watanobe, Yutaka

doi:10.25046/aj040501

Cited by 7 publications

(3 citation statements)

References 19 publications

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“…Thus, during the inference the trained model shows the probabilities for a given sequence of source code. Our approach also extends to static code analysis using the intermediate representation in Abstract Syntax Tree [28]. The results show that the proposed algorithms can detect logic errors in compliable code with high frequency for some specific problems.…”

Section: Detection Of Errors In Source Codementioning

confidence: 93%

Next-Generation Programming Learning Platform: Architecture and Challenges

et al. 2020

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With the rapid development of information technology, programming has become a vital skill. An online judge system can be used as a programming education platform, where the daily activities of users and judges are used to generate useful learning objects (e.g., tasks, solution codes, evaluations). Intelligent software agents can utilize such objects to create an ecosystem. To implement such an ecosystem, a generic architecture that covers the whole lifecycle of data on the platform and the functionalities of an e-learning system should take into account the particularities of the online judge system. In this paper, an architecture that implements such an ecosystem based on an online judge system is proposed. The potential benefits and research challenges are discussed.

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Section: Detection Of Errors In Source Codementioning

confidence: 93%

Next-Generation Programming Learning Platform: Architecture and Challenges

et al. 2020

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“…Over the years, attention mechanisms have been adapted to a wide variety of diverse tasks [25][26][27][28][29][30], the most popular and effective of which is sequence-to-sequence modeling. Typically, in sequence-tosequence modeling, the output of the last hidden state is used as the context vector for further consideration.…”

Section: Proposed Lstm-am Networkmentioning

confidence: 99%

A Neural Network Based Intelligent Support Model for Program Code Completion

Rahman

Watanobe

Nakamura

2020

Scientific Programming

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In recent years, millions of source codes are generated in different languages on a daily basis all over the world. A deep neural network-based intelligent support model for source code completion would be a great advantage in software engineering and programming education fields. Vast numbers of syntax, logical, and other critical errors that cannot be detected by normal compilers continue to exist in source codes, and the development of an intelligent evaluation methodology that does not rely on manual compilation has become essential. Even experienced programmers often find it necessary to analyze an entire program in order to find a single error and are thus being forced to waste valuable time debugging their source codes. With this point in mind, we proposed an intelligent model that is based on long short-term memory (LSTM) and combined it with an attention mechanism for source code completion. Thus, the proposed model can detect source code errors with locations and then predict the correct words. In addition, the proposed model can classify the source codes as to whether they are erroneous or not. We trained our proposed model using the source code and then evaluated the performance. All of the data used in our experiments were extracted from Aizu Online Judge (AOJ) system. The experimental results obtained show that the accuracy in terms of error detection and prediction of our proposed model approximately is 62% and source code classification accuracy is approximately 96% which outperformed a standard LSTM and other state-of-the-art models. Moreover, in comparison to state-of-the-art models, our proposed model achieved an interesting level of success in terms of error detection, prediction, and classification when applied to long source code sequences. Overall, these experimental results indicate the usefulness of our proposed model in software engineering and programming education arena.

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“…(Yoshizawa et al 2019). Logic errors in a program can be very frustrating for programmers to locate and repair.…”

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confidence: 99%

Logic Error Localization and Correction with Machine Learning (Student Abstract)

Xu¹,

Sheng²,

Lü

2023

AAAI

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We aim to propose a system repairing programs with logic errors to be functionally correct among different programming languages. Logic error program repair has always been a thorny problem: First, a logic error is usually harder to repair than a syntax error in a program because it has no diagnostic feedback from compilers. Second, it requires inferring in different ranges (i.e., the distance of related code lines) and tracking symbols across its pseudocode, source code, and test cases. Third, the logic error datasets are scarce, since an ideal logic error dataset should contain lots of components during the development procedure of a program, including a program specification, pseudocode, source code, test cases, and test reports (i.e., test case failure report). In our work, we propose novel solutions to these challenges. First, we introduce pseudocode information to assist logic error localization and correction. We construct a code-pseudocode graph to connect symbols across a source code and its pseudocode and then apply a graph neural network to localize and correct logic errors. Second, we collect logic errors generated in the process of syntax error repairing via DrRepair from 500 programs in the SPoC dataset and reconstruct them to our single logic error dataset, which we leverage to train and evaluate our models. Our experimental results show that we achieve 99.39% localization accuracy and 19.20% full repair accuracy on logic errors with five-fold cross-validation. Based on our current work, we will replenish and construct more complete public logic error datasets and propose a novel system to comprehend different programming languages from several perspectives and correct logic errors to be functionally correct.

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Logic Error Detection System based on Structure Pattern and Error Degree

Cited by 7 publications

References 19 publications

Next-Generation Programming Learning Platform: Architecture and Challenges

Next-Generation Programming Learning Platform: Architecture and Challenges

A Neural Network Based Intelligent Support Model for Program Code Completion

Logic Error Localization and Correction with Machine Learning (Student Abstract)

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