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14th International Conference on Information Technology in Medicine and Education, ITME 2024

作者： Xiang, Chengguan Yu, Maoqiu Zhi, Peipei Guizhou Education University School of Mathematics and Big Data Guiyang550018 China

ISBN: (纸本)9798350356670

In order to help programming novices locate programming errors and understand error cause, and to achieve accurate classification of programming errors, an error classification method based on deep learning networks has been proposed. Firstly, a dataset comprising 10,287 student C language programming errors (CPE28) was constructed, annotated with programming messages indicating either errors or warnings, and categorized into 28 error types to facilitate error feedback research. Secondly, the BLT-PECNet model, integrating BiLSTM and TextCNN, was designed for training and identifying programming error categories. BiLSTM captures contextual features and extracts global semantic information, while TextCNN performs convolution operations of varying sizes to extract multi-level features, ultimately yielding the classification results for programming errors. Experimental results on CPE28 demonstrate that the proposed method achieves over 92% in classification accuracy, precision, recall, and F1 score. It surpasses traditional single models in capturing key features of code errors, underscoring the method's efficacy and accuracy. © 2024 IEEE.

关键词： BiLSTM error classification feature extraction programming error TextCNN

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Integrating programming errors into knowledge graphs for automated assignment of programming tasks

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EDUCATION AND INFORMATION TECHNOLOGIES 2024年第5期29卷 5947-5980页

作者： Ding, Guozhu Shi, Xiangyi Li, Shan Guangzhou Univ Guangzhou Peoples R China Tongji Univ Shanghai Peoples R China Lehigh Univ Bethlehem PA USA

In this study, we developed a classification system of programming errors based on the historical data of 680,540 programming records collected on the Online Judge platform. The classification system described six types of programming errors (i.e., syntax, logical, type, writing, misunderstanding, and runtime errors) and their connections with fundamental programming knowledge. Furthermore, we used ontology-based learner modeling techniques to create student ontology, which provided an accurate representation of a student's information (e.g., knowledge level, programming history, and performance) and the mechanisms for tracking its continuous changes. We also designed problem ontology, providing a uniform approach to describe the characteristics of a programming problem. The instances of student and problem ontologies were visualized as knowledge graphs. Based on the classification system of programming errors and knowledge graphs, we designed an automated system for assigning programming tasks to students. We tested the effectiveness of the automated task assignment system using a quasi-experimental design. Students in the control group were asked to solve programming tasks assigned by their teacher throughout eight weeks. In the experimental group, students accomplished programming tasks assigned by the system. We found no significant difference in student performance between the two groups. This study has significant methodological and practical implications.

关键词： programming error Student ontology Problem ontology Knowledge graph Automated system

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Automatic detection and correction of programming faults for software applications

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JOURNAL OF SYSTEMS AND SOFTWARE 2005年第2期78卷 101-110页

作者： Deeprasertkul, P Bhattarakosol, P O'Brien, F Chulalongkorn Univ Fac Sci Dept Math Bangkok 10330 Thailand Univ Cent Queensland Fac Informat & Commun Sch Informat Technol Rockhampton Qld Australia

Software reliability is an important feature of a good software implementation. However some faults which cause software unreliability are not detected during the development stages, and these faults create unexpected problems for users whenever they arise. At present most of the current techniques detect faults while a software is running. These techniques interrupt the software process when a fault occurs, and require some forms of restart. In this paper Precompiled Fault Detection (PFD) technique is proposed to detect and correct faults before a source code is compiled. The objective of the PFD technique is to increase software reliability without increasing the programmers' responsibilities. The concepts of "pre-compilation" and "pattern matching" are applied to PFD in order to reduce the risk of significant damage during execution period. This technique can completely eliminate the significant faults in a software and thus, improves software reliability. (c) 2005 Elsevier Inc. All rights reserved.

关键词： programming error software fault software failure fault detection pattern matching software inspection

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Predicting Rough error Causes in Novice Programmers Using Cognitive Level 20th

Predicting Rough Error Causes in Novice Programmers Using Co...

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20th International Conference on Intelligent Tutoring Systems (ITS)

作者： Kim, Deok Yeop Lee, Woo Jin Kyungpook Natl Univ Sch Comp Sci & Engn Daegu South Korea

ISBN: (纸本)9783031630279;9783031630286

Novice programmers face various errors during the learning of a programming language. Most of them need help from instructors since they lack error resolution skills. On the other side, instructors spend a lot of time figuring out students' error causes. Long error detection times result in delayed and failed feedback, leading to a loss of student motivation. To support instructor's fast feedback, a detection method of error cause is needed. Existing detection methods, which are code-based, detect common and specific errors that can be identified by analyzing source code. These methods do not cover the diverse error patterns of novice programmers sufficiently, such as logical defects. To resolve this issue, it may be beneficial to detect rough and correct error causes of diverse error patterns. In this paper, a prediction method of rough error cause is proposed by considering not only source code, but also problem information, execution results, and the cognitive level indicating programming skills. We assume that different programming skills lead to different error patterns, which can help roughly but precisely predict error causes of runtime and logic errors in novice programmers. For performance evaluation, data from two introductory programming courses are used to validate the effectiveness of the cognitive level. Additionally, the usability for fast feedback is validated by comparing the error causes detection times of the instructors in each case.

关键词： error Detection Cognitive Level programming error Introductory programming Course Learning Taxonomy

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Ncr :: Century :: Rm-0141 Vol1 Procref Apr79 :: 12 Century251-300Hwcmds Jan76

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2016年

Ncr :: Century :: Rm-0141 Vol1 Procref Apr79 :: 12 Century251-300Hwcmds Jan76 by published by

关键词： apr79 binary century century series century251-300hwcmds character command command description command specifies commands control register description floating point hardware commands jan76 ncr ncr century processors procref product programming error repeat indicator rm-0141 series processors stored

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Uma ferramenta multiplataforma para prevenção de buffer overflow

Uma ferramenta multiplataforma para prevenção de buffer ov...

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作者： Mello, Paulo Estima

This paper presents a method to prevent the vulnerabilities caused by insecure programming which, usually, is an outcome of taking into account only the solution of a proposed problem or the development of new functionalities disregarding security on development of the system as a whole. The programming mistakes (in the context of the system security despite the systems functionality) are usually a result of the unawareness of the programmed about the vulnerabilities contained on the tools they use to develop software. The state of the art is briefly presented showing the current solutions related to preventing buffer overflows based on stack. Both compile time and post-compilation solutions (usually as part of the operating system) are the most widely used. In this work the proposed solution is demonstrated by a functional prototype which validates the model for a set of applications in two different platforms (Windows and Linux). The solution converges process instrumentation with a return address repository to prevent a function from returning to an address not legally specified. Testes of the prototype were performed in both platforms previously mentioned and have proved the correctness of the prototype by actually preventing exploitation on real case scenarios of real world applications. ...

关键词： Seguranca : Computadores Security Tolerancia : Falhas : Software Instrumentation Buffer overflow programming error Dissertação

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Protect and survive

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Information Professional 2004年第1期1卷 15-19页

作者： Ed Dickson

When the Morris worm surfaced in 1988 and infected many servers on the Internet, it revealed a serious flaw in several pieces of software including the Sendmail email software. The curious thing is that exploits used by the worm's creator to help it burrow into the host system are still being used today against software that has too many security flaws.

关键词： hackers Morris worm Security aspects of IT invasive software software security computer crime Data security technical security programming error

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Research on CNC Milling based on Subprograms

Research on CNC Milling based on Subprograms

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International Conference on Electrical Information and Mechatronics(ICEIM2012)

作者： Chen Xiaohong Chen Jianjun Yan Bicheng Zhejiang Institute of Mechanical & Electrical Engneering

ISBN: (纸本)9783037856901

Research the subprogram characteristics, combined with the actual production, introduce the subprogram application systematically, Illustrate flat milling, layer cutting, removal of residual material, realization of rough and finish processing, optimization of parts program, processing the same profiles and the same parts, etc., elucidate the method and technique of subprogramming, show the incremental programming of rotate instructions.

关键词： variable programming programming error ellipse

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