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23rd International Conference on Artificial Intelligence in Education (AIED)

作者： Lee, Jung A. Koprinska, Irena Jeffries, Bryn Univ Sydney Sch Comp Sci Sydney NSW Australia Univ Sydney Grok Acad Sydney NSW Australia

ISBN: (纸本)9783031116476;9783031116469

This paper investigates the common syntax errors students encounter when programming in Python using data from a large-scale online beginner course. We firstly analyse the error distribution to find differences between passing and failing students and then use clustering and Markov chains to identify clusters of student submissions with similar error pattern and how students move between these clusters during the course and between consecutive tasks. This type of analysis can be used by educators to understand student behaviour related to syntax errors and provide effective teaching support.

关键词： syntax errors Student behaviour Clustering Markov chains

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syntax and Stack Overflow: A methodology for extracting a corpus of syntax errors and fixes

Syntax and Stack Overflow: A methodology for extracting a co...

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35th IEEE International Conference on Software Maintenance and Evolution (ICSME)

作者： Wong, Alexander William Salimi, Amir Chowdhury, Shaiful Hindle, Abram Univ Alberta Dept Comp Sci Edmonton AB Canada

ISBN: (纸本)9781728130941

One problem when studying how to find and fix syntax errors is how to get natural and representative examples of syntax errors. Most syntax error datasets are not free, open, and public, or they are extracted from novice programmers and do not represent syntax errors that the general population of developers would make. Programmers of all skill levels post questions and answers to Stack Overflow which may contain snippets of source code along with corresponding text and tags. Many snippets do not parse, thus they are ripe for forming a corpus of syntax errors and corrections. Our primary contribution is an approach for extracting natural syntax errors and their corresponding human made fixes to help syntax error research. A Python abstract syntax tree parser is used to determine preliminary errors and corrections on code blocks extracted from the SOTorrent data set. We further analyzed our code by executing the corrections in a Python interpreter. We applied our methodology to produce a public data set of 62,965 Python Stack Overflow code snippets with corresponding tags, errors, and stack traces. We found that errors made by Stack Overflow users do not match errors made by student developers or random mutations, implying there is a serious representativeness risk within the field. Finally we share our dataset openly so that future researchers can re-use and extend our syntax errors and fixes.

关键词： stack overflow natural syntax errors python mining software repositories

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A Comparative Study of High and Low Performing Students' Visual Effort and Attention When Identifying syntax errors 18th

A Comparative Study of High and Low Performing Students' Vis...

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18th International Conference on Augmented Cognition (AC)

作者： Pacol, Caren A. Rodrigo, Maria Mercedes T. Tablatin, Christine Lourrine S. Ateneo Manila Univ Quezon City Metro Manila Philippines Pangasinan State Univ Urdaneta Pangasinan Philippines

ISBN: (纸本)9783031615689;9783031615696

Debugging is an important skill to learn for novice programmers and since compiler error messages are instrumental to the debugging process, investigating how novice programmers read and process these messages has become a subject of interest among computer science education researchers. Prior studies were valuable because they identified differences in the visual attention patterns of high and low performers and of experts and novices. They were, however, subject to certain limitations. In this study, we attempted to bridge these gaps by continuing the study of Rodrigo and Tablatin [18] and Tablatin and Rodrigo [21]. Using the methodology detailed in Rodrigo & Tablatin [18], we investigated how student programmers process literal and non-literal syntax errors embedded in Java and C++ programs. The analysis of eye tracking data collected from participants of two schools revealed a variation in visual effort and attention patterns of high and low performers. We conclude that low performance is not always associated with low visual attention. The novice programmer code comprehension errors may be rooted in other causes such as tracing and debugging strategies that students use, cognitive skills, individual preference of learning and programming experience. These factors are out of scope for this paper but may be the subject of future work.

关键词： Eye-tracking Compiler Error Messages Error Lines syntax errors Visual Attention Visual Effort

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A statistical analysis of syntax errors

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Computer Languages 1978年第4期3卷 227-240页

作者： Ripley, G.David Druseikis, Frederick C. The University of Arizona Tucson AZ 85721 United States Bell Laboratories Holmdel NJ 07733 United States

A study of errors made by Pascal programmers is described. The results of this study are discussed in relation to compiler syntax error recovery procedures. It is found that syntax errors made in practice are quite simple and occur relatively infrequently (generally at most one per sentence of the language). Also a few types of errors account for most occurrences. These and other findings from the study are helpful in evaluating compiler error handling procedures. A discussion of the relation between these errors and language constructs is presented. Other uses of the information obtained from the study are briefly described. © 1978.

关键词： Error correction Error recovery Error repair Error-proneness Language design syntax errors

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115Ways Not to Say Hello, World!: syntax errors Observed in a Large-Scale Online CS0 Python Course 22

115Ways Not to Say Hello, World!: Syntax Errors Observed in ...

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27th ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE)

作者： Jeffries, Bryn Lee, Jung A. Koprinska, Irena Grok Acad Sydney NSW Australia Univ Sydney Sch Comp Sci Sydney NSW Australia Univ Sydney Sch Comp Sci Sydney Australia

ISBN: (纸本)9781450392013

Online programming courses can provide detailed automatic feedback for code that fails to meet various test conditions, but novice students often struggle with syntax errors and are unable to write valid testable code. Even for very simple exercises, the range of incorrect code can be surprising to educators with mastery of a programming language. This research paper presents an analysis of the error messages from code run by students in an introductory Python 3 programming course, participated in by 8680 primary and high-school students from 680 institutions. The invalid programs demonstrate a wide diversity of mistakes: even for a one-line "Hello World!" exercise there were 115 unique invalid programs. The most common errors are identified and compared to the topics introduced in the course. The most generic errors in selected exercises are investigated in greater detail to understand the underlying causes. While the majority of students attempting an exercise reach a successful outcome, many students encounter at least one error in their code. Of these, many such errors indicate basic mistakes, such as unquoted string literals, even in exercises late in the course for which some proficiency of earlier concepts is assumed. These observations suggest there is significant scope to provide greater reinforcement of students' understanding of earlier concepts.

关键词： CS0 syntax errors

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Investigating Preprocessor-Based syntax errors 13

Investigating Preprocessor-Based Syntax Errors

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12th International Conference on Generative Programming: Concepts and Experiences

作者： Medeiros, Flavio Ribeiro, Marcio Gheyi, Rohit Univ Fed Campina Grande Campina Grande Brazil Univ Fed Alagoas Maceio Brazil

ISBN: (纸本)9781450323734

The C preprocessor is commonly used to implement variability in program families. Despite the widespread usage, some studies indicate that the C preprocessor makes variability implementation difficult and error-prone. However, we still lack studies to investigate preprocessor-based syntax errors and quantify to what extent they occur in practice. In this paper, we define a technique based on a variability-aware parser to find syntax errors in releases and commits of program families. To investigate these errors, we perform an empirical study where we use our technique in 41 program family releases, and more than 51 thousand commits of 8 program families. We find 7 and 20 syntax errors in releases and commits of program families, respectively. They are related not only to incomplete annotations, but also to complete ones. We submit 8 patches to fix errors that developers have not fixed yet, and they accept 75% of them. Our results reveal that the time developers need to fix the errors varies from days to years in family repositories. We detect errors even in releases of well-known and widely used program families, such as Bash, CVS and Vim. We also classify the syntax errors into 6 different categories. This classification may guide developers to avoid them during development.

关键词： Program Families Preprocessors syntax errors

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Repairing syntax errors in LR-based parsers 02

Repairing syntax errors in LR-based parsers

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Australasian Computer Science Conference

作者： Carl Cerecke Department of Computer Science University of Canterbury Private Bag 4800 Christchurch New Zealand

ISBN: (纸本)0909925828

When a compiler encounters a syntax error, it usually attempts to restart parsing to check the remainder of the input for any further errors. One common method of recovering from syntax errors is to repair the incorrect input string, allowing parsing to continue. This research presents a language independent method for repairing the input string to an LALR(1) parser. The method results in much faster repairs in general than an existing method, enabling some errors to be repaired that were previously too costly. Results are based on repairing syntax errors in Java programs from first year computer science students.

关键词： error repair syntax errors parsing LALR(1) bison yacc Parsing Buffaloes, American Bison syntax input string Parser

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Can ChatGPT Learn to Count Letters?

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COMPUTER 2025年第3期58卷 96-99页

作者： Conde, Javier Martinez, Gonzalo Reviriego, Pedro Gao, Zhen Liu, Shanshan Lombardi, Fabrizio Univ Politecn Madrid ETSI Telecomunicac Madrid 28040 Spain Univ Carlos III Madrid Madrid 28911 Spain Tianjin Univ Tianjin 300072 Peoples R China Univ Elect Sci & Technol China Chengdu 611731 Peoples R China Northeastern Univ Boston MA 02115 USA

Large language models (LLMs) struggle on simple tasks such as counting the number of occurrences of a letter in a word. In this paper, we investigate if ChatGPT can learn to count letters and propose an efficient solu... 详细信息

关键词： Basic Reproduction Number Letter Word syntax errors Strawberry Fine Tuned Model Sequence Of Letters Fine Tuning Process Previous Text Tokenized

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AUTOMATIC-GENERATION OF USEFUL syntax ERROR MESSAGES

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SOFTWARE-PRACTICE & EXPERIENCE 1986年第7期16卷 627-640页

作者： KANTOROWITZ, E LAOR, H Computer Science Department Technion–Israel Institute of Technology Haifa Israel

Many current compilers produce in some situations wrong error messages that mislead the user and harm his confidence in the system. It is demonstrated that a reliable and efficient syntax error handling system may be produced automatically by a compiler generator from the BNF specification of the language, and without any effort by the language implementor. This result is achieved in three ways: (a) Some errors may not be diagnosed without knowledge of the intentions of the programmer. Some compilers employ a sophisticated analysis that attempts to capture these intentions, but which is not always successful. Such an elaborate analysis is not employed here, and instead a list of all the legal corrections is displayed, so that the programmer may readily select the right one. (b) The recovery symbols are selected by a ‘careful’ algorithm resulting in a high probability for correct error recovery. (c) The ‘honest’ error messages show also the parts of the code which could not be analysed correctly because of errors, and where more errors may exist. Efficiency is achieved by computing the recovery sets once and for all at compiler generation time, so that a fast error recovery at program compilation time is achieved. Experiments with erroneous programs suggest that the method compares well with the best compilers that we have seen, and is specially good at the avoidance of wrong error messages.

关键词： syntax errors Error messages Error message generation

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TCMS: A Multi-sequence Log Parsing Method Based on Token Conversion

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IEEE Transactions on Dependable and Secure Computing 2024年第03期22卷 3028-3045页

作者： Wei, Mingkuan Wen, Jigang He, Shiming Xie, Kun Liang, Wei Xie, Gaogang Li, Kenli Zhu, Ziyu Hunan University The Ministry of Education Key Laboratory of 'Fusion Computing of Supercomputing and Artificial Intelligence' College of Computer Science and Electronics Engineering Changsha410012 China Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China Changsha University of Science and Technology School of Computer and Communication Engineering The Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha410114 China Beijing100045 China Columbia University Electrical Engineering Newyork10027 United States

Detailed system operations are recorded in logs. To ensure system reliability, developers can detect system anomalies through log anomaly detection. Log parsing, which converts semi-structured log messages into structured data, is a crucial step in log anomaly detection and advanced program analysis and verification. Despite the availability of various log parsing tools, they generally suffer from low parsing accuracy and slow efficiency due to the ignorance of variable characteristics and the use of costly pairwise comparison methods. In this paper, we propose a TCMS framework to parse logs, consisting of two main technologies. First, by studying 16 public log datasets, we find that most log variable tokens are structured variable tokens. Based on this discovery, we propose a token conversion algorithm to improve parsing accuracy. This algorithm converts the changed parts in structured variable tokens into wildcards (''), preventing these tokens from being directly identified as constant tokens. Second, to improve efficiency, we propose the LogMLCS algorithm, which intelligently constructs a graph to facilitate the extraction of common parts from multiple log messages at once, instead of using pairwise comparisons. Comprehensive experiments conducted on 16 log datasets reveal that our TCMS outperforms seven other parsing methods, achieving the highest parsing accuracy at the fastest speed. Furthermore, experimental results from running a log anomaly detection algorithm in conjunction with different log parsing methods demonstrate that TCMS significantly boosts detection accuracy. For instance, on the OpenStack dataset, our TCMS-facilitated log anomaly detection algorithm achieves a perfect F1-score, precision, and recall of 100% each, surpassing the best peer method by 32.2, 0.8, and 19.5 percentage points, respectively. © 2004-2012 IEEE.

关键词： Accuracy Anomaly Detection Clustering Algorithms Peer To Peer Computing Electronic Mail Source Coding Particle Separators Codes Tokenization Itemsets Log Parsing Log Analysis Token Conversion Log MLCS Log Parsing Pairwise Comparisons Low Accuracy Public Datasets Anomaly Detection Common Part Multiple Messages Long Short Term Memory Point Source Target Domain Logarithm Of The Number Captive Animals Multi Objective Optimization Problem Regular Expressions Biological Sequences Rule Based Methods Point In The Graph Kinds Of Datasets Template Generation Number Of Templates Similar Template Parse Tree syntax errors Harmonic Average Of Precision Log Length Earliest Methods Common Subsequence Precision And Recall Leaf Node Part Of The Message

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