Syntax Errors Research Articles

Statically Contextualizing Large Language Models with Typed Holes

Large language models (LLMs) have reshaped the landscape of program synthesis. However, contemporary LLM-based code completion systems often hallucinate broken code because they lack appropriate code context, particularly when working with definitions that are neither in the training data nor near the cursor. This paper demonstrates that tighter integration with the type and binding structure of the programming language in use, as exposed by its language server, can help address this contextualization problem in a token-efficient manner. In short, we contend that AIs need IDEs, too! In particular, we integrate LLM code generation into the Hazel live program sketching environment. The Hazel Language Server is able to identify the type and typing context of the hole that the programmer is filling, with Hazel's total syntax and type error correction ensuring that a meaningful program sketch is available whenever the developer requests a completion. This allows the system to prompt the LLM with codebase-wide contextual information that is not lexically local to the cursor, nor necessarily in the same file, but that is likely to be semantically local to the developer's goal. Completions synthesized by the LLM are then iteratively refined via further dialog with the language server, which provides error localization and error messages. To evaluate these techniques, we introduce MVUBench, a dataset of model-view-update (MVU) web applications with accompanying unit tests that have been written from scratch to avoid data contamination, and that can easily be ported to new languages because they do not have large external library dependencies. These applications serve as challenge problems due to their extensive reliance on application-specific data structures. Through an ablation study, we examine the impact of contextualization with type definitions, function headers, and errors messages, individually and in combination. We find that contextualization with type definitions is particularly impactful. After introducing our ideas in the context of Hazel, a low-resource language, we duplicate our techniques and port MVUBench to TypeScript in order to validate the applicability of these methods to higher-resource mainstream languages. Finally, we outline ChatLSP, a conservative extension to the Language Server Protocol (LSP) that language servers can implement to expose capabilities that AI code completion systems of various designs can use to incorporate static context when generating prompts for an LLM.

БҮГІНГІ БҰҚАРАЛЫҚ АҚПАРАТ ҚҰРАЛДАРЫ КЕҢІСТІГІНДЕГІ ЛИНГВОЭКОЛОГИЯ МӘСЕЛЕЛЕРІ

The number of mass media outlets in the information space is growing every year, with a particular increase in TV channels, websites, and portals disseminating information in the Kazakh language. A key issue today is not only to expand the quantity of Kazakh-language media but also to enhance the quality of their content. The aim of this research is to analyze the language culture within Kazakh-language media, particularly in the social sphere, to identify shortcomings, and to develop examples that align with the norms of the literary language. This article provides a detailed analysis of the issues related to linguoecology as reflected in the media, offering a comprehensive review of linguistic and stylistic errors, the use of inappropriate language, and the creation of artificial proverbs and sayings. The analysis is grounded in the principles of language culture and literary norms. Examples frequently encountered in media publications and television broadcasts are examined. Furthermore, the article suggests ways to improve the linguistic environment by addressing the influence of extralinguistic factors and provides scientific recommendations for enhancing language use. The research methods employed include analysis, control, and review. The conclusions and insights are particularly relevant for authors, journalists, and broadcasters who produce content in the Kazakh language.

Development of an enhanced educational IDE for 8051/8052 microcontroller

This paper presents and explains the development of an IDE for microcontroller 8051/8052 with new features and functionalities. These features include a built-in editor; a keypad editor and auto enabling/disabling array of buttons for eliminating syntax errors. The implementation of the interrupts is entirely based on the 8052 datasheets by taking into consideration the “Interrupt Priorities” and “Interrupt Enables”. Multiple view windows for internal ram, external ram, program memory and special function register enhance the developed IDE. One-pass cross technique was adopted for the assembling phase. The assembler plays the role of assigning addresses, translating mnemonics, and recognizing errors by considering all the 255 instructions and directives Step-by-step, skip-line debugging, breakpoints to be inserted at any part of the source code have been included to ameliorate the students’ interaction with the system. An option is provided to users to manually create external incoming transmission for triggering purposes. The IDE passes the test and verification processes successfully.

ChatGPT Enhances Programming Skills of Computer Engineering Students

This research paper aims to outline the role of ChatGPT as one artificial intelligence technology that has progressed in depth and positive effect on the field of education and research with highly capable language developed by OpenAI. This technology presents an exciting opportunity for students and educators, including personalize feedback, increased ability accesses, lesson preparation, assessment and new way of teaching complexity such as code correction and completion, automatic syntax error fixing, code snippet suggestion, code refactoring suggestions and optimization, generate of missing code, document, text to code, and answering technical queries. The experimented was adopted with 51 students in final grade of Computer Engineering, Souphanouvong University and selected 30 students in interviews to collect data for analysis and evaluation. The results show that students with using ChatGPT to help in programming they can more understandable by able to explain, giving examples, concepts, technology of complex method, searching related information and technical solving problem, which can improve programming skills and careers in software development in the future. However, students are still concerned about the accuracy of information provided, loss personal interaction, data privacy issue and price.

КАРАТАЙ САРТБАЕВ – ӨЗ МЕЗГИЛИНИН БИРИНЧИ МУГАЛИМИ

The content of the article is devoted to the works of the famous scientist, professor Karatai Sartbaev. The article classifies the scientist's works for elementary grades. The orthographic and stylistic directions of the scientist's works are considered. The spelling errors of the student when teaching spelling in elementary grades are classified and ways to eliminate them are given. The causes of spelling errors, methods and forms of working with them are shown. Stylistic errors of younger students, methods of working with them, forms and methods are analyzed. The causes of stylistic errors, methods of working with them are given. Spelling, punctuation errors, methods of eliminating them when eliminating stylistic errors are proposed. He devoted more than 64 years of his pedagogical activity to the methods of teaching literacy, reading and writing to children of primary school age, the methods of teaching spelling of the Kyrgyz language, the stylistics of the Kyrgyz language, groups of words, worked on these branches of the Kyrgyz language. language from a theoretical and practical point of view.

Weakly Supervised Exaggeration Transfer for Caricature Generation With Cross-Modal Knowledge Distillation.

Caricature generation aims to translate portrait photos into caricatures with exaggerated and hand-drawn artistic styles. Previous methods faced challenges in creating diverse and meaningful exaggeration effects, yielding unsatisfactory and uncontrollable results. To overcome this, we proposed ETCari, a novel weakly supervised exaggeration transfer network. ETCari enables the learning of diverse exaggeration caricature styles from various artists, better meeting individual customization requirements and achieving diversified exaggeration while retaining identity features. Specifically, we use the thin-plate spline control point deformation field as the ground truth, serving as the loss for weakly supervised learning to address the challenge of no labels. We convert input to an intermediate modality for domain adaptation, training a teacher model. Subsequently, we perform cross-modal knowledge distillation to train the student model, simplifying preprocessing during inference and avoiding the impact of face parser errors. Experiments on the WebCaricature dataset demonstrate that ETCari effectively performs exaggeration transfer, generating appealing caricatures.

Rhymes and Syntax: A Morpho-Syntactic Analysis of Czech Poetry

A linguistically informed distant reading presupposes an adequate performance of Natural Language Processing tools. This article describes our evaluation of the UDPipe parser on a manually annotated sample of nineteenth-century Czech poetry in the following steps: (1) creation of a documented data set for this domain (poetry, nineteenth century, Czech); (2) domain-specific annotation decisions; (3) error analysis. The sample consisted of 29 randomly selected poems which were first automatically tagged and parsed with the UDPipe parser and then manually checked word by word. The following features were checked: word segmentation (chunking), lemmatization, part of speech assignment, assignment of more fine-grained morphological details, the position in the syntactic dependency tree (selection of the syntactic parent), as well as the label of the syntactic relation between the word and its parent. The findings were analyzed. The most typical parser errors are associated with complex noun phrases that contain other noun(s) as modifier(s), especially when these occur in a poetry-specific word order, that is, preposed to the governing noun. On the other hand, neither archaic orthography nor neologisms posed substantial issues.

The development of English language connected speech perception skills: an empirical study on Chinese EFL children.

Connected speech processes (CSPs) occur randomly in everyday conversations of native speakers; however, such phonological variations can bring about challenges for non-native listeners. Looking at CSP literature, there seems to be very few studies that involved young foreign language learners. Therefore, the present study aimed to explore the development of connected speech perception skills by focusing on 201 9- to 12-year-old Chinese EFL children. It also incorporated systematic error analysis to further probe into the specific perceptual difficulties. The results indicate that: (1) Despite a significantly ascending trend for the overall growth of perception skills, no significant differences were found between 11 and 12 year olds in elision and contraction, which suggests that the developmental trend varied depending on different CSP types; (2) Although random errors decreased with age, the number of lexicon and syntax errors gradually increased, and the distribution of perceptual errors shifted from the level of words and syllables to that of phonemes; (3) The primary types of errors resulting in the perception difficulties for elision and contraction were consonant errors, grammatical errors and morphology errors. Ergo, this study enhances the understanding of connected speech perception among EFL children and provides some implications for EFL/ESL listening instructions.

Analysis of Syntactic Errors in Writing of Third Semester Students of the Department of English and Literature

Purpose of the study: This research presents the results of research on syntactic errors in writing produced by third semester students of the Department of English and Literature, Alauddin State Islamic University, Makassar. The aim of this research is to identify the dominant types of syntactic errors and provide in-depth insight into the error patterns that frequently occur. Methodology: The research method used is descriptive qualitative by collecting data from students' final written exam sheets. Data collection involved obtaining final writing exam sheets from third semester AG 1&2 students enrolled in the Department of English and Literature at Alauddin State Islamic University Makassar. Main Findings:The analysis results show that the most common syntax errors include omissions, additions, information errors, and ordering errors. Novelty/Originality of this study:The novelty of this research lies in the focus of analyzing syntactic errors during the third semester students studying English and Literature. with a more comprehensive and detailed approach than previous research.

An Analysis of the Most Common L1 Interference Grammar, Vocabulary and Syntax Errors of Lithuanian Learners in Written English

Abstract The paper describes a study on the most common English mistakes among Lithuanians in written tasks on the topic of business and finance. The study was conducted with high school students and university students, and the effectiveness of error feedback in reducing the occurrence of errors was also examined by comparing 2 written texts of each participant. Grammatical errors related to the use of articles and punctuation were found to be the most common types of errors, and feedback on errors was found to be an effective tool in increasing learner motivation and understanding, reducing common errors but not reducing the most common types of errors. The research showed that detailed, personalized feedback can help minimize mistakes in writing assignments, especially if it can be accessed during or in between tasks. However, it is uncertain whether this method will have long-lasting benefits or if improvements are dependent on continual feedback reference. Participants were advised to keep their error feedback forms and utilize teachers’ feedback as a constant guide for improvement. We plan to utilize data on frequently occurring errors to conduct additional research on tackling and enhancing language errors that have become ingrained, employing various strategies. Based on the findings, directions for future research were identified. In the future we intend to carry out a study, using controlled texts with a pre-determined number of errors in diagnostic testing. This would allow us to more precisely analyse learners’ improvements in the use of given structures, through a more extensive research.

Analyzing novice and competent programmers' problem-solving behaviors using an automated evaluation system

Background and ContextIn today's tech-driven world, programming courses are crucial. Yet, teaching programming is challenging, leading to high student failure rates. Understanding student learning patterns is key, but there's a lack of research utilizing tools to automatically collect and analyze interaction data for insights into student performance and behaviors. ObjectivesStudy aims to compare problem-solving behaviors of novice and competent programmers during coding tests, identifying patterns and exploring relationships with program correctness. MethodWe built an online system with programming challenges to collect behavior data from novice and competent programmers. Our system analyzed data using various metrics to explore behavior-program correctness relationships. FindingsAnalysis showed distinct problem-solving behavior patterns. Competent programmers had fewer syntax errors, spent less time fixing bugs, and had higher program correctness. Novices made more syntax errors and spent more time fixing coding errors. Both groups used tabs for code structure, but competent programmers introduced unfamiliar variables more often and commented on them afterward. Emphasizing iterative revisions and active engagement enhances problem-solving skills and programming proficiency. Radar charts are effective for identifying improvement areas in teaching programming. The relationship between behavior and program correctness was positively correlated for competent programmers but not novices. ImplicationsStudy findings have implications for programming education. Radar charts help teachers identify course improvement areas. Novices can learn from competent programmers' behavior. Instructors should encourage continuous skill improvement through revisions and engagement. Identified unfamiliar programming aspects offer insights for targeted learning.

Health Care System Application (Good Care) Android-Based at Primary Health Care (Case Study at Clinic of Wijaya Kusuma)

The development of information systems is currently experiencing very rapid development which we now recognize as the era of digitalization, one of which is in the field of health services. Primary healthcare is a type of medical health service that provides health services to individuals at the first level health service. The problem that most often arises is related to the registration system which is still done manually by recording in the registration book, thereby allowing the risk of errors occurring and the process of searching for visit data taking a long time. This research aims to improve better service for visits so it is necessary a good, systematic and integrated data handling and processing in a developing service system, even at the level of independent health service practice. This research method uses Field Observation, Library Research, Interviews, Study Research Literature (Research Literature Study), System Design, System Implementation and Testing. Application system from syntax errors and functionally produces functions as expected. The health service system application (Good Care) has been tested and can be used so that it can increase the effectiveness and efficiency of health services at the independent doctor's practice at Clinic of Wijaya Kusuma.

Evaluation of ChatGPT and Gemini large language models for pharmacometrics with NONMEM.

To assess ChatGPT 4.0 (ChatGPT) and Gemini Ultra 1.0 (Gemini) large language models on NONMEM coding tasks relevant to pharmacometrics and clinical pharmacology. ChatGPT and Gemini were assessed on tasks mimicking real-world applications of NONMEM. The tasks ranged from providing a curriculum for learning NONMEM, an overview of NONMEM code structure to generating code. Prompts in lay language to elicit NONMEM code for a linear pharmacokinetic (PK) model with oral administration and a more complex model with two parallel first-order absorption mechanisms were investigated. Reproducibility and the impact of "temperature" hyperparameter settings were assessed. The code was reviewed by two NONMEM experts. ChatGPT and Gemini provided NONMEM curriculum structures combining foundational knowledge with advanced concepts (e.g., covariate modeling and Bayesian approaches) and practical skills including NONMEM code structure and syntax. ChatGPT provided an informative summary of the NONMEM control stream structure and outlined the key NONMEM Translator (NM-TRAN) records needed. ChatGPT and Gemini were able to generate code blocks for the NONMEM control stream from the lay language prompts for the two coding tasks. The control streams contained focal structural and syntax errors that required revision before they could be executed without errors and warnings. The code output from ChatGPT and Gemini was not reproducible, and varying the temperature hyperparameter did not reduce the errors and omissions substantively. Large language models may be useful in pharmacometrics for efficiently generating an initial coding template for modeling projects. However, the output can contain errors and omissions that require correction.

COMPARATIVE TEXTUAL STUDY BETWEEN THE MANUSCRIPT AND PUBLICATION OF "TAZKIRAI QAYYUMI" (IN THE EXAMPLE OF THE WORK OF SOME POETS)

This article is devoted to the textual-comparative study between the Tazkira manuscript and the edition of fiqras No 3-16 in Polatjon Qayyumi's work “Tazkirai Qayyumiy”. A comparison was made between the manuscript and published versions of the tazkira. The changes of orthography between translation and publication version, stylistic errors, erroneous information, omitted places in the translation, and information added by academician Aziz Qayumov were identified.

An Error Analysis of Expository Essay Writing of English as Second Language (ESL) Undergraduate Learners in Pakistan

This study focuses on the expository essay writing of undergraduate of English as Second Language (ESL) learners in Pakistan. The study aims to identify the most common errors made by these students and analyze the reasons behind those. The study uses the quantitative method to conduct error analysis. To conduct error analysis, a sample of eight expository essays written by undergraduate second language learners of the selected college is analyzed, and the errors are identified and categorized. The data analysis identified the errors made by second language learners in expository essays, including grammatical, vocabulary, syntax and organization errors. The sample students also struggled with using the correct verb tense, forming proper sentence structure, or accurately using prepositions. The results of the study provided insights into the areas that need improvement in the learning of English as a second language in Pakistan, as well as the strategies to address the identified errors. Further research may include more areas of grammar.

Can LLM Replace Stack Overflow? A Study on Robustness and Reliability of Large Language Model Code Generation

Recently, large language models (LLMs) have shown an extraordinary ability to understand natural language and generate programming code. It has been a common practice for software engineers to consult LLMs when encountering coding questions. Although efforts have been made to avoid syntax errors and align the code with the intended semantics, the reliability, and robustness of the code generation from LLMs have not yet been thoroughly studied. The executable code is not equivalent to reliable and robust code, especially in the context of real-world software development. For example, the misuse of APIs in the generated code could lead to severe problems, such as resource leaks, program crashes, etc. Existing code evaluation benchmarks and datasets focus on crafting small tasks such as programming questions in coding interviews, which, however, deviates from the problem that developers would ask LLM for real-world coding help. To fill the missing piece, in this work, we propose a dataset RobustAPI for evaluating the reliability and robustness of code generated by LLMs. We collect 1208 coding questions from Stack Overflow on 18 representative Java APIs. We summarize the common misuse patterns of these APIs and evaluate them on current popular LLMs. The evaluation results show that even for GPT-4, 62% of the generated code contains API misuses, which would cause unexpected consequences if the code is introduced into real-world software.

A ChatGPT-MATLAB framework for numerical modeling in geotechnical engineering applications

ChatGPT has recently emerged as a representative of Large Language Models (LLMs) that have brought evolutionary changes to our society, and the effectiveness of ChatGPT in various applications has been increasingly reported. This study aimed to explore the potential of employing programming performance driven by ChatGPT responses to conversational prompts in the field of geotechnical engineering. The tested examples included the analysis of seepage flow and slope stability, and the image processing of X-ray computed tomographic image for partially saturated sand. For each case, the prompt was initially fed by a narrative explanation of the problem attributes such as geometry, initial conditions, and boundary conditions to generate the MATLAB code that was in turn executed to evaluate the correctness and functionality. Any errors and unanticipated results were further refined by additional prompts until the correct outcome was achieved. ChatGPT was able to generate the numerical code at a considerable level, demonstrating creditable awareness of the refining process, when meticulous prompts were provided based on a comprehensive understanding of given problems. While ChatGPT may not be able to replace the entire process of programming, it can help minimize sloppy syntax errors and assist in designing a basic framework for logical programming.

Syntax Error: Variations in the Verbiage of Prescription Labels for Pediatric Liquid Medications.

Background Pharmacists can modify prescriptions from prescribers for clarity and patient understanding, provided the confines of the original order are met, yet the verbiage used by pharmacists isnot standardized. Prescription directions for children, especially children eight years old and younger, should be written with the verb "give" instead of "take" as their parents or caregivers are expected to administer them. Errors in prescribing, dispensing, and administering medication comprise a significant portion of preventable medical errors in children. To intervene and assist pharmacies, we must first identify and characterize the problem. This study aimed to determine if there is a relationship between prescribers and pharmacists using the verb "give" or "take" when prescribing and printing prescription labels for pediatric liquid medications. In addition, it aimed to determine if there is a relationship between chain pharmacies and independent pharmacies using the verb "give" or "take" when printing labels for pediatric liquid medications. Methodology The participants in this study were caregivers of children eight years old and younger who had been prescribed a new liquid medication.We recruited prescribers in North Louisiana to serve as a referral base for the study. Caregivers were referred to the study by prescribers. A rubric was created to investigatethe text ofprescription labels. Fisher's exact test was used to determine the relationship between verb choice and prescribers and pharmacists, as well as the relationship between verb choice and chain pharmacies and independent pharmacies. Results A total of 11 (26.83%) prescriber texts used the verb "give," while 12 (29.27%) prescriber texts used the verb "take." Overall, 18 (43.90%) prescriber texts did not use a verb at all. Of these 18 prescriber texts that did not include a verb, 14 prescription labels used the verb "give," and four used the verb "take." In total, 10 (23.81%) chain pharmacy prescription labels used the verb "give," and 10 (23.81%) chain pharmacy prescription labels used the verb "take." The two-tailed p-value of Fisher's exact test comparing verb choice between prescribers and pharmacists equaled 0.0001. A total of 19 (46.34%) independent pharmacy prescription labels used the verb "give," and two (4.88%) independent pharmacy prescription labels used the verb "take." The two-tailed p-value of Fisher's exact test comparing verb choice between chain pharmacies and independent pharmacies equaled 0.0063. Conclusions The relationship between prescriber texts and pharmacist prescription labels shows a relationship between their verb choice (p = 0.0001). The relationship between chain pharmacy and independent pharmacy prescription labels shows a relationship between their verb choice (p = 0.0063). This study has illuminated how medication orders begin before they are modified, if necessary, for the patient's clarity and understanding. This study can be used to instruct prescribers on writing more accurate prescription instructions to prevent medical errors, and it can help pharmacists recognize potential dangers and prevent them through editing.

Intelligent Program Correction and Evaluation System

The growing popularity of computing applications has sparked the interest of students in computer programming languages. Minor mistakes are prevalent while writing small code blocks due to the coder’s lack of knowledge and carelessness. Instead of merely providing syntax warnings, it would be better to offer developers with an Integrated Development Environment (IDE) that can automatically correct short code blocks containing mistakes. This makes code composition easier and more time-efficient, thus improving the efficacy of large-scale development environments. Because Python is becoming more popular, the goal of our study is to enhance the efficiency of writing Python code by offering an automatic code-correcting approach. Furthermore, automatic program evaluation has been performed to assist in the debugging of small code blocks, which will ultimately be employed in the creation of real-time computer applications. The proposed technique would be useful for new learners who wish to create small Python code blocks for ease of writing and debugging on online platforms (like edX, Coursera, and Udacity). One of the major contributions of the project is to create an erroneous dataset of Python coding that contains all potential forms of probable syntax errors. The dataset induces variety through the use of multiple coding templates and is used to train deep learning models. We used the state-of-the-art text-to-text T5 transformer network model to automatically repair and evaluate the incorrect code. The outcomes of auto-correction are examined using the ROUGE and BLEU scores, as well as accuracy. The model corrects Python code with single, double, and the multiple number of errors with greater than 80% accuracy. Similarly, the performance of the basic T5 transformer network for program auto-evaluation with and without mistakes has been examined, and the model achieves greater than 65% accuracy in both cases. The proposed T5 base transformer outperforms the SOTA auto-correction models in terms of accuracy, according to a comparison study of the proposed method with the earlier techniques for auto-correcting codes.

UTAPIS Indonesian Word Error Detection Application: Design and Development

People misspell and incorrectly write Indonesian words without even recognizing it. Poor and incorrect language proficiency is a common cause of word spelling mistakes. Since PT Tribun Digital Online publishes between 600 and 700 articles a day, the U-Tapis website might be of great assistance to journalists in identifying grammatical problems. Three separate cities—Jakarta, Makassar, and Solo—have participated in the testing of the U-tapis website. @ Tribun Digital Online, PT. This department is in charge of using the Flask framework to create the U-tapis website design. This study develops software or applications that are comparatively brief, succinct, and quick using the RAD (Rapid Application Development) technique. In addition to system development methods. The implementation of this method uses the U-tapis Sentence Syntax Error Detection algorithm which functions to detect an algorithm comparing two pieces of data, calculated using a formula called the Jaccard Index to check spelling errors in words. UAT test results produce all the features according to the wishes of the company owner based on the problems faced.