Error Messages Research Articles

Avoiding Signature Avoidance in ML Modules with Zippers

We present ZipML, a new path-based type system for a fully fledged ML-module language that avoids the signature avoidance problem. This is achieved by introducing floating fields, which act as additional fields of a signature, invisible to the user but still accessible to the typechecker. In practice, they are handled as zippers on signatures, and can be seen as a lightweight extension of existing signatures. Floating fields allow to delay the resolution of instances of the signature avoidance problem as long as possible or desired. Since they do not exist at runtime, they can be simplified along type equivalence, and dropped once they became unreachable. We give four rules for the simplification of floating fields without loss of type-sharing and present an algorithm that implements those rules. Remaining floating fields may fully disappear at signature ascription, especially in the presence of toplevel interfaces. Residual unavoidable floating fields can be shown to the user as a last resort, improving the quality of error messages. Besides, ZipML implements early and lazy strengthening, as well as lazy inlining of definitions, preventing duplication of signatures inside the typechecker. The correctness of the type system is proved by elaboration into M𝜔 , which has itself been proved sound by translation to F𝜔. ZipML has been designed to be an improvement over OCaml that could be retrofitted into the existing implementation.

An Empirical Study on Common Sense-Violating Bugs in Mobile Apps

Mobile applications are widely used by billions of users in their daily work and life. Such GUI software is prone to bugs, potentially degrading user experience. Notably, many bugs in mobile apps are reported by end-users who cannot access the requirements of the app or test cases accompanied by explicitly specified test oracles. It may suggest that such bugs are not identified in the traditional way, i.e., by comparing the actual behaviors of the apps against their expected behaviors explicitly specified in the requirements or test cases. Instead, such bugs are often identified by comparing the actual behaviors against users’ common knowledge of apps, noted as common sense. We refer to such bugs as common sense-violating bugs. Although it is well-known that common sense-violating bugs are common in mobile apps, it remains unclear how popular they are and what kind of common sense principles are violated by them, let alone the relationship among the violated common sense principles. To this end, in this paper, we conduct the first large-scale empirical study on common sense-violating bugs in open-source mobile apps. We manually analyzed 2,808 real-world bug reports across 948 open-sourced mobile apps on GitHub. Our analysis results suggest that 1,006 (35.8%) out of the 2,808 bugs pertain to common sense-violating bugs. From those common sense-violating bugs, we identified a set of common sense principles violated by the buggy behaviors, and built a taxonomy for the common sense principles. Such principles fall into three categories: UI content-related common sense principles, UI layout-related common sense principles, and interaction-related common sense principles. By analyzing the frequency of the common sense principles being violated, we observed that a small set of common sense principles were frequently violated by the majority of common sense-violating bugs: 18 common sense principles, accounting for only 5% of the violated common sense principles, were violated by more than half of the common sense-violating bugs. These findings suggest that identifying the most frequent common sense-violating bugs could be achieved by using a small set of critical common sense principles, which may significantly reduce the cost of common sense-based bug detection. Finally, to demonstrate the feasibility of automated bug detection with common sense-based test oracles, we propose an automated approach to validating whether a given test run violates the most frequently violated common sense principle: No raw error message. Our evaluation results suggest that the automated approach is accurate, whose precision and recall are 91.3% and 91.6%, respectively.

A Transformation of the Way of Thinking: Key Principles in Programming Education Inspired by Pascal

The work of Niklaus Wirth, designer of the Pascal programming language, has led to the introduction of programming in schools in many countries often leading to a transformation in the way of thinking. In this article, we provide a retrospective analysis of the Lithuanian experience driven by Pascal and discuss the main ideas about teaching programming originating from this experience. We conducted a qualitative study by means of interviews with experts involved in the development of programming education during its early phases to examine their memories and perspectives. Programming education in Lithuania started with the Pascal-inspired Young Programmers’ School by Correspondence, founded in 1981, which had a great influence on the Lithuanian programming elite. For this purpose, a compiler for a subset of Pascal was developed for students taking their first steps towards programming, or more precisely, algorithmic thinking. Many innovations were developed and brought into practice. The ones that have proved their worth and have not lost their relevance are the subject of this article. These include assessing program text readability, cultivating programming style, program reading tasks, creative thinking tasks, problem-solving approaches, detailed compiler error messages, automatic error fixing, and compiler advice to novice programmers. While some concepts became obsolete with technological advancements, others remain relevant, directly or as inspiration for new ideas, forming the basis of this study.

Why Do People Gather? A Study on Factors Affecting Emotion and Participation in Group Chats

Group chat socialization is increasingly central to online activities, yet design strategies to enhance this experience remain underexplored. This study builds on the Stimuli–Organism–Response (SOR) framework to examine how usability, chat rhythm, and user behavior influence emotions and participation in group chats. Using data from 546 users in China, a relevant demographic given the dominance of platforms like WeChat in both social and professional settings, we uncover insights that are particularly applicable to highly connected digital environments. Our analysis shows significant relationships between usability (γ = 0.236, p < 0.001), chat rhythm (γ = 0.172, p < 0.001), user behavior (γ = 0.214, p < 0.001), and emotions, which directly impact participation. Positive emotions (γ = 0.128, p < 0.05) boost participation, while negative emotions (γ = −0.144, p < 0.01), particularly when linked to user behaviors, reduce it. Additionally, we discussed the mediating effects, notably that usability significantly impacts participation through positive emotions, while user behavior exerts a significant influence on participation through negative emotions. This research offers actionable design strategies, such as tailoring sensory inputs to reduce cognitive load and implementing reward systems to motivate participation. Positive feedback mechanisms enhance engagement by leveraging the brain’s reward systems, while optimized error messages can minimize frustration. These insights, which are particularly relevant for China’s active group chat culture, provide a framework to improve platform design and contribute valuable findings to the broader HCI field.

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.

Refinement Type Refutations

Refinement types combine SMT decidable constraints with a compositional, syntax-directed type system to provide a convenient way to statically and automatically check properties of programs. However, when type checking fails, programmers must use cryptic error messages that, at best, point out the code location where a subtyping constraint failed to determine the root cause of the failure. In this paper, we introduce refinement type refutations, a new approach to explaining why refinement type checking fails, which mirrors the compositional way in which refinement type checking is carried out. First, we show how to systematically transform standard bidirectional type checking rules to obtain refutations. Second, we extend the approach to account for global constraint-based refinement inference via the notion of a must-instantiation: a set of concrete inhabitants of the types of subterms that suffice to demonstrate why typing fails. Third, we implement our method in HayStack—an extension to LiqidHaskell which automatically finds type-refutations when refinement type checking fails, and helps users understand refutations via an interactive user-interface. Finally, we present an empirical evaluation of HayStack using the regression benchmark-set of LiqidHaskell, and the benchmark set of G2, a previous method that searches for (non-compositional) counterexample traces by symbolically executing Haskell source. We show that HayStack can find refutations for 99.7% of benchmarks, including those with complex typing constructs (e.g., abstract and bounded refinements, and reflection), and does so, an order of magnitude faster than G2.

Exploring the Utilisation of Generative AI Tools by Undergraduate First-Year Mechanical Engineering Students in Programming Assessments.

Integrating the fundamentals of computer science and programming skills into the undergraduate engineering curriculum has been a primary focus for many educational institutions worldwide. Learning the basics of programming from the beginning of undergraduate engineering education allows students to incorporate such skills into their future work easily. Therefore, an introductory programming course for first-year undergraduate students has been running since 2021 in the Mechanical Engineering Department at University College London intending to teach the fundamentals of Python programming language. However, it is well-known that generative artificial intelligence (Gen AI) tools in higher education are moving so fast that a wait-and-see approach cannot be taken. These applications have received much global attention from academics on their impact and proper use within the teaching-learning process. This paper investigates first-year undergraduate mechanical engineering students' use of Gen AI tools in their programming assessment. The results show that 60% of the cohort used tools that helped mainly to check their code, improve their English language, and understand error messages. However, 40% abstained from using any. Based on these findings, recommendations on how Gen AI tools can be utilised by undergraduate students in ways that support their learning and enhance their ability to achieve learning outcomes are made.

Electronic health record system use and documentation burden of acute and critical care nurse clinicians: a mixed-methods study.

Examine electronic health record (EHR) use and factors contributing to documentation burden in acute and critical care nurses. A mixed-methods design was used guided by Unified Theory of Acceptance and Use of Technology. Key EHR components included, Flowsheets, Medication Administration Records (MAR), Care Plan, Notes, and Navigators. We first identified 5 units with the highest documentation burden in 1 university hospital through EHR log file analyses. Four nurses per unit were recruited and engaged in interviews and surveys designed to examine their perceptions of ease of use and usefulness of the 5 EHR components. A combination of inductive/deductive coding was used for qualitative data analysis. Nurses acknowledged the importance of documentation for patient care, yet perceived the required documentation as burdensome with levels varying across the 5 components. Factors contributing to burden included non-EHR issues (patient-to-nurse staffing ratios; patient acuity; suboptimal time management) and EHR usability issues related to design/features. Flowsheets, Care Plan, and Navigators were found to be below acceptable usability and contributed to more burden compared to MAR and Notes. The most troublesome EHR usability issues were data redundancy, poor workflow navigation, and cumbersome data entry based on unit type. Overall, we used quantitative and qualitative data to highlight challenges with current nursing documentation features in the EHR that contribute to documentation burden. Differences in perceived usability across the EHR documentation components were driven by multiple factors, such as non-alignment with workflows and amount of duplication of prior data entries. Nurses offered several recommendations for improving the EHR, including minimizing redundant or excessive data entry requirements, providing visual cues (eg, clear error messages, highlighting areas where missing or incorrect information are), and integrating decision support. Our study generated evidence for nurse EHR use and specific documentation usability issues contributing to burden. Findings can inform the development of solutions for enhancing multi-component EHR usability that accommodates the unique workflow of nurses. Documentation strategies designed to improve nurse working conditions should include non-EHR factors as they also contribute to documentation burden.

Evaluasi Usability Aplikasi English Competency Test (ECT) Menggunakan Metode Computer System Usability Questionner (CSUQ): Studi Kasus Pusat Bahasa Institut Teknologi Telkom Purwokerto

The advancement of the digital era provides many conveniences in various fields. This is because its presence can support the sharing of human activities. One of the conveniences that can be felt is the digital transformation in the implementation of English certification to improve the effectiveness and quality of its implementation. English certification through a web-based English Competency Test (ECT) is a form of digital transformation used to certify English at the Telkom Institute of Technology Purwokerto. However, in practice, the use of the English Competency Test (ECT) system still has several shortcomings, such as the appearance on the English Competency Test (ECT) is less attractive and difficult to understand, the access process is less responsive and some features are not functioning properly. Therefore, to correct the existing deficiencies, it is necessary to evaluate the effectiveness of use. This study is here to analyze the quality assessment of the English Competency Test (ECT) system. By using the Computer System Usability Questioner (CSUQ) instrument in the form of a questionnaire to assess the effectiveness, efficiency and convenience provided by ECT. The results showed that the usability of the English Competency Test (ECT) system was “Neutral” with a feasibility percentage of 58%. Neutral indicates that the English Competency Test (ECT) can be used, but still cannot be said to be good. Based on the research results, improvements can be made to the Information Quality section by providing error messages and improvements and improvements to the Interface Quality section to make it more user friendly.

Evaluation of a whole blood point-of-care coagulation analyzer in dogs.

To compare the accuracy of a point-of-care coagulation analyzer (POCCA) with a reference laboratory coagulation analyzer (LabCA) and to evaluate for confounding factors that could alter the performance of the POCCA. Prospective, observational study. Two university veterinary teaching hospitals. Forty-three client-owned dogs undergoing coagulation testing between April 2020 and June 2021. Samples were obtained from dogs undergoing coagulation testing as part of a diagnostic workup. Prothrombin time (PT) and activated partial thromboplastin time (aPTT) were measured on the POCCA and on the LabCA. PCV, platelet count, total plasma protein, hyperbilirubinemia, hemolysis, lipemia, and autoagglutination were recorded. Moderate correlation was seen for PT and strong correlation was seen for aPTT between the POCCA and the LabCA (PT: 0.59, P<0.0001; aPTT: 0.71, P<0.0001). The POCCA results were consistent with normal or hypocoagulable samples for 30 of 38 PT and 33 of 37 aPTT results, as identified by the LabCA. Samples with PCV of 30%-55% were moderately correlated (PT: 0.63, P=0.0004; aPTT: 0.63, P=0.0003), but those outside that range were more likely to register an error message on the POCCA or provide disparate results. When hemolysis was present, there was a weak correlation between the POCCA and the LabCA for PT (rho: 0.38 [95% confidence interval: 0.19-0.76], P=0.18) and a strong correlation for aPTT (rho: 0.86 [95% confidence interval: 0.62-0.95], P<0.0001). Samples with hyperbilirubinemia were strongly correlated for PT (0.97, P=0.002) but not for aPTT. Lipemia and autoagglutination were not observed. There was an acceptable correlation in patients with PCV within the manufacturer's recommended reference range; however, measurements on samples with PCV outside the reference range were inconsistent with the LabCA. Caution should be used when using the POCCA in patients with coagulopathy and anemia or other potential confounders.

Ideal suturing technique for robot-assisted microsurgical anastomoses

The development of novel robotic devices specifically designed for open microsurgery leads to increasing applications in reconstructive procedures. While initial studies revealed improved precision and surgical ergonomics upon robotic assistance, surgical time was consistently observed to be increased. This study compares two robotic suturing techniques using the Symani Surgical System and RoboticScope in a preclinical setting, to further leverage the benefits of novel robotic devices in microsurgery. Six experienced microsurgeons performed three microvascular anastomoses with a “steady-thread” suturing technique and a “switch-thread” technique on 1.0-mm-diameter artificial silicone vessels. Time for anastomosis and participant’s satisfaction with the techniques and robotic setup were recorded. Anastomosis quality and microsurgical skills were assessed using the Anastomosis Lapse Index and Structured Assessment of Microsurgery Skills. Lastly, technical error messages and thread ruptures were quantified. Knot tying was significantly faster and evaluated significantly better by participants using the steady-thread technique (4.11 ± 0.85 vs. 6.40 ± 1.83 min per anastomosis). Moreover, microsurgical skills were rated significantly better using this technique, while both techniques consistently led to high levels of anastomosis quality (2.61 ± 1.21 vs. 3.0 ± 1.29 errors per anastomosis). In contrast, the switch-thread technique was associated with more technical error messages in total (14 vs. 12) and twice as many unintended thread ruptures per anastomosis (1.0 ± 0.88 vs. 0.5 ± 0.69). This study provides evidence for the enhanced performance of a steady-thread suturing technique, which is suggested to be applied upon robot-assisted microsurgical procedures for optimized efficiency.

Deepfake Audio Detection System

Abstract: To ensure the authenticity of audio material, it is important to establish reliable detection tools that can trace the spread and use of deepfake technologies. This study focuses on the methodology of deepfake audio identification via Mel Frequency Cepstral Coefficients (MFCC) as features with Random Forest as a classifier. By extracting MFCC features from audio clips and using them in a Random Forest model, one can learn unique spectral properties that can help distinguish deepfakes from authentic audio. The Random Forest algorithm, famous for its quality of being able to work well in an ensemble learning paradigm, is utilized to identify patterns that are representative of deepfake manipulation. To ensure the efficiency and reliability of this method, it was tested on a large number of different data sets that included both genuine and fake voice samples. To ensure robustness and generalization, cross-validation techniques are employed, restricting model predictions to the range of 0 to 1 and providing informative error messages for effective diagnosis. Thus, it is an important scientific study helping to develop and strengthen methods for identifying and eliminating threats in the area of artificial sound activity.

Engaging Students to Learn Coding in the AI Era with Emphasis on the Process

Engaging Students to Learn Coding in the AI Era with Emphasis on the Process

On the inadequacy of open-source application logs for digital forensics

This study explores the challenges with utilizing application logs for incident response or forensic analysis. Application logs have the potential to significantly enhance security analysis as sometimes they provide information regarding user actions, error messages, and performance metrics of the application. Although these logs can offer vital information about user activities, errors, and application performance, their use for security needs better understanding. We looked at the current logging implementation of 60 open-source applications. We checked the logs to see if they could help with five key security tasks: making timelines, linking events, separating different actions, spotting misuse, and detecting attacks. By examining source code, extracting log statements, and evaluating them for security relevance, we found many logs lacked essential elements. Specifically, 29 applications omitted timestamps, crucial for identifying the timing of actions. Furthermore, logs frequently missed unique identifiers (UIDs) for event correlation, with 23 not noting UIDs for new activities. Inconsistent logging of user activities and an absence of logs detailing successful attacks indicate current application logs need significant enhancements to be effective for security checks. The findings of our research suggest that current application logs are inadequately equipped for in-depth security analysis. Enhancements are imperative for their optimal utility. This investigation underscores the inherent challenges in leveraging logs for security and emphasizes the pressing need for refining logging methodologies.

Fake News Detection System Using Machine Learning and Deep Learning

The rise of fake news in today's age of mass media poses a serious threat to the credibility of the media and public discourse [1]. This project focuses on developing effective and robust solutions to detect fake news by integrating multiple machine learning algorithms. This project uses a variety of techniques, including natural language processing (NLP), clustering, classification algorithms and deep learning, to identify the content of patterns that indicate error messages. The project first explores NLP algorithms to extract content from news media content. These features allow the system to understand and classify the language used, helping to identify language patterns associated with fake news. Additionally, emotional analysis plays an important role in visualizing the tone and content of the text, further increasing the accuracy of the findings. The integration method is used to combine various machine learning models. By combining the performance of various algorithms, the system aims to overcome individual limitations and provide a more comprehensive and effective evaluation of media content. This approach helps increase the power of fake news detection and adapts to different fraud strategies. The project also includes deep learning algorithms, especially neural networks, to capture relationships in data. The use of deep learning ensures that the system can recognize nuances in the language and clarifies the complex information of the error message. Continuous updating and reworking of machine learning models is an important part of the process, allowing the system to constantly adapt to fraud strategies [2]. The aim of fake news detection system is to create a well-informed, weak search for fake news that can help create greater awareness and public approval. Key Words: Fake news detection, Machine learning techniques, Advanced algorithms, Classification algorithms, Deep learning, Feature extraction, Model training, Natural language processing

Validity constraints for data analysis workflows

Porting a scientific data analysis workflow (DAW) to a cluster infrastructure, a new software stack, or even only a new dataset with some notably different properties is often challenging. Despite the structured definition of the steps (tasks) and their interdependencies during a complex data analysis in the DAW specification, relevant assumptions may remain unspecified and implicit. Such hidden assumptions often lead to crashing tasks without a reasonable error message, poor performance in general, non-terminating executions, or silent wrong results of the DAW, to name only a few possible consequences. Searching for the causes of such errors and drawbacks in a distributed compute cluster managed by a complex infrastructure stack, where DAWs for large datasets typically are executed, can be tedious and time-consuming.We propose validity constraints (VCs) as a new concept for DAW languages to alleviate this situation. A VC is a constraint specifying logical conditions that must be fulfilled at certain times for DAW executions to be valid. When defined together with a DAW, VCs help to improve the portability, adaptability, and reusability of DAWs by making implicit assumptions explicit. Once specified, VCs can be controlled automatically by the DAW infrastructure, and violations can lead to meaningful error messages and graceful behavior (e.g., termination or invocation of repair mechanisms). We provide a broad list of possible VCs, classify them along multiple dimensions, and compare them to similar concepts one can find in related fields. We also provide a proof-of-concept implementation for the workflow system Nextflow.

Access to struct members and undefined behavior of C code

During software development, novice developers usually receive a lot of error messages and just warnings of various kinds. And if the code simply won't run when there are errors, then the program usually starts when there are various warnings. And here it is important to understand what consequences the presence of warnings of various kinds can lead to. This work aims to study the code's undefined behavior when working with struc-tures in the C programming language when issuing a corresponding compiler warn-ing about returning the address of a local or temporary variable. In the procedural programming language C, there is an ancestor of the OOP class — the structure struct, which encapsulates only the state of the entity. And the question arises — is it possible to work with separate components-fields of such a structure analogously to OOP languages? For research, a simple structure was taken, which contains information about the person's name, surname, and phone number. To access parts of the structure, pseudogetters were used — functions that returned a pointer to the corresponding part of the structure. The research was conducted in the Visual Studio 2019 environment under the control of the Windows 10 operating system when the default C language standard - MSVC and the more modern ISO standard C17 (2018) was selected in the project settings. As a result, a truly undefined behavior of the code was obtained, when the re-sult of the work of the code fragment (function call) depends on many factors: the length of the array, the standard of the C language, the position of a certain part in the structure. An attempt to conduct similar research under the control of the Linux Mint oper-ating system using the gcc compiler version 5.4 was unsuccessful. When compiling the code, a similar warning about returning the address of a local variable was also issued, as in the case of Visual Studio. But when the program was launched, it sim-ply crashed with a message about a segmentation error. Thus, both the Visual Studio 2019 compiler and the gcc compiler warned us about undefined code behavior. But this uncertain behavior was radically different for operating systems and compilers. If after gcc under the Linux OS, the code simply does not work at all and the program stops its work with a segmentation error mes-sage, then after Visual Studio under Windows, inexperienced developers with im-proper testing and verification of the code can miss the code that "does not always work", which can lead to unexpected results, not always pleasant, to say the least. And that's why software developers, especially beginners, should pay attention not only to compilation errors but also to warnings, even if the code works.

Smart Voting Web Based Application Using Aadhar

Abstract: Elections are a crucial part of any democratic system, and it is essential to ensure that the voting process is conducted in a fair and transparent manner. In traditional paper-based elections, the process is time-consuming, resource-intensive, and prone to errors. In this context, the use of technology can significantly improve the efficiency, security, and accuracy of the voting process. In this project, we propose an online voting system that uses face recognition technology to identify and authenticate voters. The proposed system allows voters to cast their votes remotely, eliminating the need for physical ballot boxes and reducing the cost and time involved in the voting process. The system works by capturing the facial image of the voter and passing it to the server unit for verification. The server compares the facial image with the information stored in the database and verifies the identity of the voter. If the identity is verified, the voter is allowed to cast the vote; otherwise, an error message is displayed on the screen, and the person is not allowed to poll the vote. The system is designed to be secure and tamper-proof. The use of face recognition technology makes it difficult for anyone to impersonate another person and cast a fraudulent vote. The system also ensures that each voter can cast only one vote, and the voting process is conducted in a transparent and fair manner. Overall, the proposed online voting system offers numerous advantages over traditional paper-based voting systems, including increased efficiency, reduced cost, improved security, and transparency.

Increasing the probability of transfer GMDSS broadcast messages when transmitted over HF channel in case fading

Improving the quality of communication in the Global Maritime Distress and Safety System of navigation is one of the components of the development of the Arctic zone of the Russian Federation in terms of information support. At the same time, the most difficult is to ensure communication in the A3 and A4 areas, where a communication HF channel plays a not small role. The transfer of messages over HF channel in the broadcasting mode of SITOR-B is considered. To improve probabilistic characteristics in the work, a statistical analysis of the decisions of the demodulator is carried out. It is shown that the probability density functions are “noisy”, which is associated with the non-zero probability of a bit error. Models of Rayleigh, Rice and Nakagami distributions were used for statistical analysis. It is shown that in the case of low values of the signal-to-noise ratio, which is characteristic of high latitude tracks, a Nakagami model is preferable. The symbol error rate and message error rate in a fading communication channel, as well as the probability of receiving an erroneous message are shown. The results presented in this work can significantly improve the probabilistic characteristics of the HF radiolines when transmitting messages in SITOR-B mode.

Would ChatGPT-facilitated programming mode impact college students’ programming behaviors, performances, and perceptions? An empirical study

ChatGPT, an AI-based chatbot with automatic code generation abilities, has shown its promise in improving the quality of programming education by providing learners with opportunities to better understand the principles of programming. However, limited empirical studies have explored the impact of ChatGPT on learners’ programming processes. This study employed a quasi-experimental design to explore the possible impact of ChatGPT-facilitated programming mode on college students’ programming behaviors, performances, and perceptions. 82 college students were randomly divided into two classes. One class employed ChatGPT-facilitated programming (CFP) practice and the other class utilized self-directed programming (SDP) mode. Mixed methods were utilized to collect multidimensional data. Data analysis uncovered some intriguing results. Firstly, students in the CFP mode had more frequent behaviors of debugging and receiving error messages, as well as pasting console messages on the website and reading feedback. At the same time, students in the CFP mode had more frequent behaviors of copying and pasting codes from ChatGPT and debugging, as well as pasting codes to ChatGPT and reading feedback from ChatGPT. Secondly, CFP practice would improve college students’ programming performance, while the results indicated that there was no statistically significant difference between the students in CFP mode and the SDP mode. Thirdly, student interviews revealed three highly concerned themes from students' user experience about ChatGPT: the services offered by ChatGPT, the stages of ChatGPT usage, and experience with ChatGPT. Finally, college students’ perceptions toward ChatGPT significantly changed after CFP practice, including its perceived usefulness, perceived ease of use, and intention to use. Based on these findings, the study proposes implications for future instructional design and the development of AI-powered tools like ChatGPT.