Pavlo M. Kurbet, Oleksandr Rudenok · Environmental Safety and Natural Resources · 2024
The article is devoted to increasing the efficiency of the functioning of wireless information transmission systems due to the adaptation of the verification polynomials of the component recursive systematic convolutional turbo code by solving the optimization problem using the gradient method. After the appearance of the extremely important work of K. Shannon, huge efforts have been made to find new transmission methods in order to approach the bandwidth of the channel. Channel coding is one of the main methods that enables such operation at almost full bandwidth. The probability of a white error of information decoding is chosen as the objective function. To calculate the probability of a bit error in information decoding, it is proposed to use cyclic codes. Adaptation schemes of these codes are used to improve the characteristics of information reliability. At the same time, during adaptation, in the vast majority of works, only one parameter changes – the coding speed, which does not fully increase the effectiveness of corrective coding schemes. The purpose of the article is to increase the efficiency of wireless information transmission systems by adapting the verification polynomials of the component recursive systematic convolutional turbo code by solving the optimization problem. The article consists of an introduction, which highlights the problem, analyzes the latest research and publications on this topic, and formulates the purpose of the article. The results of the research are shown, conclusions and prospects for further research are drawn. The article ends with a list of used sources. As a result of the proposed method, the effective number of verification polynomials of the RSCC turbo code, which were found using the method for the channel with additive white Gaussian noise for different sizes of the input data block, is given. We consider the direction of further research to expand the search range to take into account a larger number of parameters of turbo codes during adaptation, while the following can be foreseen: the number of bits in a block, types of interleavers, decoding algorithms, decoding iterations, etc.
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Iis Susiawati, Siti Nurul Azkiyah, Muhbib Abdul Wahab et al. · Langkawi Journal of The Association for Arabic and English · 2025
This study aims to identify the common patterns of code-switching and code-mixing employed by multilingual learners and their pedagogical implications. Using a systematic literature review method, 44 empirical studies published between 2019 and 2024 were selected from Scopus and Google Scholar databases. The findings reveal seven dominant forms of code-switching and code-mixing, including intra-sentential, inter-sentential, tag-switching, intra-word switching, situational, metaphorical, and code-mixing. These patterns are shaped by three primary factors: social context, learning environment, and cultural identity. Pedagogically, these linguistic strategies play a crucial role in enhancing comprehension, developing language skills, supporting cultural identity, fostering flexibility, and increasing engagement in multilingual classrooms. However, excessive use without guidance may lead to confusion and hinder language development. The study highlights the importance of understanding these practices for designing inclusive and effective language education strategies. It contributes to sociolinguistics and multilingual pedagogy by offering practical insights for teachers, curriculum developers, and education policymakers. Additionally, this review sheds light on emerging digital communication trends, such as the use of code-switching and code-mixing in online platforms and blended learning environments. It also provides actionable suggestions for integrating these strategies into teacher training and curriculum development. By addressing both linguistic and pedagogical aspects, the study underscores the relevance of language alternation in contemporary education.
Cited by 1Open access
Khaleghi Hozhabrasa, Javad · LUTPub (LUT University) · 2025
This thesis investigates how machine learning (ML) is used in low-code/no-code (LC/NC) platforms. These platforms allow end users to develop applications more easily. It presents a systematic literature review of recent studies to explore the main opportunities, advantages, benefits, challenges and obstacles of combining ML with LC/NC development. The results represents that ML can improve automation, decision-making, and user experience in LC/NC tools. However, there exists challenges related to model transparency, data quality, and system scalability. Moreover, this thesis discusses research gaps and possible future directions to improve ML integration into LC/NC platforms.
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Dimas Banu Dwi Hanggoro, Laela Kurniawati, Yan Rianto · Jurnal ilmiah Sistem Informasi dan Ilmu Komputer · 2025
The acceleration of digital transformation has increased the demand for fast and efficient software development. This condition has led to the emergence of Low-Code/No-Code (LC/NC) approaches, which allow users with limited technical expertise to build applications through visual interfaces and pre-built components. This study aims to analyze the quality of Low-Code No-Code (LCNC) application development based on the ISO/IEC 25010 standard through a Systematic Literature Review (SLR) method, supported by bibliometric visualization using VOSviewer software. The reviewed articles were published between 2020 and 2024. The visualization results indicate that keywords such as context, development, and system are the primary focus in the study of LCNC application quality. Furthermore, the findings of Meira et al. (2020) demonstrate that the Analytical Hierarchy Process (AHP) method, when combined with the ISO/IEC 25010 standard, is effective in evaluating and comparing software system quality within an industrial context. This study concludes that a standards-based evaluation approach, complemented by bibliometric visualization, can provide valuable insights for decision-making processes related to the development and implementation of LCNC applications.
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Hansini Gangani Kariyawasam · Open Science Framework · 2026
This systematic literature review aims to examine how Low-Code/No-Code (LCNC) platforms compare with traditional coding approaches across three outcome dimensions—development speed, efficiency, and accessibility. Despite the rapid growth of LCNC adoption globally, the existing literature lacks a comprehensive synthesis of comparative evidence across these three dimensions simultaneously. This review aims to address that gap by following PRISMA 2020 guidelines and the PICO framework—where the Population is developers and non-technical users, the Intervention is LCNC platforms, the Comparator is traditional coding, and the Outcomes are development speed, efficiency, and accessibility. The expected outcomes of this review include a complexity-bounded understanding of development speed advantages, a lifecycle trade-off model of efficiency, systematic evidence on LCNC accessibility for non-technical users, identification of key moderating variables—including project complexity, user technical background, and organizational governance—and a structured future research agenda identifying critical evidence gaps in this rapidly evolving field.
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Tomasz Lewowski, Lech Madeyski · Studies in systems, decision and control · 2021
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Siu Kit Yeung, Alison Lam · OSF Preprints (OSF Preprints) · 2025
This page is for the non-pre-registered correlation analysis regarding the number of years ago and the sample size, as suggested by the peer reviewer Prasad Chandrashekar. For other materials of this project led by Alison Lam (first author) and Siu Kit Yeung as co-author (second author), please see https://osf.io/2t67z/
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Robson Keemps Silva, Kleinner Farias, Rafael Kunst · Journal of Applied Research and Technology · 2023
Context: Nowadays, the prediction of source code design problems plays an essential role in the software development industry, identifying defective architectural modules in advance. For this reason, some studies explored this subject in the last decade. Researchers and practitioners often need to create an overview of such studies considering the predictors of design problems, their main contributions, the used prediction techniques and research methods. Problem: However, the current literature remains scarce of studies proposing a detailed mapping of studies already published. Objective: This article, therefore, focuses on classifying the current literature and pinpointing trends and challenges worth investigating in this research field. Method: A systematic mapping of the literature was designed and performed based on well-established practical guidelines. In total, 35 primary studies were selected, analyzed, and categorized after applying a careful filtering process from a corpus of 894 candidate studies to answer six research questions. Results: The main results are that a majority of the primary studies (1) explore Bloater bad smells, (2) use code complexity and size as predictors, (3) apply machine learning techniques to generate predictions, and (4) present a prediction proposal without an extensive empirical assessment. Conclusions: Predicting design problems is still in its infancy, showing that there is plenty of room for future work. Finally, this study can serve as a starting point for the research community
Cited by 1Open access
Cuk Tho, Harco Leslie Hendric Spits Warnars, Benfano Soewito et al. · 2020
Code-mixed language is ubiquitous. Having been commonly practiced among bilingual communities, code-mixed language has emerged as a common language among social media users. Despite its popularity, the analysis of a code-mixed text is challenging as the text does not typically comply with the monolingual grammar. Therefore, the popularity of social media in the past ten years has raised wide attention to develop methods for analyzing code-mixed text such as extracting popularity sentiment from the text. Machine learning-based classifier such as Support Vector Machine, Naïve Bayes, Decision Tree, Logistic Regression have been widely used to analyze the sentiment. This paper intends to further explore machine learning classifiers, their performances, variables, and most common classifiers for the code-mixed sentiment analysis. Prisma Methodology was used in this paper, extracting 12 from 230 papers that met predefined required criteria, including publication year within the last 5 years. Our findings suggested that the most common classifiers found in the papers were Support Vector Machine, Naïve Bayes, and Logistic Regression. By using the accuracy and F1 as the performance measures, the Support Vector Machine exhibited a better performance compared to Naïve Bayes and Logistic Regression. Thus, this study supported the use of Support Vector Machine, Naïve Bayes and Logistic Regression as the main classifiers for the code-mixed sentiment analysis.
Cited by 5
Yang Wu · Zenodo (CERN European Organization for Nuclear Research) · 2026
Scripts supporting the study: Resistance Management Strategies for Aedes albopictus Informed by Global Resistance Profiles and Cross-Resistance Network Analysis
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Venceslav Kafedziski, Natasa Paunkoska Dimoska · International Journal of Communication Networks and Distributed Systems · 2024
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Hillarie Man, Chow, Bonnie Wing-Yin, Taylor, Joanne · Open Science Framework · 2025
The objective of this systematic review is to examine the literature that has investigated the impact of code-switching on language processing, and to identify whether its influence differs across methodologies, and how participant- and item-level factors may affect this. Early research focused on code-switching production and the associated reaction-time costs in cued language-switching tasks. Although some work on code-switched reading and spoken language processing has identified similar costs, recent work has suggested that such costs are absent for common code-switches and among frequent code-switchers. This review aims to synthesise these findings to understand whether code-switching hinders or benefits reading and spoken language processing. It also investigates whether these costs or benefits may depend on the study design, the switches participants are exposed to, as well as the language or code-switching background of the participants. This review will be focused on non-clinical bilingual populations, with no age range of participants or date range of studies imposed. Three databases will be searched: Web of Science, SCOPUS, and PsychInfo. Studies included must be experimental where participants are either exposed to unilingual and code-switching stimuli, or to different types of code-switching stimuli, and must include a numeric outcome measure that assesses the impact of code-switching on participants’ language processing. Studies only investigating code-switching production or only investigating code-switching’s impact on language control will be excluded. This protocol is based on the JBI methodology for systematic reviews and the PRISMA-P checklist.
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Aloisio Cairo, Glauco de Figueiredo Carneiro, Miguel P. Monteiro · Information · 2018
Context: Code smells are associated to poor design and programming style, which often degrades code quality and hampers code comprehensibility and maintainability. Goal: identify published studies that provide evidence of the influence of code smells on the occurrence of software bugs. Method: We conducted a Systematic Literature Review (SLR) to reach the stated goal. Results: The SLR selected studies from July 2007 to September 2017, which analyzed the source code of open source software projects and several code smells. Based on evidence of 16 studies covered in this SLR, we conclude that 24 code smells are more influential in the occurrence of bugs relative to the remaining smells analyzed. In contrast, three studies reported that at least 6 code smells are less influential in such occurrences. Evidence from the selected studies also point out tools, techniques, and procedures that should be applied to analyze the influence of the smells. Conclusions: To the best of our knowledge, this is the first SLR to target this goal. This study provides an up-to-date and structured understanding of the influence of code smells on the occurrence of software bugs based on findings systematically collected from a list of relevant references in the latest decade.
Cited by 45Open access
Association for Computational Linguistics 2025, Cheng, Xueqi, Ding, Hanxing et al. · Underline Science Inc. · 2025
Tool learning has emerged as a crucial capability for large language models (LLMs) to solve complex real-world tasks through interaction with external tools. Existing approaches face significant challenges, including reliance on hand-crafted prompts, difficulty in multi-step planning, and lack of precise error diagnosis and reflection mechanisms. We propose $\texttt{ToolCoder}$, a novel framework that reformulates tool learning as a code generation task. Inspired by software engineering principles, $\texttt{ToolCoder}$ transforms natural language queries into structured Python function scaffold and systematically breaks down tasks with descriptive comments, enabling LLMs to leverage coding paradigms for complex reasoning and planning. It then generates and executes function implementations to obtain final responses. Additionally, $\texttt{ToolCoder}$ stores successfully executed functions in a repository to promote code reuse, while leveraging error traceback mechanisms for systematic debugging, optimizing both execution efficiency and robustness. Experiments demonstrate that $\texttt{ToolCoder}$ achieves superior performance in task completion accuracy and execution reliability compared to existing approaches, establishing the effectiveness of code-centric approaches in tool learning.
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Hanson, Jeffrey O, Schuster, Richard, Strimas-Mackey, Matthew et al. · Zenodo (CERN European Organization for Nuclear Research) · 2024
Plans for expanding protected area systems (prioritizations) need to fulfill conservation objectives. They also need to account for other factors, such as economic feasibility and anthropogenic land use requirements. Although prioritizations are often generated with decision support tools, most tools have limitations that hinder their use for decision-making. We outline the prioritizr R package (https://prioritizr.net) for systematic conservation prioritization. This decision support tool provides a flexible interface to build conservation planning problems. It can leverage a variety of commercial (e.g., Gurobi) and open source (e.g., CBC and SYMPHONY) exact algorithm solvers to identify optimal solutions in a short period. It is also compatible with a variety of spatially explicit (e.g., ESRI Shapefile, GeoTIFF) and nonspatial tabular (e.g., Microsoft Excel Spreadsheet) data formats. Additionally, it provides functionality for evaluating prioritizations, such as assessing the relative importance of different places selected by a prioritization. To showcase the prioritizr R package, we applied it to a case study based in Washington State (United States) for which we developed a prioritization to improve protected area coverage of native avifauna. We accounted for land acquisition costs, existing protected areas, places that might not be suitable for protected area establishment, and spatial fragmentation. The prioritization identified 12,400 km2 of priority areas for increasing the percentage of species’ distributions covered by protected areas. We also conducted a benchmark analysis to examine the performance of different solvers. Although open source and commercial solvers were able to quickly solve large-scale conservation planning problems, commercial solvers were required for complex, large-scale problems. The prioritizr R package is available on the Comprehensive R Archive Network (CRAN). In addition to reserve selection, it can inform habitat restoration, connectivity enhancement, and ecosystem service provisioning. It has been used in numerous conservation planning exercises to inform best practices and aid real world decision-making.
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Strässle, Miles · HSR (HSR University of Applied Sciences Rapperswil) · 2024
Abstract Introduction -------------- As software becomes more complex security issues in applications grow. This research focuses on the use of high-performance fuzzing techniques and also investigates performance parameters for fuzzing in different contexts. Fuzzing is a method of finding software vulnerabilities by injecting random data into programs to reveal and fix potential security flaws. The goal is to use an advanced fuzzing framework to identify vulnerabilities in real-world open-source C and C++ software, thereby improving its robustness and security. Approach -------------- To find a suitable project for fuzzing, the search targeted software that accepts input from users or external sources, focusing on areas most likely to contain vulnerabilities. A variety of open-source C and C++ projects with significant user interaction components were selected. A fuzzing harness was then created to test these critical areas of the software, utilizing various inputs and seed values. Employing white-box fuzzing, full access to the source code allowed for more informed tests, simplifying bug identification and avoiding reverse engineering as in black-box fuzzing. Once the tests were executed, the resulting bugs and hangs were analyzed to understand their causes and potential security implications. Metrics such as the number of bugs found, the types of vulnerabilities, and the duration of tests were collected to assess the effectiveness of the fuzzing process. Conclusion -------------- This project used advanced fuzzing techniques to test real-world C and C++ open-source projects. The fuzzing framework successfully reproduced many known security vulnerabilities, proving its effectiveness and reliability. Although no new vulnerabilities were found, the high performance and efficiency of the setup make it suitable for ongoing security testing. The metrics collected - such as the number and types of bugs found and test durations - highlighted areas for improvement. The results demonstrate the robustness of the code in the context of penetration testing and security audits, underscoring the importance of continuous security testing and how fuzzing can enhance software security.
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Keuning, Hieke, Jeuring, Johan, Heeren, Bastiaan · Utrecht University Repository (Utrecht University) · 2023
While functionality and correctness of code has traditionally been the main focus of computing educators, quality aspects of code are getting increasingly more attention. High-quality code contributes to the maintainability of software systems, and should therefore be a central aspect of computing education. We have conducted a systematic mapping study to give a broad overview of the research conducted in the field of code quality in an educational context. The study investigates paper characteristics, topics, research methods, and the targeted programming languages. We found 195 publications (1976-2022) on the topic in multiple databases, which we systematically coded to answer the research questions. This paper reports on the results and identifies developments, trends, and new opportunities for research in the field of code quality in computing education.
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Cai, Jingwen · Figshare · 2025
Data and Code for A Systematic Review of Global Youth Employment Policies
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Renu Verma, Kuldeep Kumar, Harsh Kumar Verma et al. · Journal of Software Evolution and Process · 2023
Abstract Code smells indicate poor design and imperfection in coding style. They increase maintenance costs and degrade software quality. Detecting and mitigating these code smells reduce the effects of flaws present in the software design. However, to save time and effort, it is a good practice to prioritize critical code smells and resolve them first. There have been various empirical studies on code smell prioritization in the last decades. Although the research community has been continuously working on the prioritization of code smells, yet a systematic mapping of all these studies would provide a panorama of research work. With this motivation, we selected 39 primary studies published between May 2011 and May 2022 and classified them based on seven factors: (i) code smells, (ii) factors, (iii) evaluation techniques, (iv) tools, (v) ranking formulas, (vi) data sets used, and (vii) measurements used for validating the research work. This paper aims to outline and discuss the prioritization procedure for code smells in object‐oriented software systems. The issues and recommendations for every question act as input for the researchers and industrialists for further research. Some issues for further investigation are less coverage of architecture smells, limited ranking formulas, lack of automated evaluation techniques, and so forth.
Cited by 13
Morteza Zakeri‐Nasrabadi, Saeed Parsa, Ehsan Esmaili et al. · ACM Computing Surveys · 2023
The accuracy reported for code smell-detecting tools varies depending on the dataset used to evaluate the tools. Our survey of 45 existing datasets reveals that the adequacy of a dataset for detecting smells highly depends on relevant properties such as the size, severity level, project types, number of each type of smell, number of smells, and the ratio of smelly to non-smelly samples in the dataset. Most existing datasets support God Class, Long Method, and Feature Envy, while six smells in Fowler and Beck's catalog are not supported by any datasets. We conclude that existing datasets suffer from imbalanced samples, lack of supporting severity level, and restriction to Java language.
Cited by 31Open access