Hui LI, Ming YE, Qiang TONG et al. · SHILAP Revista de lepidopterología · 2019
Polar code in channel coding schemes is a research hotspot in 5G communication field.Polar code is susceptible to error propagation under successive cancellation decoding,and the performance of the code at short and medium block lengths is not ideal.For the problems,the performance difference between systematic polar code and non-systematic polar code was studied under various simulation conditions.Simulation results demonstrate that systematic polar code has advantage over non-systematic polar code in bit error rate performance,and the former are more robust against error propagation than the latter under successive cancellation decoding.The above results are considerable importance from the theoretical values in 5G communication and performance optimization of polar code.
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Xool-Tamayo, Jorge, Moreno Ulloa, Aldo · Zenodo (CERN European Organization for Nuclear Research) · 2023
No description provided.
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Mens, Kim, Nijssen, Siegfried, Pham, Hoang Son et al. · Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)) · 2020
Software developers repeatedly perform similar but non-identical changes to a systems source code. Such groups of similar systematic code changes are performed for various reasons: adapting code to a changed API, migrating to a different library, refactoring to improve code quality, performing routine code maintenance tasks, fixing multiple manifestations of the same bug, implementing or modifying crosscutting concerns, managing code clones, making similar changes to multiple branches of a system, etc. While developers may have some notion of the systematic changes they performed in their own code, it requires significant effort to manually locate existing systematic code changes in projects they do not know. Consequently, an automated approach for locating such systematic code changes between versions is required.
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Abdullah Ali H. Alzahrani · International Journal of Advanced Computer Science and Applications · 2020
Code generation is longstanding goal in software engineering. It allows more productivity of computer programming as it aims to provide automation of transformation of models into actual source code. This process has been covered adequately in many programming languages. However, this topic has not been covered sufficiently with regards to Fourth Generation Languages (4GL) which have a high specialized nature. The goal of this paper is to represent a systematic literature review of 4GL Code generation. The paper focuses on reviewing systemically the studies published in the past 20 years on the topic. This is to investigate the trends in the topic and the approaches introduced in order to identify potential new research lines.
Cited by 3Open access
Tianyi Zhang, Myoungkyu Song, Joseph Pinedo et al. · 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering · 2015
Developers often inspect a diff patch during peer code reviews. Diff patches show low-level program differences per file without summarizing systematic changes -- similar, related changes to multiple contexts. We present Critics, an interactive approach for inspecting systematic changes. When a developer specifies code change within a diff patch, Critics allows developers to customize the change template by iteratively generalizing change content and context. By matching a generalized template against the codebase, it summarizes similar changes and detects potential mistakes. We evaluated Critics using two methods. First, we conducted a user study at Salesforce.com, where professional engineers used Critics to investigate diff patches authored by their own team. After using Critics, all six participants indicated that they would like Critics to be integrated into their current code review environment. This also attests to the fact that Critics scales to an industry-scale project and can be easily adopted by professional engineers. Second, we conducted a user study where twelve participants reviewed diff patches using Critics and Eclipse diff. The results show that human subjects using Critics answer questions about systematic changes 47.3% more correctly with 31.9% saving in time during code review tasks, in comparison to the baseline use of Eclipse diff. These results show that Critics should improve developer productivity in inspecting systematic changes during peer code reviews.
Cited by 49
Tianbo Xue, Francis C. M. Lau · 2017
For channels that introduce substitution, insertion and deletion errors, one challenging problem for a code designer is to avoid false code synchronization. In other words, the probability of false codewords occurring should be minimized with appropriate code design. In this paper, we propose a new class of systematic comma free code called generalized F(n, s, t) code. We first prove that this code is a synchronous code and then we derive the probabilities of false synchronization when a substitution, insertion or deletion error occur. We compare the theoretical and simulation results under different parameters. We also compare the performance of our proposed code with the classical F code.
Cited by 1
Calvin Bulla · RECERCAT (Consorci de Serveis Universitaris de Catalunya) · 2017
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Priyanshu Vishwakarma · International Journal for Research in Applied Science and Engineering Technology · 2026
By enabling natural-language-to-code translation, recent advances in Large Language Models (LLMs) like Gemini, GPT-4, and Codex have revolutionized human-computer interaction. Despite these advancements, the quality, maintainability, and dependability of generated code are still inconsistent, primarily due to the way prompts are phrased. Prompt sensitivity becomes a major constraint in Low-Code/No-Code (LC/NC) systems, where automation and accessibility are given top priority. The literature on prompt engineering as the primary method for coordinating user intent with executable logic in AI-driven code development is compiled in this review study. It examines current frameworks for automated refining, contextual enrichment, iterative feedback, and structured prompting [1]–[5]. The study also looks at how these techniques might be integrated into LC/NC ecosystems to produce development workflows that are flexible and suitable for production. We find that systematic rapid engineering, which bridges the semantic gap between executable syntax and natural language, is the primary enabler for deterministic, high-fidelity AI code creation through comparative analysis of works published between 2023 and 2025.
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Vahid Garousi, Yasaman Amannejad, Aysu Betin-Can · Information and Software Technology · 2014
Cited by 74Open access
Erdal Özdoğan, Onur Ceran, Mutlu Tahsin Üstündağ · Gazi University Journal of Science Part A Engineering and Innovation · 2023
“Infrastructure as Code” technologies are the network automation concept used in configuring network devices, allocating network resources, and deploying developed applications. By using machine-readable codes, various tasks that previously required time and effort can now be done dynamically with infrastructure as code tools. Although Infrastructure as Code is a technology that brings many advantages and is still at the beginning of its popularity, there are not enough resource in the literature. In this study, the key concepts of Infrastructure as Code technologies are discussed and infrastructure as code tools are systematically examined. The six most used Infrastructure as Code tools were examined in terms of management, language, data representation, code approach, stateful and stateless, architectural perspectives. Also, they were compared over these key concepts. The main purpose of this article is to define, classify, and elucidate the emerging infrastructure as code tools.
Cited by 7Open access
Tando Maduna · Zenodo (CERN European Organization for Nuclear Research) · 2026
This is dataset includes the R code used to identify, collect and screen litereature from PubMed through a literatue searched performed between September 2024 and December 2025. The code was developed from published systematic review workflows from Turi et al., 2021 and Giordano et al., 2023.
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Porfirio, Andres Jessé, Pereira, Roberto, Eleandro Maschio · 2021
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Yuxiang Zhu, Minxue Pan · arXiv (Cornell University) · 2019
Background: During software maintenance and development, the comprehension of program code is key to success. High-quality comments can help us better understand programs, but they're often missing or outmoded in today's programs. Automatic code summarization is proposed to solve these problems. During the last decade, huge progress has been made in this field, but there is a lack of an up-to-date survey. Aims: We studied publications concerning code summarization in the field of program comprehension to investigate state-of-the-art approaches. By reading and analyzing relevant articles, we aim at obtaining a comprehensive understanding of the current status of automatic code summarization. Method: In this paper, we performed a systematic literature review over the automatic source code summarization field. Furthermore, we synthesized the obtained data and investigated different approaches. Results: We successfully collected and analyzed 41 selected studies from the different research communities. We exhaustively investigated and described the data extraction techniques, description generation methods, evaluation methods and relevant artifacts of those works. Conclusions: Our systematic review provides an overview of the state of the art, and we also discuss further research directions. By fully elaborating current approaches in the field, our work sheds light on future research directions of program comprehension and comment generation.
Cited by 23Open access
Tando Maduna · Zenodo (CERN European Organization for Nuclear Research) · 2026
This is dataset includes the R code used to identify, collect and screen litereature from PubMed through a literatue searched performed between September 2024 and December 2025. The code was developed from published systematic review workflows from Turi et al., 2021 and Giordano et al., 2023.
Cited by 0Open access
Qurat Ul Ain, Wasi Haider Butt, Muhammad Waseem Anwar et al. · IEEE Access · 2019
Code cloning refers to the duplication of source code. It is the most common way of reusing source code in software development. If a bug is identified in one segment of code, all the similar segments need to be checked for the same bug. Consequently, this cloning process may lead to bug propagation that significantly affects the maintenance cost. By considering this problem, code clone detection (CCD) appears as an active area of research. Consequently, there is a strong need to investigate the latest techniques, trends, and tools in the domain of CCD. Therefore, in this paper, we comprehensively inspect the latest tools and techniques utilized for the detection of code clones. Particularly, a systematic literature review (SLR) is performed to select and investigate 54 studies pertaining to CCD. Consequently, six categories are defined to incorporate the selected studies as per relevance, i.e., textual approaches (12), lexical approaches (8), tree-based approaches (3), metric-based approaches (7), semantic approaches (7), and hybrid approaches (17). We identified and analyzed 26 CCD tools, i.e., 13 existing and 13 proposed/developed. Moreover, 62 open-source subject systems whose source code is utilized for the CCD are presented. It is concluded that there exist several studies to detect type1, type2, type3, and type4 clones individually. However, there is a need to develop novel approaches with complete tool support in order to detect all four types of clones collectively. Furthermore, it is also required to introduce more approaches to simplify the development of a program dependency graph (PDG) while dealing with the detection of the type4 clones.
Cited by 158Open access
Gunawardena, Sanuri · IEEE DataPort · 2019
Code review is a widely used quality assurance technique. From the heavy-weight and manual process that it was first introduced as code review has evolved into a more light-weight and tool-assisted process overtime to support modern software development that demands frequent delivery of source code. A large body of research on code review support exists. To help to understand the state of nature, quality, and evaluation of existing code review support, we conducted a systematic mapping study, a form of systematic literature review (SLR). We examined 5 major academic databases for evidence on existing support. We found 334 relevant articles on code review support. The results revealed that even though performing code review using existing forms of support is capable of fulfilling the expectations of reviewers, they possess many challenges and inadequacies revealing improvement opportunities. We report those challenges and inadequacies. Additionally, based on the nature of support and their evaluation, we discuss 6 major observations and future research directions.
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Suihua Cai, Wenchao Lin, Xinyuanmeng Yao et al. · IEEE Transactions on Information Theory · 2021
In this paper, we propose a systematic low density generator matrix (LDGM) code ensemble, which is defined by the Bernoulli process. We prove that, under maximum likelihood (ML) decoding, the proposed ensemble can achieve the capacity of binary-input output symmetric (BIOS) memoryless channels in terms of bit error rate (BER). The proof technique reveals a new mechanism, different from lowering down frame error rate (FER), that the BER can be lowered down by assigning light codeword vectors to light information vectors. The finite length performance is analyzed by deriving an upper bound and a lower bound, both of which are shown to be tight in the high signal-to-noise ratio (SNR) region. To improve the waterfall performance, we construct the systematic convolutional LDGM (SysConv-LDGM) codes by a random splitting process. The SysConv-LDGM codes are easily configurable in the sense that any rational code rate can be realized without complex optimization. As a universal construction, the main advantage of the SysConv-LDGM codes is their near-capacity performance in the waterfall region and predictable performance in the error-floor region that can be lowered down to any target as required by increasing the density of the uncoupled LDGM codes. Numerical results are also provided to verify our analysis.
Cited by 15
Ingo Stuermer, Mirko Conrad, Heiko Doerr et al. · IEEE Transactions on Software Engineering · 2007
Unlike for conventional compilers for imperative programming languages such as C or ADA, no established methods for safeguarding artifacts generated by model-based code generators exist despite progress in the field of formal verification. Several test approaches dominate the engineering practice. This paper describes a general and tool-independent test architecture for code generators used in model-based development. We evaluate the effectiveness of our test approach by means of testing optimizations performed by the TargetLink code generator, a widely accepted and complex development tool used in automotive model-based development.
Cited by 58
Shoumik Saha, Jianjun Chen, Sam Mayers et al. · ArXiv.org · 2025
Code-capable large language model (LLM) agents are increasingly embedded into software engineering workflows where they can read, write, and execute code, raising the stakes of safety-bypass ("jailbreak") attacks beyond text-only settings. Prior evaluations emphasize refusal or harmful-text detection, leaving open whether agents actually compile and run malicious programs. We present JAWS-BENCH (Jailbreaks Across WorkSpaces), a benchmark spanning three escalating workspace regimes that mirror attacker capability: empty (JAWS-0), single-file (JAWS-1), and multi-file (JAWS-M). We pair this with a hierarchical, executable-aware Judge Framework that tests (i) compliance, (ii) attack success, (iii) syntactic correctness, and (iv) runtime executability, moving beyond refusal to measure deployable harm. Using seven LLMs from five families as backends, we find that under prompt-only conditions in JAWS-0, code agents accept 61% of attacks on average; 58% are harmful, 52% parse, and 27% run end-to-end. Moving to single-file regime in JAWS-1 drives compliance to ~ 100% for capable models and yields a mean ASR (Attack Success Rate) ~ 71%; the multi-file regime (JAWS-M) raises mean ASR to ~ 75%, with 32% instantly deployable attack code. Across models, wrapping an LLM in an agent substantially increases vulnerability -- ASR raises by 1.6x -- because initial refusals are frequently overturned during later planning/tool-use steps. Category-level analyses identify which attack classes are most vulnerable and most readily deployable, while others exhibit large execution gaps. These findings motivate execution-aware defenses, code-contextual safety filters, and mechanisms that preserve refusal decisions throughout the agent's multi-step reasoning and tool use.
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Stephen G. Walker, Mark E. Schafer, Michael D. Young · The SHAFR Guide Online · 2017
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