Skip to content
Free planNo credit card requiredStart writing
AI for Research home
AI for Research home
Dashboard
Sign inGet started

Search 250 million research papers

Read the abstract, see who wrote it and who cites it, and get the citation in APA, MLA, Chicago or Harvard.

About 10,000 papers for “Systematic code”

Systematic Procedures for Operational Code Analysis: Measuring and Modeling Jimmy Carter's Operational Code

Stephen G. Walker, Mark Schafer, Michael D. Young · International Studies Quarterly · 1998

In this article we introduce a new scoring system for doing operational code analysis and test its reliability and validity by measuring and modeling President Jimmy Carter's operational code. Using speeches from the public record, we construct indices for the elements of the operational code construct. Based upon the valences and scaled intensities of verbs uttered in the speeches, President Jimmy Carter's views of the political universe and approaches to political action in different issue areas are identified and compared. The results of the analysis provide reasonable support for the face, construct, and content validity of the operational code indices. We find that Carter's view of the political universe and approach to political action were consistent across issue areas during the first three years of his term as president. Following the Soviet invasion of Afghanistan President Carter's support for human rights remained steadfast. Statistically significant shifts occurred in his views of the Soviet Union and others in the political universe and in his approach to political action regarding the conduct of U.S.-Soviet relations and other issues.

Cited by 180Open access

Refactoring-Aware Code Review: A Systematic Mapping Study

Flávia Coelho, Tiago Massoni, Everton L. G. Alves · 2019

Modern Code Review (MCR) demands enhancements in the way change logs are presented to reviewers. Their task benefits from higher-level descriptions about the intention behind commits; for instance, evolution tasks, such as refactorings, could be more effectively reviewed in the presence of the intended transformation - better if automatically detected by MCR tool support. This paper presents a systematic literature mapping (combining results from search strings, snowballing and a two-phase classification scheme) on refactoring-aware solutions to support MCR. We could observe that, since 2015, interest in tools and techniques for automatic detection of refactorings has been steadily growing. Most publications refer to new development methods or carry out characterisation studies. As a consequence of this overview, we point out a few potential research topics for the next years. In special, detection of multiple refactoring types in a mixed change log (in which refactorings are combined with other kinds of changes), or the need for case studies or experiments in applying refactoring detection in MCR, on distinct application domains and development environments.

Cited by 17

Android Source Code Smells: A Systematic Literature Review

Muhammad Fawad, Ghulam Rasool, Francis Palma · Software Practice and Experience · 2024

ABSTRACT Introduction Modern software developers strive to develop applications that are robust, easy to maintain, and possess high levels of quality. However, code smells can hinder this goal as they are visible signs of underlying issues. Numerous techniques and tools have been proposed for detecting code smells in various contexts and programming languages. Despite this, research on Android‐specific code smells and their impact on external quality attributes is still in its early stages. Objective This study aims to provide a comprehensive summary of state‐of‐the‐art techniques, tools, and approaches used for detecting and refactoring code smells in Android applications. Methodology A systematic literature review was conducted between November 2007 and December 2023, adhering to standard guidelines. In total, 79 primary studies were identified, analyzed, and synthesized. Results A total of 237 code smells were identified using 51 techniques and tools, based on seven distinct approaches. Efficiency was found to be the most affected external quality attribute. The code smell Durable Wakelock was the most studied. Challenges Despite the extensive research, the software engineering community faces numerous challenges. These include a lack of in‐depth investigation into Android‐specific code smells, a limited number of studied quality attributes, insufficient involvement of industry experts in the research process, scarcity of Android‐oriented metrics, and dependence on industry‐exclusive datasets. Future Directions This review suggests potential research directions that are valuable for researchers and practitioners seeking to gain insights into current open research problems in this domain.

Cited by 3Open access

Systematic Debugging of Logical Errors in Source Code

Felix Ziemann, Florian Reuß · 2024

In this paper we describe initial findings from the development of a web-based learning environment aimed at teaching a systematic approach to debugging, meaning it supports learners’ identification and correction of errors in source code. During the use of the tool, learners iteratively narrow down the error space and subsequently explicate their experience knowledge in their personal logbook. The instructional design of the tool follows the Cognitive Apprenticeship approach. In order to explore learners’ behaviour and their first experiences with the tool, the think aloud protocol was applied with six undergraduate computer science students. The findings show a positive response to the systematization of the debugging process and indicate specific design actions that could be taken into account in order to further improve the tool.

Cited by 1

Systematic Diagram Refinement for Code Generation in SEAM

Do Quang Tri, Tho Quan · 2012

Model-driven development (MDD) is methodology of software construction emerging recently to counter the drawbacks of classic approaches. Apart from the well-known UML, SEAM is another MDD approach increasingly attracting attention due to its capability of regarding the whole system as a single view, thus enabling consistency maintaining during the design development. Especially, the final diagram designed in SEAM can support automatic code generation, which has been the desirable goal of software engineering. However, SEAM suffers from the disadvantage of being too complicated when the system is designed in details at last steps. To tackle this problem, in this paper we propose an approach of systematic diagram refinement which allows users to specify transition matrices in each refinement steps, instead a whole new diagrams. Thus, the diagram developments will become less complicated and more natural. We also illustrate the usefulness of our approach in a featured case-study.

Cited by 2

Code Generation Using Machine Learning: A Systematic Review

Enrique Dehaerne, Bappaditya Dey, Sandip Halder et al. · IEEE Access · 2022

Recently, machine learning (ML) methods have been used to create powerful language models for a broad range of natural language processing tasks. An important subset of this field is that of generating code of programming languages for automatic software development. This review provides a broad and detailed overview of studies for code generation using ML. We selected 37 publications indexed in arXiv and IEEE Xplore databases that train ML models on programming language data to generate code. The three paradigms of code generation we identified in these studies are description-to-code, code-to-description, and code-to-code. The most popular applications that work in these paradigms were found to be code generation from natural language descriptions, documentation generation, and automatic program repair, respectively. The most frequently used ML models in these studies include recurrent neural networks, transformers, and convolutional neural networks. Other neural network architectures, as well as non-neural techniques, were also observed. In this review, we have summarized the applications, models, datasets, results, limitations, and future work of 37 publications. Additionally, we include discussions on topics general to the literature reviewed. This includes comparing different model types, comparing tokenizers, the volume and quality of data used, and methods for evaluating synthesized code. Furthermore, we provide three suggestions for future work for code generation using ML.

Cited by 79Open access

A systematic review of “Hour of Code” research

Jessica Yauney, Scott Bartholomew, Peter Rich · Computer Science Education · 2021

Background and Context Hour of Code is one of the largest educational campaigns implemented. It exposes millions of learners, to an hour of computer science fundamentals. With such a large impact, a large number of research articles have been published on the topic. This research ranges from reports of experiments testing the efficacy of Hour of Code and analysis of learner behavior, to reports of participation and suggestions for facilitating.Objective The goal of this work is to determine the effects of Hour of Code on Computer Science education.Method Articles in IEEE Xplore, ACM, EBSCO, ERIC, and Google Scholar that include “Hour of Code” were identified. Papers with “Hour of Code” as an emphasis of the research design or exploratory effort were analyzed.Findings Systematic review identified 64 papers which were sorted into six categories. Papers focused on overall participation numbers to novel Hour of Code Activities and research using Hour of Code data.Implications Analysis of these articles provides detail into the impact of Hour of Code on participants and influences resources for researchers. However, many questions remain to understand how Hour of Code can improve to meet its goals, expanding access to computer science to all students.

Cited by 16

hanapetersen/mte_climate_change_review: MTE systematic review data and code

Hana Petersen · Zenodo (CERN European Organization for Nuclear Research) · 2026

Supplementary data and Quarto markdown workflows for Petersen et al. "A systematic review of observational studies reporting climate change impacts on plant communities in Mediterranean-type ecosystems".

Cited by 0Open access

Smart card systematic mapping review code and datasets

Monteiro-Fialho, Leonardo, Cueto, Enrique · Zenodo (CERN European Organization for Nuclear Research) · 2025

The following are the Jupyter Notebook in charge of the analysis for the smart card systematic mapping review and the datasets. This systematic mapping review aims to comprehensively examine the current state-of-the-art in using smart cards for analytical studies applied to public transportation research. The review focuses on identifying and analyzing the main analytical purposes, algorithmic approaches, methods, datasets, and trends employed in these studies. The methodology followed the PRISMA statement for systematic reviews. This code is used for obtaining the results after extracting the data from the selected studies, also provided in the datasets folder. The software repository in GitHub: GeoTecINIT/smartcard-systematic-mapping-review (github.com)

Cited by 0Open access

hanapetersen/mte_systematic_review: MTE systematic review data and code

Hana Petersen · Zenodo (CERN European Organization for Nuclear Research) · 2025

Data and Quarto markdown workflows for Petersen et al. "Observed climate change impacts on plant communities in Mediterranean-type ecosystems". This version includes revisions to both the dataset and thematic analysis R workflow included in the original release.

Cited by 0Open access

Smart card systematic mapping review code and datasets

Monteiro-Fialho, Leonardo, Cueto, Enrique · Zenodo (CERN European Organization for Nuclear Research) · 2025

The following are the Jupyter Notebook in charge of the analysis for the smart card systematic mapping review and the datasets. This systematic mapping review aims to comprehensively examine the current state-of-the-art in using smart cards for analytical studies applied to public transportation research. The review focuses on identifying and analyzing the main analytical purposes, algorithmic approaches, methods, datasets, and trends employed in these studies. The methodology followed the PRISMA statement for systematic reviews. This code is used for obtaining the results after extracting the data from the selected studies, also provided in the datasets folder. The software repository in GitHub: GeoTecINIT/smartcard-systematic-mapping-review (github.com)

Cited by 0Open access

A Systematic Literature Review on Neural Code Translation

Xiang Chen, Junchen Xue, Xiaofei Xie et al. · ArXiv.org · 2025

Code translation aims to convert code from one programming language to another automatically. It is motivated by the need for multi-language software development and legacy system migration. In recent years, neural code translation has gained significant attention, driven by rapid advancements in deep learning and large language models. Researchers have proposed various techniques to improve neural code translation quality. However, to the best of our knowledge, no comprehensive systematic literature review has been conducted to summarize the key techniques and challenges in this field. To fill this research gap, we collected 57 primary studies covering the period 2020~2025 on neural code translation. These studies are analyzed from seven key perspectives: task characteristics, data preprocessing, code modeling, model construction, post-processing, evaluation subjects, and evaluation metrics. Our analysis reveals current research trends, identifies unresolved challenges, and shows potential directions for future work. These findings can provide valuable insights for both researchers and practitioners in the field of neural code translation.

Cited by 0Open access

hanapetersen/mte_climate_change_review: MTE systematic review data and code

Hana Petersen · Zenodo (CERN European Organization for Nuclear Research) · 2026

Supplementary data and Quarto markdown workflows for Petersen et al. "A systematic review of observational studies reporting climate change impacts on plant communities in Mediterranean-type ecosystems".

Cited by 0Open access

Static Code Analysis Tools: A Systematic Literature Review

Darko Stefanović, Danilo Nikolić, Dušanka Dakić et al. · Annals of DAAAM for ... & proceedings of the ... International DAAAM Symposium · 2020

Static code analysis tools are being increasingly used to improve code quality. Such tools can statically analyze the code to find bugs, security vulnerabilities, security spots, duplications, and code smell. The quality of the source code is a key factor in any software product and requires constant inspection and supervision. Static code analysis is a valid way to infer the behavior of a program without executing it. Many tools allow static analysis in different frameworks, for different programming languages, and for detecting different defects in the source code. Still, a small number of tools provide support for domain-specific languages. This paper aims to present a systematic literature review focusing on the most frequently used static code analysis tools and on classifying the presented tools according to the supported both generalpurpose and domain-specific programming languages and the types of defects a specific tool can detect.

Cited by 38Open access