SBC Computing Reviews

A Systematic Literature Review of Code Smell Detection Tools for JavaScript Systems

2026-06-13T11:44:50+00:00

JavaScript is one of the most popular programming languages. As projects grow, their code can become complex which leads to code smells, signs that can indicate problems. Many tools are available to detect and fix these issues, but we need a comprehensive summary of their most important features. This paper provides a systematic literature review of JavaScript code smell detection tools. We searched four databases (Scopus, ACM Digital Library, IEEE Xplore, and Springer) using a specific search string to find relevant studies. To refine the results, we applied a four-step selection process, reducing 1002 initial studies to 18 by removing duplicates, filtering metadata, and reviewing their full texts. We then used backward and forward snowballing to find more relevant studies, increasing the total number to 27 primary studies. Finally, we examined these studies to analyze the code smell detection tools they described. We identified 22 tools, many published in top software engineering venues, such as ICSE, MSR and TSE. We found that most tools use rule-based linting (55%), which is efficient but struggles with complex architectural smells. Dynamic analysis (23%) is underused and AI-driven detection is completely missing, despite its relevance in modern software engineering research. Researchers are also developing framework-specific tools for modern JavaScript practices and focusing on the detection of test smells (22%). Most tools available to practitioners detect only basic smells and ignore deeper design issues. Tool builders can address these gaps by combining static and dynamic analysis and creating more adaptable tools. For researchers, the lack of AI-driven detection and modern benchmark datasets presents an opportunity for progress.

A Systematic Review on Audio-Visual Speech-To-Speech Translation Models

2025-12-03T00:55:51+00:00

This systematic literature review summarizes the evolution of Audio-Visual Speech-to-Speech Translation (AV-S2ST), focusing on the shift from cascaded systems to direct end-to-end models. Key advancements utilize self-supervised learning (SSL) (e.g. AV-HuBERT) and discrete units (e.g. TransFace) to overcome data scarcity and enable textless multilingual translation. Current research tackles challenges in noise robustness, lip-synchrony, isometric translation, and speaker preservation. Techniques like cross-modal distillation show promise, driving progress towards robust, real-time AV-S2ST with enhanced zero-shot capabilities.

Software Requirements in the Context of Educational Metaverses: A Non-Functional Requirements-Centered Approach

2025-12-03T12:53:22+00:00

The customization of products and services has become an increasing demand in the 21st century; however, it remains underexplored in the educational context. The metaverse emerges as an alternative to enable mass customization in teaching and learning processes, although it requires a systematic analysis of software requirements, particularly non-functional requirements. This study presents the results of a systematic mapping of the literature aimed at identifying and analyzing requirements applied to the development of educational metaverses. Searches were conducted in the ACM Digital Library, IEEE Xplore, Scopus, and Engineering Village databases, resulting in 1,013 identified studies, of which 22 articles published between 2019 and 2024 were selected after the application of inclusion and exclusion criteria. The analysis was structured into four dimensions — Pedagogical, Technical, Social, and Ethical-Legal — defined based on guiding documents and the researchers’ practical experience. The results demonstrate that non-functional requirements play a fundamental role in software quality within immersive educational environments, although they are still insufficiently systematized in this context. Significant gaps were also identified in the social and ethical-legal dimensions, particularly regarding interactivity and security. Furthermore, the findings revealed the limited participation of teachers and students in the structuring of these environments, as well as the tendency of many solutions to merely virtualize existing processes, thereby underutilizing the interaction and customization potential of metaverse technologies. The proposed dimensions contributed to a systematic analysis of requirements, highlighting opportunities for advancing Requirements Engineering in immersive educational solutions and supporting the future development of a taxonomy for educational metaverses.

Constructing Knowledge Graphs from Text Using Large Language Models: Scoping Review

2026-02-19T20:18:53+00:00

Constructing Knowledge Graphs from unstructured textual sources poses significant challenges due to the inherent ambiguity of natural language and the high cost and limited scalability of manual knowledge modeling. Recently, Large Language Models (LLMs) have emerged as a promising alternative for automating knowledge extraction and structuring, resulting in a rapidly expanding body of research. This article aims to provide a scoping review of methods using LLMs to construct Knowledge Graphs from text, map existing approaches, identify methodological patterns, and analyze their strengths and limitations, thereby synthesizing the state of the art. The review employs a systematic protocol consistent with PRISMA guidelines and examines 126 primary studies. The literature is categorized into four methodological groups: Ontology-Based, Prompt-Based, RAG-Based, and Hybrid Pipelines. Each category is analyzed with respect to the role of LLMs within the construction pipeline, the degree of semantic formalization, and the architectural strategies implemented. Comparative analysis is performed using five evaluation criteria: exactness, scalability, adaptability, reproducibility, and ease of implementation. The findings demonstrate that no single approach comprehensively addresses all challenges inherent to LLM-based Knowledge Graph construction. Ontology-Based methods provide robust semantic guarantees but demand considerable manual effort. Prompt-Based approaches facilitate rapid deployment yet exhibit variability and limited reproducibility. RAG-Based methods enhance grounding through external evidence but introduce reliance on retrieval mechanisms. Hybrid Pipelines yield higher extraction accuracy, though at the expense of increased architectural complexity. The review identifies substantial heterogeneity in evaluation practices and a lack of standardized quantitative metrics. These results underscore the necessity for consistent evaluation frameworks to enhance comparability, reliability, and the advancement of research in LLM-assisted Knowledge Graph construction.

Women in Business Process Management: a Systematic Mapping

2026-05-04T18:27:55+00:00

In recent years, Business Process Management (BPM) has increasingly incorporated non-technical dimensions, recognizing cultural and social factors as critical to the success of BPM projects. Against this backdrop, this study investigates women’s participation in the BPM research domain, a field where their presence has traditionally been lower, especially in technology-related areas. Employing a systematic mapping methodology, the authors analyzed 5,029 scientific papers to assess patterns of authorship. The results demonstrate that women’s scientific production in BPM remains considerably below that of men, accounting for less than one-third of all identified authors. Moreover, this disparity exhibits a slight decreasing trend over time, revealing only modest progress in female participation. By providing empirical evidence of gender diversity trends in BPM academia, this research contributes valuable insights into women’s evolving role within the discipline and underscores the need for further efforts to foster inclusivity in this community.

From Digital Data to Electoral Forecasts: A Systematic Review and Taxonomy of Computational Approaches

2026-03-19T17:17:24+00:00

The increasing use of digital data in electoral prediction has motivated a growing body of computational research, yet the field remains methodologically diverse and lacks consolidated comparative frameworks. This article presents a systematic review of computational approaches for electoral outcome prediction using digital data between 2020 and 2025. Following rigorous systematic methodology, searches were conducted across three scientific databases, resulting in 80 primary studies analyzed after applying explicit quality criteria. The review proposes a taxonomy classifying studies by data integration and predictive complexity, enabling systematic identification of methodological patterns. Results reveal geographic concentration in few countries, with Twitter as the dominant platform and sentiment analysis as the most frequent technique. Vote percentage prediction and winner identification represent the primary objectives, evaluated mainly through regression and classification metrics. The field demonstrates numerical expansion with modest geographic diversification, yet persistent challenges remain regarding sample representativeness, cross-context generalization, and absence of standardized validation protocols. Findings indicate the need for broader geographic coverage, reduced platform dependency, and establishment of uniform evaluation criteria to advance methodological maturity in computational electoral prediction.