2026· SINTEZA· pp. 165-170· 0 citations· 14 references
TL;DR
A structured review of the application of AI in frontend development, with a particular focus on analyzing existing benchmarking studies of widely used AI tools, suggests that these tools currently function most effectively as assistive technologies rather than fully autonomous solutions.
Abstract
: Artificial intelligence has become an increasingly influential component of modern software development, with a growing impact on frontend engineering practices. This paper presents a structured review of the application of AI in frontend development, with a particular focus on analyzing existing benchmarking studies of widely used AI tools. The study examines how these tools are applied in common frontend tasks, including component generation, styling, debugging, and design-to-code transformation. By synthesizing findings from recent research, the paper identifies key performance patterns across different AI systems, highlighting their strengths in improving development speed and productivity, as well as their limitations in terms of reliability, security, and handling of complex frontend logic. The analysis also reveals a lack of standardized evaluation frameworks tailored specifically to frontend development, as most existing studies rely on general-purpose metrics that do not fully capture user interface and user experience requirements. Based on these observations, the paper outlines several directions for future research, including the development of frontend-specific evaluation criteria, improvements in contextual understanding for complex tasks, and enhanced integration between design and development processes. The findings suggest that, while AI tools provide valuable support in frontend workflows, they currently function most effectively as assistive technologies rather than fully autonomous solutions. This review contributes to a clearer understanding of the current capabilities and limitations of AI in frontend development and highlights opportunities for further advancement in this rapidly evolving field.
An application-focused review of 35 selected empirical studies focusing on the use of AI during software testing, based on PRISMA guidelines, reveals that large language models, machine learning, and computer vision can significantly improve testing efficiency.
Guilherme Martins, Nelson N. Tenório, Jorge Bernardino· Big Data and Cognitive Compu...· 1 citation
It is concluded that AI meaningfully augments developer productivity but does not yet demonstrably improve satisfaction or earnings, and that a hybrid human-AI model, supported by governance and training, remains the most defensible direction for application development.
Perseus Bhavnagri· International Journal for Re...· 0 citations
Modern software systems are characterized by continuous integration, frequent releases, heterogeneous architectures, and increasingly complex interaction patterns. These conditions place substantial pressure on conventional test-case design, particularly where manually authored tests struggle to achieve adequate coverage within constrained development cycles. This research examines an AI-enabled approach to test-case generation and optimization for modern software development by synthesizing evidence from studies concerning augmented reality, simulation-based learning, computational visualization, embedded-system monitoring, and AI-driven software quality engineering. The proposed methodology conceptualizes test generation as a pipeline comprising requirement interpretation, test-objective identification, candidate test generation, execution-oriented prioritization, redundancy reduction, and continuous optimization. Particular emphasis is placed on the relationship between intelligent automation and software quality engineering, where AI-driven frameworks can transform testing from a predominantly scripted activity into an adaptive quality-assurance process (Ramamurthy, 2023). The analysis indicates that AI can provide substantial benefits in generating diverse test scenarios, prioritizing high-value cases, and adapting test suites to changing software conditions. However, optimization effectiveness depends on the quality of requirements, training or heuristic signals, system observability, and validation mechanisms. The research therefore positions AI-enabled testing not as a replacement for engineering judgment but as an augmentation mechanism that improves scalability, coverage, and prioritization while retaining human oversight for critical decisions.
D. Perera, Nethmi Fernando· International Journal of Nex...· 0 citations
In today's fast-changing software landscape, the need for effective software testing has grown increasingly vital for guaranteeing quality, reliability, and security in the software-driven world. With the increasing capabilities of Artificial Intelligence (AI), it has the promise of overcoming the known shortcomings of traditional testing methods that are still largely manual, rule-based, and reactive in their approach to quality assurance. The scope of research conducted on AI tools in testing has covered diverse areas such as machine learning, deep learning, natural language processing, and generative AI, showing the potential of these tools in different testing tasks, and identifying some ongoing challenges. In this paper, we'll discuss how traditional and AI-powered tools are used in four critical areas test management, test case management, defect management, and version management and our study results prove that AI-powered testing is better than traditional testing in each of these areas.
Rajat Sharma, Shahid Ali· VLSI & Embedded Systems...· 0 citations
: Generative AI has intensified interest in Automated Software Engineering, but most current evidence still concerns short, local coding tasks rather than the broader software engineering lifecycle. This paper presents a structured experience report on the development of a Python package for interpretable multiclass classification that hosts heterogeneous estimator families behind a shared interface. We analyze how AI assistance contributed to four recurring engineering activities—cross-language transfer, paper-to-code implementation, benchmark-driven algorithm refinement, and packaging/refactoring work—and how a small set of machine-checkable contracts acted as a cross-cutting enabler that made AI-assisted changes safer to accept. The contribution of the paper is threefold: a concrete research-software case from interpretable machine learning, a task-oriented account of where AI created practical leverage, and a set of lessons on planning, architecture, and validation for trustworthy use. The case suggests that generative AI is especially useful when requirements are only partially formalized, yet objective feedback from tests, benchmarks, and model quality metrics is available. At the same time, the study indicates that human oversight remains essential for semantic correctness, experimental validity, and maintainable software architecture. Overall, the results suggest that AI-augmented development is a relevant topic for scientific software engineering.
Robin Nunkesser· Proceedings of the 21st Inte...· 0 citations
: We are in a time of change in regards to the emergence of software development as we know it due to the growing number of developers using large language models (LLMs), which eventually will enable major shifts toward the "post-code" era in which software development will become less reliant on coding through using AI-driven development systems that accept natural language and high-level specifications as inputs. This research will analyze the impact of these AI assistants (e.g., GitHub Copilot, Gemini and GPT) through quantitative data collected from Stack Overflow Developer Surveys, GitHub Octoverse Reports, and JetBrains Developer Ecosystem Survey regarding how developers are currently embedding AI into their current practices and what it will look like moving forward. The research found out three things about how developers use Artificial Intelligence. These things are adoption of Artificial Intelligence satisfaction, with Artificial Intelligence the different ways developers are using Artificial Intelligence is changing. The results indicate that there is a distinct directional trend toward AI-native development environments, and that developers are in the midst of rapid change to adopt these tools.
P. Vijayakumar, Jegatheeswari Perumalsamy, Priya Ranjan Parida et al.· Proceedings of the 1st Inter...· 0 citations