Jul 2026· 2026 6th International Conference on Inventive Computation and Information Technologies (ICICIT)· pp. 1589-1596· 0 citations· 21 references
Abstract
SaaSGenAI is an AI-driven framework for generating and deploying Software-as-a-Service (SaaS) applications from natural language requirements. Developing SaaS applications traditionally requires significant programming expertise, manual configuration, and considerable development time, making it challenging for startups, entrepreneurs, and nontechnical users to transform ideas into functional software products. Recent advances in artificial intelligence have enabled greater automation in software development; however, many existing solutions focus primarily on individual tasks such as code generation, interface design, or prototyping. SaaSGenAI addresses this limitation by integrating requirement analysis, intelligent blueprint generation, automated code generation, database configuration, authentication setup, and cloud deployment into a single workflow. The framework converts user requirements into structured blueprints that define application features, database schemas, authentication requirements, and user interface components. These blueprints are then used to automatically generate and deploy full-stack web applications with minimal human intervention. By combining artificial intelligence with low-code/no-code development principles, SaaSGenAI reduces development effort, accelerates application delivery, and simplifies the overall software development process. The framework can be effectively applied to rapid prototyping, minimum viable product (MVP) development, internal business applications, content management systems, and other SaaS-based solutions, demonstrating the practical potential of AI-assisted software engineering.
The paradigm of software self-evolution enables systems to autonomously extend and reconfigure their own capabilities during execution in response to technical specifications. Yet requests for new functionality often originate from end users and are rarely expressed in technical terms. As a result, developers must tran...
DevOps programming (e.g., using CLI/API scripts or IaC frameworks) is key to cloud infrastructure management. Unlike traditional programming tasks, DevOps program testing needs provisioning and execution against actual cloud resources, which is often time-consuming, unsafe, and costly. Cloud emulators have gained popul...
Archit Bhatnagar, Zhenning Yang, Sarah McClure et al.· 0 citations
This study investigates whether a frontier LLM can generate Dockerfiles and Docker Compose configurations for multi-service applications using repository contents without access to developer-authored deployment artifacts and analytically derives a minimal explicit deployment specification for information that cannot be...
Oleg Grynets, Kyrylo Fursov, V. Lyashkevych et al.· arXiv.org· 1 citation
This paper presents a layered architecture for AI-integrated development systems, encompassing prompt formulation, semantic interpretation, code generation, and validation mechanisms, and explores the dynamics of human–AI interaction, highlighting the importance of trust calibration, control boundaries, and iterative r...
Yasin Arik· International Journal of Res...· 0 citations
This paper describes a framework that brings together three capabilities: automatic generation of new test cases from API specifications and usage data, self-healing of broken test scripts through similarity-based and learned matching, and risk-based prioritisation of test execution using a reinforcement learning agent...
Ihor Diakonov· Journal of Artificial Intell...· 0 citations
A Domain-Specific Language (DSL), named RI language, designed for the declarative description of microservices, along with a supporting tool, TSE (Toolbox Service Executor), implemented in Python and based on the RI grammar, enables the representation of structural and operational aspects of services in a technology-ag...
Cássio Ritse, Machado Dos, S. Silva et al.· 0 citations
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