In fast-evolving software systems, effective 'natural language requirements parsing' and downstream change effect analysis capability across a multitude of codes represents low-hanging-fruit in this regard. We present a structured framework to deploy Large Language Models (LLMs) for automating two essential software engineering tasks, namely requirement interpretation and change impact analysis Utilizing the inherent understanding of semantics offered by transformer-based LLMs, the novel approach advances by converting vague and unstructured requirement documents into structured but machine-readable specifications to offer a direct traceability mapping from requirements to system components. Additionally, the framework leverages LLM-driven dependency analysis to predict and quantify how change effects percolate through connected modules which can minimize manual effort and human errors. This approach combines prompt engineering and retrieval-augmented generation (RAG) for domain-relevant accuracy plus fine-tuning techniques. On open-source and enterprise-grade software projects, experimental evaluations show that disambiguation accuracy, traceability precision, and change impact coverage of our approach are orders of magnitude better than state-of-the-art rule-based or static analysis tools. Notes: The results illustrate the application of LLMs at scale and demonstrate how these can alter software engineering workflows by removing bottlenecks (at a massive scale) at different stages of the software development lifecycle. In this research, we provide a generalizable pipeline that helps to bridge the gap from NLP advancements into practice for software lifecycle management.
Nithya Krishnan, Kumaran Ramanujam, Suresh Babu Narra et al.· 2026 International Conferenc...· 0 citations
Modern enterprise systems increasingly combine data-intensive Extract-Transform-Load (ETL) pipelines with microservice-based application architectures, creating a hybrid quality-assurance surface that neither classical database-testing methods nor conventional service-testing methods address in isolation. This paper reviews established approaches to database/ETL testing and microservice testing, identifies the challenges that arise when the two paradigms are combined within a single delivery pipeline, and proposes a unified continuous-testing framework that integrates data-quality gates with service-level contract and resilience gates inside one CI/CD workflow. The framework covers extraction, staging, transformation and loading validation for ETL pipelines; unit, component, contract, integration and end-to-end testing for microservices; and cross-cutting concerns such as test-data management and service virtualization. A prototype pipeline was evaluated on a retail data-integration case study comprising a five-stage ETL pipeline and six containerized microservices. Results show that the unified pipeline reduced the defect-escape rate to production from 12.8% to 7.6% and shortened the mean defect-detection time from 3.2 days to 6.4 hours relative to a baseline pipeline that tested the two layers independently. The paper concludes with practical recommendations for teams adopting combined data-and-service testing pipelines and outlines directions for future automation using model- and AI-assisted test generation.
Urvish Gajjar· International Journal of Sci...· 0 citations
An integrated reference architecture is proposed that combines TDD's fine-grained unit-level feedback loop with BDD's stakeholder-readable acceptance criteria inside a single continuous integration and continuous delivery (CI/CD) pipeline.
Urvish Gajjar· International Journal of Sci...· 0 citations
A comprehensive framework for integrating Load & Performance Testing and Security Testing into the software development lifecycle of enterprise-grade applications is presented, addressing scalability bottlenecks, throughput degradation under concurrent load, and security vulnerabilities that emerge specifically under high-traffic conditions.
Urvish Gajjar, H. Shah· International Journal Of Eng...· 0 citations