Jul 2026· Infotek : Jurnal Informatika dan Teknologi· Vol 9, pp. 387-398· 0 citations
TL;DR
The transition to automation testing using Cypress has proven highly effective in accelerating the quality assurance (QA) cycle and maintaining continuous software functionality stability on the Parisada application.
Abstract
Manual testing on interactive applications such as web e-conferences often faces constraints in the form of human error and long execution durations, especially in repetitive regression processes. This research aims to implement automated end-to-end (E2E) testing using the Cypress framework and evaluate the level of testing time efficiency on the Parisada platform. The research methodology includes designing core functionality scenarios covering the authentication process, system navigation, meeting room creation, and communication session termination. Testing was conducted through three trials (P1, P2, P3) to simulate various inhibiting variables, such as unstable network latency and user familiarity levels with the application interface. The results showed that all automated testing scenarios successfully achieved a 100% pass rate. In terms of efficiency, the use of Cypress significantly reduced the execution duration from an average of 60.0 seconds in manual testing to only 23.67 seconds in automated testing. Data analysis indicates an average time efficiency increase of 60.55%. Automated testing also proved to be more consistent and reliable in dealing with technical fluctuations compared to manual testing. In conclusion, the transition to automation testing using Cypress has proven highly effective in accelerating the quality assurance (QA) cycle and maintaining continuous software functionality stability on the Parisada application.
The experimental results show that the Cypress-based end-to-end test suite has short and stable execution times, and resilient data-cy attributes significantly reduce maintenance overhead when UI changes occur.
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The evaluated controller functions of the E-OBE application exhibit simple structural complexity, however, the automated testing revealed significant input-handling weaknesses and schema inconsistencies, ensuring better reliability for future front-end integration.
Abdul Azis Naufal Farizqi, Made Hanindia Prami Swari, I. G. S. Mas Diyasa· bit-Tech· 0 citations
This paper presents the development of a software verification and validation tool focused on the Prolog logic programming language, specifically designed for educational contexts. The project arose from the lack of a functional testing framework in the course "Logic for Computer Science" at the Universidad Nacional del Sur, which affects the quality and efficiency of grading students' work. The tool facilitates the automated creation and execution of test cases, allowing teachers to save time in validating students' programs. It features a user-friendly graphical interface and a console mode, following the Model-View-Presenter architecture to ensure clear separation of responsibilities and ease of maintenance. Additionally, it allows for the storage and comparison of the obtained results with the expected ones in structured formats such as JSON. The implementation has shown key benefits, significantly reducing evaluation time and providing detailed feedback to students. The developed tool is distributed freely.
Nicolas M. Leidi, Andrea Cohen, Luciano H. Tamargo et al.· Revista Iberoamericana de Te...· 0 citations
As mobile applications grow in complexity, traditional End-to-End (E2E) testing frameworks struggle with UI volatility, maintenance overhead, and cross-platform scalability. This paper presents DragonCrawl, an AI-driven mobile testing system for continuous regression testing that has evolved from embedding-based similarity matching to generative intent-based reasoning using large language models. Unlike prior LLM-based testing research focused on exploratory testing and crash detection, DragonCrawl validates specific user flows on every code change, blocking commits that break critical functionality. By leveraging GPT-4o's multimodal capabilities, DragonCrawl achieves 91.6% pass rate on iOS and 92.2% on Android across 1,013 automated tests running continuously in CI/CD pipelines. The system reduces test onboarding time from 96-120 hours to under 4 hours and has saved an estimated 27 developer years in test maintenance effort. We present the architectural evolution from V1 (semantic embedding matching) to V2 (generative intent-based reasoning), discuss implementation challenges including token explosion and memory constraints, and report operational experience from production deployment. The integration of multimodal vision for end-state detection and tool calling for backend state transitions enables comprehensive regression testing that bridges UI interactions with system state. Our results demonstrate that AI-driven testing can maintain stability while eliminating the brittleness of traditional automated tests, enabling continuous quality assurance at scale.
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Enterprise regression testing for microservice systems is often constrained by incomplete or outdated documentation. In practice, QA engineers frequently rely on real execution traffic to reconstruct business scenarios; however, turning raw traffic into replayable regression tests with stable validation logic remains labor-intensive and error-prone. This paper presents NL2Test, an end-to-end approach and tool that generates executable API regression tests from (i) a natural-language scenario description and (ii) a traffic capture recorded while executing the scenario. NL2Test addresses two coupled tasks: test case carving, which extracts a minimal replayable request sequence and reconstructs data dependencies so that dynamic values are bound from their responses rather than hard-coded; and assertion generation, which produces assertions aligned with business intent while avoiding non-deterministic fields and hallucinated paths. To improve reliability, NL2Test uses LLMs for semantic interpretation and constrained code synthesis, and uses deterministic algorithms for request filtering, dependency confirmation via value consistency, and assertion-path validation. We evaluate NL2Test on 51 industrial regression scenarios extracted from a large consumer-facing Internet company. NL2Test achieves an exact-match rate of 82.4% (42/51), and produces a functionally usable draft in 98.0% (50/51) of scenarios when allowing minor post-edits. In a 9-month production deployment starting in March 2025, NL2Test generated 3,196 test cases with an overall code adoption rate of 85.4%. These results indicate that traffic-grounded generation with deterministic guardrails can substantially reduce manual effort while improving regression automation in complex microservice environments.
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Function calling enables Large Language Models (LLMs) to orchestrate backend services by invoking structured tools, extending their role from text generators to intelligent agents. While prior work evaluates tool-use accuracy, there is limited empirical evidence on the backend-level performance trade-offs between LLM-driven orchestration and traditional deterministic implementations in enterprise systems. This study presents a controlled, reproducible benchmark comparing Javabased deterministic backend logic with LLM function-calling pipelines using locally deployed open-weight models (Mistral 7B, Llama 3.1 8B, and Qwen 2.5 7B). Across three representative enterprise scenarios, data retrieval, data normalization, and command execution, we measure accuracy, latency, error rate, token efficiency, and development effort. Results show that deterministic implementations achieve near-instantaneous execution (0.2-1.8 ms) with 100% reliability, while LLM-driven approaches incur a $\mathbf{1 0 0} \times \mathbf{- 1 6 0 0} \times$ latency overhead (200-800 ms per request). Because of this significant delay overhead, LLM function calling is inappropriate for high-throughput and latencycritical backend services. However, selected 8B-class models (Llama 3.1 and Qwen 2.5) maintain 100% schema adherence under zero-temperature settings and reduce codebase size by approximately 33%, improving development agility. These findings demonstrate that LLM function calling is not a replacement for deterministic backend logic in latency-critical systems, but rather an effective orchestration layer for dynamic and natural language-driven workflows. We conclude by proposing a hybrid backend architecture that combines compiled logic for highperformance transactional paths with LLM-based reasoning for flexible interaction layers.
M. A. Ala’anzy, Adil Akhmetov, Amani Ibraheem et al.· 2026 6th International Confe...· 0 citations