PreventativeTestPro: A Scalable Hybrid Testing Framework Utilizing Observability and Generative AI for Proactive Software Quality Engineering

Summary

PreventativeTestPro is an AI-driven testing framework that utilizes observability data and large language models to automate root cause analysis, test generation, and continuous validation, with the objective of improving software reliability and optimizing quality assurance for both frontend and backend systems to facilitate more efficient support ticket management.

Abstract

This paper introduces a sophisticated, scalable testing system that integrates observability-driven automation with AI-augmented proactive quality engineering to tackle contemporary software delivery difficulties. The suggested system enhances PreventativeTestPro, an open-source, hybrid testing platform that combines black-box and white-box methodologies, by incorporating an innovative observability-based test orchestration layer. The platform utilizes logs, metrics, events, and traces alongside browser and server-side monitoring to promptly identify anomalies, enhance test case selection, and automate the creation of functional, performance, and security test suites. A distinctive characteristic is the incorporation of large language models (LLMs) to provide root cause insights and autonomously construct new test cases based on production behaviors and identified abnormalities, thus providing adaptive regression coverage and intelligent remediation.

The system facilitates concurrent test execution with instantaneous AI-driven log analysis, fostering a continuous feedback loop between operations and testing. It has been validated in several enterprise scenarios, including microservices-based SaaS platforms and SAP BTP ecosystems. Empirical findings from four production deployments and a beta group of 49 engineers indicate a decrease of up to 30% in mean time to resolution, over 95% compliance with SLAs, and substantial improvements in both test coverage and defect traceability. The effortless connection with industry-standard tools illustrates its plug-and-play capability.

This research presents a comprehensive, tool-independent, and forward-looking quality engineering methodology consistent with agile and DevOps principles. Future endeavors encompass dynamic anomaly classification through machine learning, extension to mobile and user experience-oriented systems, and augmented large language model capabilities for domain-specific test development and failure forecasting.

Introduction

The increasing popularity of the agile paradigm in software businesses has led to a growing interest in continuous integration environments. The advantages of such systems encompass the seamless integration of regular program modifications, resulting in accelerated and cost-effective software evolution. Consequently, it will efficiently manage duties like building procedures, test execution, and test result reporting. Software testing has been implemented since the inception of software engineering. The practice of software testing was implemented to assess the quality of software¹. Testing encompasses a range of actions aimed at detecting and ....

Protocol

System architecture and prototype summary:

This research presents an improved and adaptable prototype system, PreventativeTestPro, that exemplifies a proactive quality engineering approach utilizing observability data and large language models (LLMs) to further improve support issue resolution. The system seeks to tackle modern software delivery issues by automating anomaly detection, root cause analysis, and the intelligent execution and development of test cases for unaddressed coverage using synthetic monitoring, observability data, and GenAI integration. The architecture is modular and comprises three core components: Observability Data C....

Results

Initially, we shared the outcomes derived from the case studies conducted in collaboration with various industries in real-time. Furthermore, we have provided the outcomes derived from the beta testers who have utilized this framework and algorithm, together with the final observations on potential risks to the validity of the results.

Industry case study results:

Based on our research, which focuses on practical applications and addresses support c.......

Discussion

This research presents PreventativeTestPro, a comprehensive testing and observability platform that integrates synthetic monitoring, observability data, and generative AI-driven automation to improve software quality assurance. The system consists of three fundamental modules: an observability data collector and analyzer, a generative AI-driven intelligence layer, and a test orchestration and execution engine. Collectively, these components establish a feedback loop in which real-time system behaviors guide test case pro.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Apache Maven	Apache Software Foundation	3.9.6	Dependency and project management tool for Java projects
ChatGPT (GPT-3.5 Turbo API)	OpenAI	https://platform.openai.com/api-keys	For generating AI-based test recommendations from logs, generating the manual test cases, generating the automated test cases and getting the root cause analysis
Computer (Development/Test Machine)	Standard Desktop/Laptop	-	Used for developing, executing, and testing PreventativeTestPro
Disk Space	-	-	At least 10 GB of free disk space recommended for logs, reports, and test artifacts
Docker	Docker Inc.	27 (https://docs.docker.com/desktop/setup/install/windows-install/)	Used for containerization to ensure reproducibility across environments
Git	Git SCM	git version 2.45.2.windows.1	Version control system used for development and collaboration
GitHub Repository	GitHub	https://github.com/sohambpatel/PreventativeTests	Public repository containing source code, documentation, datasets, and examples
Google Chrome	Google	140.0.7339.128	Primary browser used for synthetic monitoring and testing
Java	Oracle / OpenJDK	21.0.2	Used for software development and execution of PreventativeTestPro
Operating System	Platform Independent	-	Tool works on any OS with Java and Maven installed (Windows, Linux, macOS)
OWASP ZAP	OWASP Foundation	2.14.0	Security scanning and vulnerability detection tool
Processor	-	-	Intel i5 or higher (or equivalent) recommended for parallel execution and AI processing
RAM	-	-	Minimum 8 GB RAM recommended for running tests and browser-based monitoring