AI-Driven Test Case Optimization for Performance Engineering

EasyChair Preprint 12913

Abstract

In the field of software development, ensuring optimal performance is crucial for delivering high-quality applications. Performance engineering involves identifying and eliminating bottlenecks and inefficiencies to enhance the system's responsiveness, scalability, and resource utilization. One key aspect of performance engineering is the creation and execution of test cases to assess the system's performance under various conditions.

Traditionally, test case creation and optimization have been labor-intensive and time-consuming tasks. However, recent advancements in artificial intelligence (AI) have paved the way for more efficient and effective approaches. This abstract presents an overview of an AI-driven test case optimization technique specifically tailored for performance engineering.

The proposed approach leverages AI algorithms to automatically generate and optimize test cases, taking into account various factors such as system architecture, workload patterns, and performance requirements. By analyzing historical performance data and utilizing machine learning techniques, the AI model can identify critical areas of the system that require testing and optimization.

The AI-driven test case optimization process involves several steps. Firstly, the system under test is profiled to gather relevant performance metrics. These metrics serve as input to the AI model, which uses them to identify performance bottlenecks and determine the most impactful test cases. The AI model then generates a set of test cases that target these bottlenecks, considering different workload scenarios and system configurations.

Keyphrases: Engineering, Performance, software

@booklet{EasyChair:12913,
  author    = {Favour Olaoye and Kaledio Potter},
  title     = {AI-Driven Test Case Optimization for Performance Engineering},
  howpublished = {EasyChair Preprint 12913},
  year      = {EasyChair, 2024}}