Authors
1
Research Scholar, Department of Mechanical Engineering ,Sri SatyaSai University of Technology and Medical Sciences, Sehore Bhopal-Indore Road, Madhya Pradesh, India
2
Research Guide, Department of Mechanical Engineering ,Sri SatyaSai University of Technology and Medical Sciences, Sehore Bhopal-Indore Road, Madhya Pradesh, India
Abstract
Automated Image-Based Testing (AIBT) has emerged as a promising approach to enhance software testing efficiency and accuracy, particularly in the context of graphical user interfaces (GUIs) and image-based applications. This paper presents a comprehensive framework for AIBT, aiming to provide a systematic and practical guide for achieving mastery and efficiency in image-based testing processes. The framework encompasses key stages of the testing lifecycle, including test design, test execution, result analysis, and test maintenance. It integrates a variety of techniques and methodologies, such as computer vision, machine learning, and test automation, to enable effective image recognition, test generation, and result verification. In the test design phase, the framework offers strategies for capturing representative test cases, defining test oracles, and addressing challenges related to dynamic interfaces and visual variances. During test execution, it leverages image comparison algorithms, adaptive recognition models, and intelligent test prioritization techniques to maximize the efficiency of test suites and reduce false positives and negatives. For result analysis, the framework provides mechanisms for robust result verification, log analysis, and defect localization. It also emphasizes the importance of continuous test maintenance to adapt to application changes and evolving user requirements. The proposed framework has been validated through empirical studies and case studies on various image-based applications, demonstrating its effectiveness in improving testing productivity and enhancing the reliability of software systems. By adopting this comprehensive framework, software testing practitioners can harness the power of AIBT to achieve mastery and efficiency in testing image-based applications, thereby reducing manual effort, accelerating testing cycles, and ensuring high software quality.
Keywords
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