AI-driven Test Automation for Salesforce and System Integration

Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering

Software testing is a process of evaluating and verifying whether a software product still works as expected, and it is repetitive, laborious, and time-consuming. To address this problem, automation tools have been developed to automate testing activities and enhance quality and delivery time. However, automation tools become less effective with continuous integration and continuous delivery (CI/CD) pipelines when the system under test is constantly changing. Recent advances in artificial intelligence and machine learning (AI/ML) present the potential for addressing important challenges in test automation. AI/ML can be applied to automate various testing activities such as detecting bugs and errors, maintaining existing test cases, or generating new test cases much faster than humans. In this study, we will outline testing activities where AI has significantly impacted and greatly enhanced the testing process. Based on that, we identify primary AI techniques that are used in each testing activity. Further, we conduct a comprehensive study of test automation tools to provide a clear look at the role of AI/ML technology in industrial testing tools. The results of this paper help researchers and practitioners understand the current state of AI/ML applied to software testing, which is the first important step towards achieving successful and efficient software testing. CCS CONCEPTS • Software and its engineering → Software testing and debugging.

IAEME PUBLICATION, 2024

The integration of artificial intelligence (AI) and machine learning (ML) techniques into test automation frameworks has emerged as a promising approach to enhance software testing processes. This paper explores the potential of AI and ML in augmenting traditional test automation approaches, focusing on test case selection, execution, and analysis By leveraging data-driven insights and predictive analytics, AI/ML algorithms can intelligently prioritize test cases, facilitate dynamic test case generation, optimize test execution, and provide deeper insights into test results and defect patterns. Through case studies and empirical evaluations, this research aims to provide practical guidelines for integrating AI/ML into test automation frameworks, enabling organizations to improve software quality, accelerate time-to-market, and drive continuous innovation in software development processes.

2022

Artificial Intelligence (AI) is making a significant impact in multiple areas like medical, military, industrial, domestic, law, arts as AI is capable to perform several roles such as managing smart factories, driving autonomous vehicles, creating accurate weather forecasts, detecting cancer and personal assistants, etc. Software testing is the process of putting the software to test for some abnormal behaviour of the software. Software testing is a tedious, laborious and most time-consuming process. Automation tools have been developed that help to automate some activities of the testing process to enhance quality and timely delivery. Over time with the inclusion of continuous integration and continuous delivery (CI/CD) pipeline, automation tools are becoming less effective. The testing community is turning to AI to fill the gap as AI is able to check the code for bugs and errors without any human intervention and in a much faster way than humans. In this study, we aim to recognize...

IJARSCT, 2025

Automation systems have become a cornerstone of modern software testing infrastructure, enabling organizations to enhance test coverage, reduce human error, and accelerate release cycles. These systems are instrumental in managing repetitive testing tasks and optimizing complex test workflows, contributing to consistent software quality across development environments. However, despite their widespread adoption, conventional test automation frameworks often encounter substantial limitations when deployed in dynamic, real-world software projects.

The International Journal of Software Engineering & Applications (IJSEA), 2024

In the continually evolving domain of software development, guaranteeing the dependability and functionality of Application Programming Interfaces (APIs) is of utmost importance. Traditional approaches to API testing frequently encounter difficulties in keeping up with the dynamic nature of APIs, resulting in inefficiencies and overlooked defects. This research paper investigates the transformative potential of Artificial Intelligence (AI) in API testing, ushering in a new era of intelligent testing. Intelligent API testing harnesses the capabilities of AI to enhance the efficiency, precision, and adaptability of the testing process. API driven techniques enable the production of diverse and realistic test data, ensuring comprehensive test coverage. Furthermore, AI-powered algorithms can anticipate potential issues, identify anomalies, and optimize test case selection, all while adapting to evolving API schemas. This research paper delves into the various aspects of intelligent API testing, encompassing data generation, tools and technologies, benefits and impact, challenges, and real-world use cases. We illustrate how AI empowers testers to discover subtle defects, streamline testing endeavors, and enhance the overall quality of APIdriven applications. As we navigate the era of digital transformation, intelligent API testing emerges as an essential tool in the software development toolkit, enabling organizations to deliver robust and resilient APIs that fulfill the demands of contemporary applications. Embracing AI in API testing not only holds the promise of expediting the development lifecycle but also ensures that APIs remain agile and reliable in an ever-changing digital landscape.

International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), 2024

This scholarly article delves into the intersection of Artificial Intelligence (AI) and Test Automation, thoroughly examining the challenges inherent in implementing AI methodologies and elucidating imperatives critical for successful integration within contemporary software testing frameworks. The research entails a comprehensive exploration of challenges, ranging from intricacies in data quality to algorithmic biases, tool complexities, and integration challenges, drawing on empirical evidence from case studies and real-world scenarios. The paper articulates imperatives essential for overcoming challenges and ensuring the efficacy of AI in test automation. It emphasizes the significance of structured training programs, meticulous data management strategies, and the cultivation of an organizational culture conducive to the seamless integration of AI technologies. Through a rigorous analysis of successful case studies, the article provides a scholarly basis for the formulation of strategies and solutions to surmount challenges faced by organizations adopting AI in testing practices. A visual matrix aligning challenges with corresponding imperatives adds scholarly rigor to the article, offering a comprehensive framework for understanding the intricate relationships between challenges and the imperative strategies required for resolution. Furthermore, the exploration of emerging trends and innovations anticipates the future trajectory of AI-driven test automation, contributing valuable insights for strategic planning in the realm of software testing. This scholarly work underscores the importance of a systematic and informed approach to AI in Test Automation. By addressing challenges with academic rigor and embracing imperative strategies grounded in empirical evidence, organizations can position themselves at the forefront of AIdriven testing practices, advancing the field with a scholarly foundation for continued exploration and innovation.

Continuous Integration (CI) has become a cornerstone of modern software development, ensuring rapid feedback on code changes and enhancing software quality. However, traditional test case generation in CI pipelines remains manual, time-consuming, and prone to human error. Artificial Intelligence (AI) offers a transformative approach by automating test case generation, optimizing coverage, and adapting to evolving software requirements. This paper explores AIdriven test case generation techniques, their integration into CI workflows, and the resulting impact on efficiency, reliability, and software quality. We present a framework that leverages AI models to analyse code changes, historical defect patterns, and execution results to automatically generate, prioritize, and evolve test cases. Experimental results highlight improved defect detection rates and reduced testing overhead in CI environments.

International Journal of Artificial Intelligence & Applications, 2021

The history of Artificial Intelligence and Machine Learning dates back to 1950’s. In recent years, there has been an increase in popularity for applications that implement AI and ML technology. As with traditional development, software testing is a critical component of an efficient AI/ML application. However, the approach to development methodology used in AI/ML varies significantly from traditional development. Owing to these variations, numerous software testing challenges occur. This paper aims to recognize and to explain some of the biggest challenges that software testers face in dealing with AI/ML applications. For future research, this study has key implications. Each of the challenges outlined in this paper is ideal for further investigation and has great potential to shed light on the way to more productive software testing strategies and methodologies that can be applied to AI/ML applications.

International Journal of Artificial Intelligence & Applications, 2021

The history of Artificial Intelligence and Machine Learning dates back to 1950's. In recent years, there has been an increase in popularity for applications that implement AI and ML technology. As with traditional development, software testing is a critical component of an efficient AI/ML application. However, the approach to development methodology used in AI/ML varies significantly from traditional development. Owing to these variations, numerous software testing challenges occur. This paper aims to recognize and to explain some of the biggest challenges that software testers face in dealing with AI/ML applications. For future research, this study has key implications. Each of the challenges outlined in this paper is ideal for further investigation and has great potential to shed light on the way to more productive software testing strategies and methodologies that can be applied to AI/ML applications.

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2023

As the intricacy of web applications continues to escalate, the difficulties linked with the upkeep of testing in web-based automation have grown more apparent. Conventional automation scripts frequently encounter difficulties in adjusting to frequent modifications in web components, resulting in substantial manual endeavours and diminished testing efficacy. In reaction to these challenges, self-correcting web-based automation frameworks have emerged as a promising resolution. This critical review paper offers a comprehensive examination of web-based automation frameworks that have the ability to selfheal. It provides a thorough analysis of their principles, mechanisms, and real-world applications. Through the categorization and analysis of these frameworks' essential components, this review aims to elucidate their effectiveness in addressing the continuously evolving nature of web applications.While acknowledging the advancements made possible by self-healing frameworks, this review also explores the inherent challenges and limitations they possess. It outlines potential areas for future research and highlights emerging trends, such as the integration of artificial intelligence and machine learning, which have the potential to further enhance the self-healing capabilities of these frameworks. In summary, this evaluative critique offers a valuable asset for researchers, practitioners, and organisations aiming to optimize the process of managing tests in web-based automation. Through comprehending the fundamental concepts and potential compromises of self-healing frameworks for web-based automation, testing teams can strive towards effortless test maintenance and enhanced quality of web applications.