Artificial Intelligence in Software Testing : Impact, Problems, Challenges and Prospect

Minimol Anil Job

2021

The final product of software development process is a software system and testing is one of the important stages in this process. The success of this process can be determined by how well it accomplishes its goal. Due to the advancement of technology, various software testing tools have been introduced in the software engineering discipline. The use of software is increasing day-by-day and complexity of software functions are challenging and there is need to release the software within the short quality evaluation period, there is a high demand in adopting automation in software testing. Emergence of automatic software testing tools and techniques helps in quality enhancement and reducing time and cost in the software development activity. Artificial Intelligence (AI) techniques are widely applied in different areas of Software engineering (SE). Application of AI techniques can help in achieving good performance in software Testing and increase the productivity of the software development firms. This paper briefly presents the state of the art in the field of software testing by applying AI techniques in software testing. Keywords—Software testing; artificial intelligence; testing automation; software engineering; software quality

Naveenkumar Jayakumar Nilofar Mulla

5 April 2021

This study provides information about the use of artificial intelligence (AI) and machine learning (ML) techniques in the field of software testing. The use of AI in software testing is still in its initial stages. Also the automation level is lesser compared to more evolved areas of work.AI and ML can be used to help reduce tediousness and automate tasks in software testing. Testing can be made more efficient and smarter with the help of AI. Researchers recognize potential of AI to bridge the gap between human and machine driven testing capabilities. There are still number of challenges to fully utilize AI and ML techniques in testing but it will definitely enhance the entire testing process and skills of testers and will contribute in business growth. Machine learning research is a subset of overall AI research. The lifecycle of software is increasingly shortening and becoming more complicated. There is a struggle in software development between the competing pressures of developing software and meeting deadlines. AI-powered automated testing makes conducting full test suites in a timely manner on every change. In this article a detailed overview about the various applications of AI in software testing have been demonstrated. Also the implementation of machine learning in software testing has been discussed in detail and use of different machine learning techniques has been explained as well.

S. Matalonga Stefano Faralli J. Hauck + 2 lainnya

17 Agustus 2023

Context: Artificial intelligence (AI) methods and models have extensively been applied to support different phases of the software development lifecycle, including software testing (ST). Several secondary studies investigated the interplay between AI and ST but restricted the scope of the research to specific domains or sub-domains within either area. Objective: This research aims to explore the overall contribution of AI to ST, while identifying the most popular applications and potential paths for future research directions. Method: We executed a tertiary study following well-established guidelines for conducting systematic literature mappings in software engineering and for answering nine research questions. Results: We identified and analyzed 20 relevant secondary studies. The analysis was performed by drawing from well-recognized AI and ST taxonomies and mapping the selected studies according to them. The resulting mapping and discussions provide extensive and detailed information on the interplay between AI and ST. Conclusion: The application of AI to support ST is a well-consolidated and growing interest research topic. The mapping resulting from our study can be used by researchers to identify opportunities for future research, and by practitioners looking for evidence-based information on which AI-supported technology to possibly adopt in their testing processes.

Kishore Sugali Venkata N. Inukollu Chris Sprunger

31 Januari 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.

Farhan Khan Sabrina Alam Mahmudul Islam + 1 lainnya

31 Oktober 2023

Software testing is a crucial component of software development. With the increasing complexity of software systems, traditional manual testing methods are becoming less feasible. Artificial Intelligence (AI) has emerged as a promising approach to software testing in recent years. This review paper aims to provide an in-depth understanding of the current state of software testing using AI. The review will examine the various approaches, techniques, and tools used in this area and assess their effectiveness. The selected articles for this study have been extracted from different research databases using the advanced search string strategy. Initially, 40 articles have been extracted from different research libraries. After gradual filtering finally, 20 articles have been selected for the study. After studying all the selected papers, we find that various testing tasks can be automated successfully using AI (Machine Learning and Deep Learning) such as Test Case Generation, Defect Prediction, Test Case Prioritization Metamorphic Testing, Android Testing, Test Case Validation, and White Box Testing. This study also finds that the integration of AI in software testing is making software testing activities easier along with better performance. This literature review paper provides a thorough analysis of the impact AI can have on the software testing process.