Software Testing of Generative AI Systems: Challenges and Opportunities

• URL: http://arxiv.org/abs/2309.03554v3
• Date: Mon, 11 Sep 2023 10:03:55 GMT
• Title: Software Testing of Generative AI Systems: Challenges and Opportunities
• Authors: Aldeida Aleti
• Abstract summary: I will explore the challenges posed by generative AI systems and discuss potential opportunities for future research in the field of testing. I will touch on the specific characteristics of GenAI systems that make traditional testing techniques inadequate or insufficient.
• Score: 5.634825161148484
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Software Testing is a well-established area in software engineering, encompassing various techniques and methodologies to ensure the quality and reliability of software systems. However, with the advent of generative artificial intelligence (GenAI) systems, new challenges arise in the testing domain. These systems, capable of generating novel and creative outputs, introduce unique complexities that require novel testing approaches. In this paper, I aim to explore the challenges posed by generative AI systems and discuss potential opportunities for future research in the field of testing. I will touch on the specific characteristics of GenAI systems that make traditional testing techniques inadequate or insufficient. By addressing these challenges and pursuing further research, we can enhance our understanding of how to safeguard GenAI and pave the way for improved quality assurance in this rapidly evolving domain.

Related papers

• The Future of Software Testing: AI-Powered Test Case Generation and Validation [0.0]
  This paper explores the transformative potential of AI in improving test case generation and validation. It focuses on its ability to enhance efficiency, accuracy, and scalability in testing processes. It also addresses key challenges associated with adapting AI for testing, including the need for high quality training data.
  arXiv  Detail & Related papers  (2024-09-09T17:12:40Z)
• The Role of Artificial Intelligence and Machine Learning in Software Testing [0.14896196009851972]
  Artificial Intelligence (AI) and Machine Learning (ML) have significantly impacted various industries. Software testing, a crucial part of the software development lifecycle (SDLC), ensures the quality and reliability of software products. This paper explores the role of AI and ML in software testing by reviewing existing literature, analyzing current tools and techniques, and presenting case studies.
  arXiv  Detail & Related papers  (2024-09-04T13:25:13Z)
• Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems [88.80306881112313]
  We will introduce and define a family of approaches to AI safety, which we will refer to as guaranteed safe (GS) AI. The core feature of these approaches is that they aim to produce AI systems which are equipped with high-assurance quantitative safety guarantees. We outline a number of approaches for creating each of these three core components, describe the main technical challenges, and suggest a number of potential solutions to them.
  arXiv  Detail & Related papers  (2024-05-10T17:38:32Z)
• Bridging Gaps, Building Futures: Advancing Software Developer Diversity and Inclusion Through Future-Oriented Research [50.545824691484796]
  We present insights from SE researchers and practitioners on challenges and solutions regarding diversity and inclusion in SE. We share potential utopian and dystopian visions of the future and provide future research directions and implications for academia and industry.
  arXiv  Detail & Related papers  (2024-04-10T16:18:11Z)
• On the Challenges and Opportunities in Generative AI [135.2754367149689]
  We argue that current large-scale generative AI models do not sufficiently address several fundamental issues that hinder their widespread adoption across domains. In this work, we aim to identify key unresolved challenges in modern generative AI paradigms that should be tackled to further enhance their capabilities, versatility, and reliability.
  arXiv  Detail & Related papers  (2024-02-28T15:19:33Z)
• Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness [53.91018508439669]
  The study explores the complexities of integrating Artificial Intelligence into Autonomous Vehicles (AVs) It examines the challenges introduced by AI components and the impact on testing procedures. The paper identifies significant challenges and suggests future directions for research and development of AI in AV technology.
  arXiv  Detail & Related papers  (2024-02-21T08:29:42Z)
• The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence [67.70415658080121]
  Recent advances in machine learning and AI are disrupting technological innovation, product development, and society as a whole. AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access. Here we explore and investigate aspects of an AI-driven, automated, closed-loop approach to scientific discovery.
  arXiv  Detail & Related papers  (2023-07-09T21:16:56Z)
• Constrained Adversarial Learning and its applicability to Automated Software Testing: a systematic review [0.0]
  This systematic review is focused on the current state-of-the-art of constrained data generation methods applied for adversarial learning and software testing. It aims to guide researchers and developers to enhance testing tools with adversarial learning methods and improve the resilience and robustness of their digital systems.
  arXiv  Detail & Related papers  (2023-03-14T00:27:33Z)
• An interdisciplinary conceptual study of Artificial Intelligence (AI) for helping benefit-risk assessment practices: Towards a comprehensive qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
  This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence. The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
  arXiv  Detail & Related papers  (2021-05-07T12:01:31Z)
• Adaptive Immunity for Software: Towards Autonomous Self-healing Systems [0.6117371161379209]
  Self-healing software systems can detect, diagnose, and contain unanticipated problems at runtime. Recent advances in machine learning may be learned by observing the system. Artificial immune systems are particularly well-suited for building self-healing systems.
  arXiv  Detail & Related papers  (2021-01-07T13:22:55Z)
• Software engineering for artificial intelligence and machine learning software: A systematic literature review [6.681725960709127]
  This study aims to investigate how software engineering has been applied in the development of AI/ML systems. Main challenges faced by professionals are in areas of testing, AI software quality, and data management.
  arXiv  Detail & Related papers  (2020-11-07T11:06:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.