Quantum software plays a critical role in exploiting the full potential of quantum computing systems. As a result, it has been drawing increasing attention recently. This paper defines the term "quantum software engineering" and introduces a quantum software life cycle. The paper also gives a generic view of quantum software engineering and discusses the quantum software engineering processes, methods, and tools. Based on these, the paper provides a comprehensive survey of the current state of the art in the field and presents the challenges and opportunities we face. The survey summarizes the technology available in the various phases of the quantum software life cycle, including quantum software requirements analysis, design, implementation, test, and maintenance. It also covers the crucial issues of quantum software reuse and measurement.
University College London（伦敦大学学院）；Facebook London（Facebook伦敦）；Kyushu University（九州大学）；Nanyang Technological University（南洋理工大学）
This paper provides a comprehensive survey of Machine Learning Testing (ML testing) research. It covers 144 papers on testing properties (e.g., correctness, robustness, and fairness), testing components (e.g., the data, learning program, and framework), testing workflow (e.g., test generation and test evaluation), and application scenarios (e.g., autonomous driving, machine translation). The paper also analyses trends concerning datasets, research trends, and research focus, concluding with research challenges and promising research directions in ML testing.
Search-Based Software Engineering and AI Foundation Models: Current Landscape and Future Roadmap
Simula Research Laboratory（Simula研究实验室）；National Institute of Informatics（日本信息机构）；Kristiania University of Applied Sciences and Oslo Metropolitan University（克里斯蒂安尼亚应用科学大学和奥斯陆 Metropolitan 大学）
Satisfactory software performance is essential for the adoption and the success of a product. In organizations that follow traditional software development models (e.g., waterfall), Software Performance Engineering (SPE) involves time-consuming experimental modeling and performance testing outside the actual production environment. Such existing SPE methods, however, are not optimized for environments utilizing Continuous Integration (CI) and Continuous Delivery (CD) that result in high frequency and high volume of code changes. We present a summary of lessons learned and propose improvements …
Agents in Software Engineering: Survey, Landscape, and Vision
Sun Yat-sen University（中山大学）；Xi’an Jiaotong University（西安交通大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Xiamen University（厦门大学）；Huawei Cloud Computing Technologies Co., Ltd.（华为云计算技术有限公司）
Taking a Pulse on How Generative AI is Reshaping the Software Engineering Research Landscape
Colorado State University（科罗拉多州立大学）；University of Bari（巴里大学）；University of Auckland（奥克兰大学）；IT University of Copenhagen（哥本哈根信息技术大学）；University of California Irvine（加州大学尔湾分校）；University of Victoria（维多利亚大学）
Doc2Harness: Benchmarking LLMs on Long-Horizon Harness Engineering of Coding Agents
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Tsinghua University（清华大学）；Peking University（北京大学）；Southeast University（东南大学）
We describe the design of an integrated development environment (IDE) for PSF. In the software engineering process we used process algebra in the form of PSF for the specification of the architecture of the IDE. This specification is refined to a PSF specification of the IDE system as a ToolBus application, by applying vertical and horizontal implementation techniques. We implemented the various tools as specified and connected them with a ToolBus script extracted from the system specification.
The software engineering field has a long history of classifying software failure causes. Understanding them is paramount for fault injection, focusing testing efforts or reliability prediction. Since software fails in manifold complex ways, a broad range of software failure cause models is meanwhile published in dependability literature. We present the results of a meta-study that classifies publications containing a software failure cause model in topic clusters. Our results structure the research field and can help to identify gaps. We applied the systematic mapping methodology for performi…
