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IEEE Transactions on Software Engineering
"IEEE Transactions on Software Engineering is interested in well-defined theoretical results and empirical studies that have potential impact on the construction, analysis, or management of software. The scope of this Transactions ranges from the mechanisms through the development of principles to the application of those principles to specific environments. Specific topic areas include: a) development and maintenance methods and models, e.g., techniques and principles for the specification, design, and implementation of software systems, including notations and process models; b) assessment methods, e.g., software tests and validation, reliability models, test and diagnosis procedures, software redundancy and design for error control, and the measurements and evaluation of various aspects of the process and product; c) software project management, e.g., productivity factors, cost models, schedule and organizational issues, standards; d) tools and environments, e.g., specific tools, integrated tool environments including the associated architectures, databases, and parallel and distributed processing issues; e) system issues, e.g., hardware-software trade-off; and f) state-of-the-art surveys that provide a synthesis and comprehensive review of the historical development of one particular area of interest."
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Paper: The Impact of Prompt Programming on Function-Level Code Generation
arXiv
Paper: Software Testing with Large Language Models: Survey, Landscape, and Vision
ACM Computing Surveys
"These comprehensive, readable surveys and tutorial papers give guided tours through the literature and explain topics to those who seek to learn the basics of areas outside their specialties in an accessible way. The carefully planned and presented introductions in Computing Surveys (CSUR) are also an excellent way for researchers and professionals to develop perspectives on, and identify trends in complex technologies. Contributions which bridge existing and emerging technologies (such as machine learning) with a variety of science and engineering domains in a novel and interesting way are also welcomed. Contributions are intended to be accessible to a broad audience, featuring clear exposition, a lively tutorial style, and pointers to the literature for further study."