The 20th International Conference on Mining Software Repositories (MSR 2023) was held in Melbourne, Australia in May 2023, where the research paper AutoML from Software Engineering Perspective: Landscapes and Challenges was honored with the ACM SIGSOFT Distinguished Paper Award. The authors include Chao Wang (PhD Student, School of Computer Science, Peking University), Zhenpeng Chen (Research Fellow. Department of Computer Science. University College London), and Minghui Zhou (Tenured Full Professor of PKU’s School of Computer Science, Software Engineering Institute).

The ACM SIGSOFT Distinguished Paper Award is an award established by ACM SIGSOFT to reward no more than 10% of outstanding papers presented at top software engineering conferences.

In the paper, a comprehensive understanding of the application of automated machine learning (AutoML) technology in software development is provided through the exploration and analysis of open-source software artifacts. With the widespread use of machine learning in software applications, AutoML technology, as an essential tool for lowering the barriers to machine learning training, has garnered significant attention from developers. However, due to the scarcity of relevant knowledge, developers often face various challenges at different stages of implementing AutoML technology. Addressing this issue, this work, through the exploration and analysis of relevant software artifacts, presented a detailed portrayal of the development trends, application scenarios, usability, and common challenges of AutoML technology in software development applications for the first time. The research results offer potential solutions to the common challenges faced by developers in practice, provide guidance for developers to better utilize AutoML technology, and highlight the engineering and theoretical support that is still lacking in related practices. Professor Minghui Zhou's research group has been engaged in research related to open-source software development, with a series of achievements in the measurement, analysis, prediction, and intelligent support of open-source software and communities (http://osslab-pku.org/).