Software systems have been playing important roles in business, scientific research, and our everyday lives. It is critical to improve both software productivity and quality, which are major challenges to software engineering researchers and practitioners. As developers work on a project, they leave behind many digital artifacts. These digital trails can provide insights into how software is developed and provide a rich source of information to help improve development practices. For instance, GitHub hosts more than 57M repositories, and is currently used by more than 20M developers. As another example, Stack Overflow has more than 3.9M registered users, 8.8M questions, and 41M comments. The productivity of software developers and testers can be improved using information from these repositories.
TIn recent years, intelligent software engineering has emerged as a promising means to address these challenges. In intelligent software engineering, Artificial Intelligence (AI) techniques (e.g., deep learning) have been frequently applied to discover knowledge or build intelligent tools from software artifacts (e.g., specifications, source code, documentations, execution logs, code commits and bug reports) to improve software quality and development process (e.g., to obtain the insights for the causes leading to poor software quality, and to help the managers optimize the resources for better productivity). And these techniques have shown a great success in addressing various software engineering problems (e.g., code generation, code recommendation, and bug fix and repair). Therefore, intelligent software engineering has attracted great attention in both software engineering and AI communities.
We invite the submission of high-quality papers describing original and significant work in all areas of intelligent software engineering including (but not limited to): 1) Methodological and technical foundations of intelligent software engineering, 2) Approaches and techniques for knowledge discovery in various software artefacts, and 3) Applications of AI techniques to facilitate specialized tasks in software engineering. Topics of interest include but are not limited to:
To speed up the review process, and to encourage people to join Internetware 2020 conference (in Singapore), we setup two rounds of reviews: a Internetware conference round, and a journal round.
In the Internetware conference round, authors are encouraged to submit their manuscripts to the Easychair submission site https://easychair.org/conferences/?conf=internetware2020, You should choose “Intelligent Software Engineering Track” in Easychair. The submissions must have not been previously published or considered for publication elsewhere. Each submission must not exceed 10 pages for all text, figures, tables, and references. All submissions must be in English and in PDF format. Please use the ACM Master article template, as can be obtained from the ACM Proceedings Template pages. Each submission will receive at least three high-quality reviews from our PCs. Each accepted submission must be accompanied by a registration of at least one author and presented at Internetware 2020.
Xin Xia, Monash University, Australia (Xin.Xia@monash.edu)
David Lo, Singapore Management University, Singapore (firstname.lastname@example.org )
Ge Li, Peking University, China (email@example.com)