MSR 2020 : Mining Software Repositories

The technical track of MSR 2020 solicits novel, high quality submissions on a wide range of topics, including (but not limited to):

• Analysis of software data with the goal of improving software productivity and reliability
• Analysis and modeling of runtime information to optimize deployment, delivery and error handling in software development processes
• Analysis of change patterns and trends to assist in future development
• Analysis of natural language artifacts in software data
• Analysis of software ecosystems and mining of software data across multiple projects
• Approaches, applications, and tools for mining software data
• Artificial intelligence for software engineering
• Characterization, classification, and prediction of software defects based on analysis of software data
• Characterization of bias in mining and guidelines to ensure the quality of results
• Data science for software projects
• Empirical studies on extracting data from large long-lived and/or industrial projects
• Machine learning for software engineering
• Meta-models, exchange formats, and infrastructure tools to facilitate the sharing of extracted data and to encourage reuse and repeatability
• Methods of integrating mined data from various historical sources
• Mining code review data
• Mining execution traces and logs
• Mining human and social aspects of development
• Mining interaction data
• Mining mobile app stores and app reviews
• Mining software licensing and copyrights
• Models for social and development processes in large software projects
• Models of software project evolution based on historical repository data
• Models and processes for improving the quality of machine learning pipelines
• Natural language processing in software engineering
• Prediction and modeling of software quality
• Privacy and ethics in mining software data
• Release engineering, including continuous integration, delivery and deployment
• Search-driven software development, including search techniques to assist developers in finding suitable components and code fragments for reuse, and software search engines
• Software analytics
• Software engineering for artificial intelligence and machine learning
• Energy efficiency of software
• Studies of programming language features and their usage
• Techniques and tools for capturing new forms of software data such as effort data, fine-grained changes, and refactoring
• Techniques to model reliability and defect occurrences
• Visualization techniques and models of mined data