Mining Software Repositories 2019
The Mining Software Repositories (MSR) field analyzes the rich data available in software repositories to uncover interesting and actionable information about software systems and projects. The goal of this two-day conference is to advance the science and practice of MSR. The 16th International Conference on Mining Software Repositories will be co-located with ICSE 2019 in Montréal, QC, Canada.
Software repositories such as source control systems, archived communications between project personnel, and defect tracking systems are used to help manage the progress of software projects. Software practitioners and researchers are recognizing the benefits of mining this information to support the maintenance of software systems, improve software design/reuse, and empirically validate novel ideas and techniques. Research is now proceeding to uncover the ways in which mining these repositories can help to understand software development and software evolution, to support predictions about software development, and to exploit this knowledge in planning future development. The goal of this two-day international conference is to advance the science and practice of software engineering via the analysis of data stored in software repositories.
Education Track (New this year)
This year, MSR will feature a new education track. The track will consist of a panel to discuss the latest teaching practices, a tutorial for newcomers to MSR, and a session to showcase community shared educational resources—these resources will be an open project that MSR researchers are welcome to contribute to like an open source project. Check the Track’s page for more information!
This year’s tutorials:
“Software Analytics in Action: A Hands-on Tutorial on Analyzing and Modelling Software Data” by Chakkrit (Kla) Tantithamthavorn from Monash University, Australia.
"Qualitative Data Analysis in Software Engineering: A Hands-on Tutorial” by Christoph Treude from the University of Adelaide, Australia.
EMSE Special Issue
A selection of the best research and data papers will be invited to be revised and extended for consideration in a special issue of the for consideration in a special issue of the Empirical Software Engineering (EMSE) journal edited by Springer.
MSR FOSS Impact Paper Award
In an effort to encourage research on understanding and improving FOSS (Free, Open Source Software), MSR has established the “FOSS Impact paper” award. The award will be granted to papers that show outstanding contributions to the FOSS community. For many years, the MSR community has leveraged public data from FOSS projects, and in the process the community has contributed new insights, tools and techniques to assist FOSS projects in different ways. This award recognizes and encourages such line of research.
Authors can self-nominate their research papers for the FOSS award, after which the dedicated committee will evaluate these papers.
More information about the MSR 2019 FOSS Award will be added soon!
The Impact and Value of MSR publications:
The MSR conference is ranked as a CORE A conference, which is an “excellent conference, and highly respected in a discipline area”. For additional information concerning the impact and value of MSR publications, please consult this document.
The MSR 2019 Award Recipients!
For the MSR Foundational Contribution award the recipient is
Katsuro Inoue for fostering a vibrant international community around software clone analysis and the development of the CCFinder clone detector, which has enabled countless others to do research involving code clones.
For the MSR Early Career Achievement award the recipient is
Emad Shihab for contributions to the state of the art in research and practice in software quality assurance as well as outreach and education efforts throughout the international MSR community.
Congratulations to both recipients!
Save the date: Rob DeLine's keynote on Sunday May 26th!
Rob DeLine, a Principal Researcher at Microsoft Research, has spent the last thirty years designing programming environments for a variety of audiences: end users making 3D environments (Alice); software architects composing systems (Unicon); professional programmers exploring unfamiliar code (Code Thumbnails, Code Canvas, Debugger Canvas); and, most recently, data scientists analyzing streaming data (Tempe). He is a strong advocate of user-centered design and founded a research group applying that approach to software development tools. This approach aims for a virtuous cycle: conducting empirical studies to understand software development practices; inventing technologies that aim to improve those practices; and then deploying these technologies to test whether they actually do.
Title: We won! So what?
To quote our research community’s succinct mission statement: “The Mining Software Repositories (MSR) field analyzes the rich data available in software repositories to uncover interesting and actionable information about software systems and projects.” In the earliest days of this conference, this mission was a novel possibility that the flourishing Open Source movement created. These days, however, the practice of turning repository data into actionable insights and deployed models has become bog standard. So, congratulations to the MSR community for leading the way! But now what? MSR finds itself caught in a heated competition among industry researchers and data scientists to find novel ways to exploit data and apply models. Given the resources and energy that industry now invests in data science and machine learning, MSR cannot hope to succeed by working on the same types of problems, using the same techniques. It’s time to pivot. Luckily there are hard open problems for which industry is hungry for results: How can we continue to get insights and build models while upholding privacy laws (GDPR) and user privacy preferences? How can we make trained models understandable to all relevant stakeholders? How can we ensure that our insights and models are not harmed by human biases like sexism, racism, political manipulation, etc.? The first half of this talk will describe current industry practice in data science and machine learning, based on recent studies. In the second half, I’ll describe some difficult new problems, to prod energetic discussion about the future direction of MSR.