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[PAST EVENT] Colloquium: : Big(ger) Data in Software Engineering
February 17, 2014
8am - 9am
Meiyappan Nagappan, Queen's University
Abstract
My research is centered around analyzing Software Engineering (SE) datasets that are several orders of magnitude bigger than the typical SE datasets. Examples of my datasets include: all the mobile apps in the Google Play store, all of the world's Open Source projects, and hundreds of gigabytes of execution logs. Such large datasets, provide us with a unique view into the SE field.
However, these large datasets also bring some tough challenges given their 4 V's (volume, variety, velocity, and veracity). Such challenges often complicate the analysis of the data and can invalidate the interpretation of the results. In this talk, I present an overview of key results from several of my recent studies, and how my collaborators and I overcame these challenges. In particular, I will touch on some of the key research questions that I have tackled:
- How can we pick a diverse sample of projects from all the available projects in the world for a case study?
- How come the mobile app markets are growing so fast while still ensuring a high quality user experience?
I will conclude my talk with some of the exciting research
opportunities present in analyzing such large datasets.
Bio
Meiyappan Nagappan is a postdoctoral fellow in the Software Analysis and Intelligence Lab (SAIL) at Queen's University, Canada. His research is centered around the use of large-scale Software Engineering (SE) data to address the concerns of the various stakeholders (e.g., developers, operators, and managers). He received a PhD in computer science from North Carolina State University.
Dr. Nagappan has published in various top SE venues such as TSE, FSE, EMSE, and IEEE Software. He has also received a best paper award at the International Working Conference on Mining Software Repositories (MSR 12). He continues to collaborate with both industrial and academic researchers from the US, Canada, Japan, Germany, Chile, and India. You can find more about him at http://sailhome.cs.queensu.ca/~mei/.
Abstract
My research is centered around analyzing Software Engineering (SE) datasets that are several orders of magnitude bigger than the typical SE datasets. Examples of my datasets include: all the mobile apps in the Google Play store, all of the world's Open Source projects, and hundreds of gigabytes of execution logs. Such large datasets, provide us with a unique view into the SE field.
However, these large datasets also bring some tough challenges given their 4 V's (volume, variety, velocity, and veracity). Such challenges often complicate the analysis of the data and can invalidate the interpretation of the results. In this talk, I present an overview of key results from several of my recent studies, and how my collaborators and I overcame these challenges. In particular, I will touch on some of the key research questions that I have tackled:
- How can we pick a diverse sample of projects from all the available projects in the world for a case study?
- How come the mobile app markets are growing so fast while still ensuring a high quality user experience?
I will conclude my talk with some of the exciting research
opportunities present in analyzing such large datasets.
Bio
Meiyappan Nagappan is a postdoctoral fellow in the Software Analysis and Intelligence Lab (SAIL) at Queen's University, Canada. His research is centered around the use of large-scale Software Engineering (SE) data to address the concerns of the various stakeholders (e.g., developers, operators, and managers). He received a PhD in computer science from North Carolina State University.
Dr. Nagappan has published in various top SE venues such as TSE, FSE, EMSE, and IEEE Software. He has also received a best paper award at the International Working Conference on Mining Software Repositories (MSR 12). He continues to collaborate with both industrial and academic researchers from the US, Canada, Japan, Germany, Chile, and India. You can find more about him at http://sailhome.cs.queensu.ca/~mei/.