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Deursen, Arie van
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ASE '17-KEY: "Software Engineering without ..."
Software Engineering without Borders
Arie van Deursen
(Delft University of Technology, Netherlands)
DevOps approaches software engineering by advocating the removal of borders between development and operations. DevOps emphasizes operational resilience, continuous feedback from operations back to development, and rapid deployment of features developed. In this talk we will look at selected (automation) aspects related to DevOps, based on our collaborations with various industrial partners. For example, we will explore (automated) methods for analyzing log data to support deployments and monitor REST API integrations, (search-based) test input generation for reproducing crashes and testing complex database queries, and zero downtime database schema evolution and deployment. We will close by looking at borders beyond those between development and operations, in order to see whether there are other borders we need to remove in order to strengthen the impact of software engineering research.
@InProceedings{ASE17p3,
author = {Arie van Deursen},
title = {Software Engineering without Borders},
booktitle = {Proc.\ ASE},
publisher = {IEEE},
pages = {3--3},
doi = {},
year = {2017},
}
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Han, Jiawei
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ASE '17-KEY: "Mining Structures from Massive ..."
Mining Structures from Massive Text Data: Will It Help Software Engineering?
Jiawei Han
(University of Illinois at Urbana-Champaign, USA)
The real-world big data are largely unstructured, interconnected text data. One of the grand challenges is to turn such massive unstructured text data into structured, actionable knowledge. We propose a text mining approach that requires only distant or minimal supervision but relies on massive text data. We show quality phrases can be mined from such massive text data, types can be extracted from massive text data with distant supervision, and entities/attributes/values can be discovered by meta-path directed pattern discovery. We show text-rich and structure-rich networks can be constructed from massive unstructured data. Finally, we speculate whether such a paradigm could be useful for turning massive software repositories into multi-dimensional structures to help searching and mining software repositories.
@InProceedings{ASE17p2,
author = {Jiawei Han},
title = {Mining Structures from Massive Text Data: Will It Help Software Engineering?},
booktitle = {Proc.\ ASE},
publisher = {IEEE},
pages = {2--2},
doi = {},
year = {2017},
}
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Holzmann, Gerard |
ASE '17-KEY: "Cobra - An Interactive Static ..."
Cobra - An Interactive Static Code Analyzer
Gerard Holzmann
(Nimble Research, USA)
Sadly we know that virtually all software of any significance has residual errors. Some of those errors can be traced back to requirements flaws or faulty design assumptions; others are just plain coding mistakes.
Static analyzers have become quite good at spotting these types of errors, but they don’t scale very well. If, for instance, you need to check a code base of a few million lines you better be prepared
to wait for the result; sometimes hours.
Eyeballing a large code base to find flaws is clearly not an option, so what is missing is a static analysis capability that can be used to answer common types of queries interactively, even for large code bases. I will describe the design and use of such a tool in this talk.
@InProceedings{ASE17p1,
author = {Gerard Holzmann},
title = {Cobra - An Interactive Static Code Analyzer},
booktitle = {Proc.\ ASE},
publisher = {IEEE},
pages = {1--1},
doi = {},
year = {2017},
}
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