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Publication search results
found 70 matches
- 2016
- Thomas Zimmermann:
Card-sorting. Perspectives on Data Science for Software Engineering 2016: 137-141 - Stefan Wagner:
Natural language processing is no free lunch. Perspectives on Data Science for Software Engineering 2016: 175-179 - Thomas Zimmermann:
One size does not fit all. Perspectives on Data Science for Software Engineering 2016: 347-348 - Jeff Huang:
Gameplay data plays nicer when divided into cohorts. Perspectives on Data Science for Software Engineering 2016: 81-84 - Bram Adams:
How the release process impacts your software analytics. Perspectives on Data Science for Software Engineering 2016: 249-253 - Alberto Bacchelli:
Structure your unstructured data first! Perspectives on Data Science for Software Engineering 2016: 161-168 - Titus Barik, Emerson R. Murphy-Hill:
A process for surviving survey design and sailing through survey deployment. Perspectives on Data Science for Software Engineering 2016: 213-219 - Earl T. Barr, Premkumar T. Devanbu:
The naturalness of software ☆. Perspectives on Data Science for Software Engineering 2016: 51-55 - Olga Baysal:
A tailored suit. Perspectives on Data Science for Software Engineering 2016: 107-110 - Ayse Bener, Ayse Tosun:
If it is software engineering, it is (probably) a Bayesian factor. Perspectives on Data Science for Software Engineering 2016: 187-191 - Ayse Bener, Burak Turhan, Ayse Tosun, Bora Caglayan, Ekrem Kocaguneli:
A success story in applying data science in practice. Perspectives on Data Science for Software Engineering 2016: 85-90 - Christian Bird:
Interviews. Perspectives on Data Science for Software Engineering 2016: 125-131 - Christian Bird:
Don't embarrass yourself. Perspectives on Data Science for Software Engineering 2016: 309-315 - David Budgen:
Aggregating empirical evidence for more trustworthy decisions. Perspectives on Data Science for Software Engineering 2016: 181-186 - Trevor Carnahan:
Reducing time to insight. Perspectives on Data Science for Software Engineering 2016: 239-243 - Jacek Czerwonka:
Code reviews are not for finding defects - Even established tools need occasional evaluation. Perspectives on Data Science for Software Engineering 2016: 119-122 - Stephan Diehl:
Make visualization part of your analysis process. Perspectives on Data Science for Software Engineering 2016: 267-269 - Tore Dybå, Gunnar R. Bergersen, Dag I. K. Sjøberg:
Evidence-based software engineering. Perspectives on Data Science for Software Engineering 2016: 149-153 - Thomas Fritz:
Measuring individual productivity. Perspectives on Data Science for Software Engineering 2016: 67-71 - Harold Valdivia Garcia, Meiyappan Nagappan:
Diversity in software engineering research. Perspectives on Data Science for Software Engineering 2016: 295-298 - Michael W. Godfrey:
Why provenance matters. Perspectives on Data Science for Software Engineering 2016: 227-231 - Georgios Gousios:
Open from the beginning. Perspectives on Data Science for Software Engineering 2016: 233-237 - Philip J. Guo:
Parse that data! Practical tips for preparing your raw data for analysis. Perspectives on Data Science for Software Engineering 2016: 169-173 - Kim Herzig:
There's never enough time to do all the testing you want. Perspectives on Data Science for Software Engineering 2016: 91-95 - Abram Hindle:
The perils of energy mining. Perspectives on Data Science for Software Engineering 2016: 97-102 - Reid Holmes:
Look for state transitions in temporal data. Perspectives on Data Science for Software Engineering 2016: 133-135 - Zhitao Hou, Hongyu Zhang, Haidong Zhang, Dongmei Zhang:
Visual analytics for software engineering data. Perspectives on Data Science for Software Engineering 2016: 77-80 - Natalia Juristo:
Once is not enough. Perspectives on Data Science for Software Engineering 2016: 299-302 - Sascha Just, Kim Herzig:
Gotchas from mining bug reports. Perspectives on Data Science for Software Engineering 2016: 261-265 - Miryung Kim:
Five steps for success. Perspectives on Data Science for Software Engineering 2016: 245-248
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