Name
Abstract | ||
|---|---|---|
We apply a seasonal decomposition time series analysis to TravisTorrent data in order to examine growth trends and periodic behavior related to number of builds in a continuous integration environment. We apply our techniques at the macro level using the full TravisTorrent repository consisting of 1,283 projects, and at the micro level considering the Apache Drill project. Our results demonstrate strong seasonal behavior at both the large and small scale using an additive time series model. In addition to being able to accurately capture trend and periodicity in build data, our techniques are also able to accurately forecast the expected number of builds for a future time interval. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/MSR.2017.29 | MSR |
Keywords | Field | DocType |
TravisTorrent, Time Series, Build Data, Data Seasonality | Data mining,Time series,Computer science,Expected value,Software,Continuous integration,Drill,Macro,Periodic graph (geometry),Market research | Conference |
ISSN | ISBN | Citations |
2160-1852 | 978-1-5386-1545-4 | 3 |
PageRank | References | Authors |
0.37 | 7 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Abigail Atchison | 1 | 14 | 2.37 |
| Christina Berardi | 2 | 3 | 0.70 |
| Natalie Best | 3 | 7 | 1.09 |
| Elizabeth Stevens | 4 | 5 | 0.74 |
| Erik Linstead | 5 | 360 | 27.44 |