A Time Series Analysis of TravisTorrent Builds: To Everything There Is a Season

We apply a seasonal decomposition time series analysisto TravisTorrent data in order to examine growth trendsand periodic behavior related to number of builds ina continuous integration environment. We apply our techniquesat the macro level using the full TravisTorrent repository consisting of 1,283...

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Veröffentlicht in:	2017 IEEE/ACM 14th International Conference on Mining Software Repositories (MSR) S. 463 - 466
Hauptverfasser:	Atchison, Abigail, Berardi, Christina, Best, Natalie, Stevens, Elizabeth, Linstead, Erik
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.05.2017
Schlagworte:	Additives Build Data Data Seasonality Forecasting History Software Time Series Time series analysis TravisTorrent
Online-Zugang:	Volltext
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Beschreibung
Zusammenfassung:	We apply a seasonal decomposition time series analysisto TravisTorrent data in order to examine growth trendsand periodic behavior related to number of builds ina continuous integration environment. We apply our techniquesat 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 builddata, our techniques are also able to accurately forecast the expected number of builds for a future time interval.
DOI:	10.1109/MSR.2017.29