NDLI: Software evolution and time series volatility: an empirical exploration

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Software evolution and time series volatility: an empirical exploration

Content Provider	ACM Digital Library
Author	Ruohonen, Jukka Hyrynsalmi, Sami Leppänen, Ville
Abstract	The paper presents the first empirical study to examine econometric time series volatility modeling in the software evolution context. The econometric volatility concept is related to the conditional variance of a time series rather than the conditional mean targeted in conventional regression analysis. The software evolution context is motivated by relating these variance characteristics to the proximity of operating system releases, the theoretical hypothesis being that volatile characteristics increase nearby new milestone releases. The empirical experiment is done with a case study of FreeBSD. The analysis is carried out with 12 time series related to bug tracking, development activity, and communication. A historical period from 1995 to 2011 is covered under a daily sampling frequency. According to the results the time series dataset contains visible volatility characteristics, but these cannot be explained by the time windows around the six observed major FreeBSD releases. The paper consequently contributes to the software evolution research field with new methodological ideas, as well as with both positive and negative empirical results. .
Starting Page	56
Ending Page	65
Page Count	10
File Format	PDF
ISBN	9781450338165
DOI	10.1145/2804360.2804367
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-08-30
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Software evolution Conditional variance Arima Time series analysis Volatility Code churn Garch Freebsd
Content Type	Text
Resource Type	Article

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