Software evolution and time series volatility: an empirical exploration

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.

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