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Software evolution and time series volatility: An empirical exploration

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Software evolution and time series volatility : An empirical exploration. / Ruohonen, Jukka; Hyrynsalmi, Sami; Leppänen, Ville.

14th International Workshop on Principles of Software Evolution, IWPSE 2015 - Proceedings. Vuosikerta 30-Aug-2015 Institute of Electrical and Electronics Engineers Inc., 2015. s. 56-65.

Tutkimustuotosvertaisarvioitu

Harvard

Ruohonen, J, Hyrynsalmi, S & Leppänen, V 2015, Software evolution and time series volatility: An empirical exploration. julkaisussa 14th International Workshop on Principles of Software Evolution, IWPSE 2015 - Proceedings. Vuosikerta. 30-Aug-2015, Institute of Electrical and Electronics Engineers Inc., Sivut 56-65, Bergamo, Italia, 30/08/15. https://doi.org/10.1145/2804360.2804367

APA

Ruohonen, J., Hyrynsalmi, S., & Leppänen, V. (2015). Software evolution and time series volatility: An empirical exploration. teoksessa 14th International Workshop on Principles of Software Evolution, IWPSE 2015 - Proceedings (Vuosikerta 30-Aug-2015, Sivut 56-65). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1145/2804360.2804367

Vancouver

Ruohonen J, Hyrynsalmi S, Leppänen V. Software evolution and time series volatility: An empirical exploration. julkaisussa 14th International Workshop on Principles of Software Evolution, IWPSE 2015 - Proceedings. Vuosikerta 30-Aug-2015. Institute of Electrical and Electronics Engineers Inc. 2015. s. 56-65 https://doi.org/10.1145/2804360.2804367

Author

Ruohonen, Jukka ; Hyrynsalmi, Sami ; Leppänen, Ville. / Software evolution and time series volatility : An empirical exploration. 14th International Workshop on Principles of Software Evolution, IWPSE 2015 - Proceedings. Vuosikerta 30-Aug-2015 Institute of Electrical and Electronics Engineers Inc., 2015. Sivut 56-65

Bibtex - Lataa

@inproceedings{33c32569525b44d2b41fada678979adb,
title = "Software evolution and time series volatility: An empirical exploration",
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.",
keywords = "ARIMA, Code churn, Conditional variance, FreeBSD, GARCH, Software evolution, Time series analysis, Volatility",
author = "Jukka Ruohonen and Sami Hyrynsalmi and Ville Lepp{\"a}nen",
year = "2015",
month = "8",
day = "30",
doi = "10.1145/2804360.2804367",
language = "English",
volume = "30-Aug-2015",
pages = "56--65",
booktitle = "14th International Workshop on Principles of Software Evolution, IWPSE 2015 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",

}

RIS (suitable for import to EndNote) - Lataa

TY - GEN

T1 - Software evolution and time series volatility

T2 - An empirical exploration

AU - Ruohonen, Jukka

AU - Hyrynsalmi, Sami

AU - Leppänen, Ville

PY - 2015/8/30

Y1 - 2015/8/30

N2 - 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.

AB - 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.

KW - ARIMA

KW - Code churn

KW - Conditional variance

KW - FreeBSD

KW - GARCH

KW - Software evolution

KW - Time series analysis

KW - Volatility

U2 - 10.1145/2804360.2804367

DO - 10.1145/2804360.2804367

M3 - Conference contribution

VL - 30-Aug-2015

SP - 56

EP - 65

BT - 14th International Workshop on Principles of Software Evolution, IWPSE 2015 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

ER -