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Time series trends in software evolution

Tutkimustuotosvertaisarvioitu

Standard

Time series trends in software evolution. / Ruohonen, Jukka; Hyrynsalmi, Sami; Leppänen, Ville.

julkaisussa: Journal of Software: Evolution and Process, Vuosikerta 27, Nro 12, 01.12.2015, s. 990-1015.

Tutkimustuotosvertaisarvioitu

Harvard

Ruohonen, J, Hyrynsalmi, S & Leppänen, V 2015, 'Time series trends in software evolution', Journal of Software: Evolution and Process, Vuosikerta. 27, Nro 12, Sivut 990-1015. https://doi.org/10.1002/smr.1755

APA

Ruohonen, J., Hyrynsalmi, S., & Leppänen, V. (2015). Time series trends in software evolution. Journal of Software: Evolution and Process, 27(12), 990-1015. https://doi.org/10.1002/smr.1755

Vancouver

Ruohonen J, Hyrynsalmi S, Leppänen V. Time series trends in software evolution. Journal of Software: Evolution and Process. 2015 joulu 1;27(12):990-1015. https://doi.org/10.1002/smr.1755

Author

Ruohonen, Jukka ; Hyrynsalmi, Sami ; Leppänen, Ville. / Time series trends in software evolution. Julkaisussa: Journal of Software: Evolution and Process. 2015 ; Vuosikerta 27, Nro 12. Sivut 990-1015.

Bibtex - Lataa

@article{4d18b09276054911bd83b49d2f047f6b,
title = "Time series trends in software evolution",
abstract = "Background The laws of software evolution were formulated to describe time series trends in software over time. Objective Building on econometrics, the paper relates the laws theoretically to the concept of stationarity. The theoretical argumentation builds on the fact that in a stationary time series, the mean and variance remain constant. The concept is further elaborated with different statistical types of time series trends. These provide the objective for the empirical experiment that evaluates whether software size measures in a typical software evolution dataset are stationary. Method The time series analysis is based on conventional statistical tests for the evaluation of stationarity. Results The empirical dataset contains time series extracted from the version control systems used in Vaadin and Tomcat between circa 2006 and 2013. The results establish that the observed time series are neither stationary nor follow simple mathematical functions that would translate into stationarity. Conclusion The testing framework presented in the paper allows evaluating the stationarity of software evolution time series. The results can be interpreted theoretically against the laws of software evolution. These methodological and theoretical contributions improve the foundations of predictive time series modeling of software evolution problems.",
keywords = "dynamic regression, software evolution, stationarity, time series analysis, unit roots",
author = "Jukka Ruohonen and Sami Hyrynsalmi and Ville Lepp{\"a}nen",
year = "2015",
month = "12",
day = "1",
doi = "10.1002/smr.1755",
language = "English",
volume = "27",
pages = "990--1015",
journal = "Journal of Software: Evolution and Process",
issn = "2047-7473",
number = "12",

}

RIS (suitable for import to EndNote) - Lataa

TY - JOUR

T1 - Time series trends in software evolution

AU - Ruohonen, Jukka

AU - Hyrynsalmi, Sami

AU - Leppänen, Ville

PY - 2015/12/1

Y1 - 2015/12/1

N2 - Background The laws of software evolution were formulated to describe time series trends in software over time. Objective Building on econometrics, the paper relates the laws theoretically to the concept of stationarity. The theoretical argumentation builds on the fact that in a stationary time series, the mean and variance remain constant. The concept is further elaborated with different statistical types of time series trends. These provide the objective for the empirical experiment that evaluates whether software size measures in a typical software evolution dataset are stationary. Method The time series analysis is based on conventional statistical tests for the evaluation of stationarity. Results The empirical dataset contains time series extracted from the version control systems used in Vaadin and Tomcat between circa 2006 and 2013. The results establish that the observed time series are neither stationary nor follow simple mathematical functions that would translate into stationarity. Conclusion The testing framework presented in the paper allows evaluating the stationarity of software evolution time series. The results can be interpreted theoretically against the laws of software evolution. These methodological and theoretical contributions improve the foundations of predictive time series modeling of software evolution problems.

AB - Background The laws of software evolution were formulated to describe time series trends in software over time. Objective Building on econometrics, the paper relates the laws theoretically to the concept of stationarity. The theoretical argumentation builds on the fact that in a stationary time series, the mean and variance remain constant. The concept is further elaborated with different statistical types of time series trends. These provide the objective for the empirical experiment that evaluates whether software size measures in a typical software evolution dataset are stationary. Method The time series analysis is based on conventional statistical tests for the evaluation of stationarity. Results The empirical dataset contains time series extracted from the version control systems used in Vaadin and Tomcat between circa 2006 and 2013. The results establish that the observed time series are neither stationary nor follow simple mathematical functions that would translate into stationarity. Conclusion The testing framework presented in the paper allows evaluating the stationarity of software evolution time series. The results can be interpreted theoretically against the laws of software evolution. These methodological and theoretical contributions improve the foundations of predictive time series modeling of software evolution problems.

KW - dynamic regression

KW - software evolution

KW - stationarity

KW - time series analysis

KW - unit roots

U2 - 10.1002/smr.1755

DO - 10.1002/smr.1755

M3 - Article

VL - 27

SP - 990

EP - 1015

JO - Journal of Software: Evolution and Process

JF - Journal of Software: Evolution and Process

SN - 2047-7473

IS - 12

ER -