What question did this study set out to answer?

Quantify empirical bounds on long-term source code growth rates across software systems to provide a predictive baseline for software asset sizing.

February 16, 2017Journal of Software Evolution and Process

The long‐term growth rate of evolving software: Empirical results and implications

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Authors

LHLes HattonKingston University DSDiomidis SpinellisAthens University of Economics and Business MGMichiel van GenuchtenTIAS School for Business and Society

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Implication

Empirical analysis reveals source code doubles every 42 months across open and closed systems, suggesting critical benchmarks for planning software roadmaps and resource needs.

Key Points

Quantify empirical bounds on long-term source code growth rates across software systems to provide a predictive baseline for software asset sizing.
Analyzed a reference base encompassing over 404 million lines of code from open source and closed software systems.
Evaluated historical longitudinal changes to model software code growth trends and compound growth trajectories.
Software source code doubles approximately every 42 months on average across evaluated systems.
The median compound annual growth rate of the analyzed software systems is 1.21 ± 0.01.

Cite This Study

Hatton et al. (2017) studied this question.

synapsesocial.com/papers/6a0887d0113ba5b476de3ff1 https://doi.org/10.1002/smr.1847

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Implication

Empirical analysis reveals source code doubles every 42 months across open and closed systems, suggesting critical benchmarks for planning software roadmaps and resource needs.

The long‐term growth rate of evolving software: Empirical results and implications

Discussion

Member takes

Implication

Key Points

Cite This Study

The long‐term growth rate of evolving software: Empirical results and implications

Discussion

Member takes

Implication

Key Points

Cite This Study

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