Quantifying Software's Impact

Genuchten, Michiel van; Hatton, Les

doi:10.1109/mc.2013.7

Cited by 7 publications

(2 citation statements)

References 18 publications

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“…After previously reported experience with a very small number of datasets, we had decided to characterise growth by using CAGR using endpoints. This is a nontrivial assumption and deserves some detailed justification.…”

Section: Methodsmentioning

confidence: 99%

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

Hatton

Spinellis

Genuchten³

2017

J Software Evolu Process

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The amount of code in evolving software-intensive systems appears to be growing relentlessly, affecting products and entire businesses. Objective figures quantifying the software code growth rate bounds in systems over a large time scale can be used as a reliable predictive basis for the size of software assets. We analyze a reference base of over 404 million lines of open source and closed software systems to provide accurate bounds on source code growth rates. We find that software source code in systems doubles about every 42 months on average, corresponding to a median compound annual growth rate of 1.21 ± 0.01. Software product and development managers can use our findings to bound estimates, to assess the trustworthiness of road maps, to recognise unsustainable growth, to judge the health of a software development project, and to predict a system's hardware footprint. KEYWORDS CAGR, code growth rate, compound annual growth rate, empirical study, software evolution 1 merely noise when we are trying to estimate the actual growth of a source code asset. When applied to the avionic systems above, flight software had a CAGR of 1.18 over a period of 53 years. Robotic space flights had a CAGR of 1.17 between 1975 (Viking, 5000 lines of code) and 2005 J Softw Evol Proc. 2017;29:e1847.wileyonlinelibrary.com/journal/smr

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Section: Methodsmentioning

confidence: 99%

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

Hatton

Spinellis

Genuchten³

2017

J Software Evolu Process

Self Cite

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“…13 The column also featured an overview in 2013. 14 We're optimistic about applying process mining in a meaningful way to determine the value of an upgrade for all 19 products described so far. In some cases, such as car navigation, it could be as straightforward as we described in this article.…”

Section: Reviewing the Listmentioning

confidence: 99%

Is Your Upgrade Worth It? Process Mining Can Tell

et al. 2014

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His research interests include process mining, workfl ow management, discrete event simulation, and healthcare. Contact him at r.s.mans@tue.nl.HAJO REIJERS is a professor of business informatics in the computer science department of VU University Amsterdam. His research and teaching focus on process-aware information systems, business process improvement, and process modeling. Contact him at h.a.reijers@vu.nl. DANIEL WISMEIJER is professor of oral implantology and prosthetic dentistry at ACTA Amsterdam, which he combines with his referral practice. He's chair and head of the Oral Function and Restorative Dentistry section. His research interests include CAD/CAM, digital dentistry, implant surface and bone substitute optimization, peri-implantitis, and the evaluation of different implant-based treatment modalities. Contact him at d.wismeijer@acta.nl. ABOUT THE AUTHORSSubscribe today for the latest in computational science and engineering research, news and analysis, CSE in education, and emerging technologies in the hard sciences.

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