OSS 2009: What does it take to develop a million lines of code?

8/8/2019 OSS 2009: What does it take to develop a million lines of code?

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IntroductionApproach

ResultsConclusions and Further Work

What Does it Take to Develop a Million Lines ofOSS?

5th Conference on Open Source Systems, Skovde, Sweden.

J. Fernandez-Ramil, D. Izquierdo-Cortazar, Tom Mens

[email protected], [email protected], [email protected] Open University, U.K., Universidad Rey Juan Carlos, Spain,

University of Mons, Belgium

June 5, 2009

J. Fernandez-Ramil, D. Izquierdo-Cortazar, Tom Mens What Does it Take to Develop a Million Lines of OSS?
http://find/http://goback/


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(cc) 2009 Juan Fernandez-Ramil, Daniel Izquierdo-Cortazar, Tom Mens.Some rights reserved. This document is distributed under the Creative

Commons Attribution-ShareAlike 2.5 licence, available inhttp://creativecommons.org/licenses/by-sa/2.5/

J. Fernandez-Ramil, D. Izquierdo-Cortazar, T. Mens What Does it Take to Develop a Million Lines of OSS?


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Table of contents

1 Introduction

2 Approach

3 Results

4 Conclusions and Further Work



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Index

1 Introduction

2 Approach

3 Results



I d i


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Introduction

For more than 30 years, software cost estimation andproductivity measurement has been a research topic.

Most studies based on data from proprietary software.

However there are few studies looking at OSS productivity.We are aware of only two.

Our study is preliminary and exploratory, hoping that betterstudies will follow.


I t d ti


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Motivation

New datasets provided by FLOSSMetrics(http://flossmetrics.org) make possible to analyse large

quantities of data.Most of the productivity and effort studies are based onnon-traceable datasets.

FLOSS projects are a great opportunity to compare those

proprietary-based effort models with the reality of FLOSSworld.


Introduction
http://flossmetrics.org/http://flossmetrics.org/http://find/http://goback/


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Research Question

What does it take to develop a million lines of open source code?

Relationships between, firstly, size and...

...secondly, effort, duration and team size.


Introduction


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Index

1 Introduction

2 Approach

3 Results




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Introduction


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FLOSS Systems Studied and Characteristics

Project PL Desc. Years

Blender C/C++ 3D animation 6Eclipse Java IDE 6.9

FPC Pascal Compiler 3.4GCC C/Java/Ada GNU compiler 19.9GCL C/Lisp/ASM GNU Lisp 8.8GDB C/C++ GNU debug. 9.5

GIMP C Image manipulation 10.9

GNUBinUtils C/C++ Binary tools 9.4NCBITools C/C++ Libs. for biology apps. 15.4WireShark C Network analyser 10

XEmacs Lisp/C App. dev. system 12.2


Introduction


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ApproachResults

Conclusions and Further Work

Measurements

KLOC - Physical lines of source code (SLOCCount)

FILES - Total number of files in code repository (CVSAnalY)

EFFORT - Effort in Contributor-years (CVSAnalY)

DUR - Time Length of active evolution in years (CVSAnalY)

DEV - Number of distinct contributors (CVSAnalY)


Introduction


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ApproachResults


COCOMO model

Basic COCOMO 81 modelFor comparisons we used the default COCOMO 81 values asset in the SLOCCount tool.


Introduction


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ApproachResults


Index

1 Introduction

2 Approach

3 Results



Introduction


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ApproachResults


Growth Models

We found a very good fit to linear, quadratic or exponentialmodels with R2 values ranging from 0.98 to 0.99.

Five follow linear models.Three follow sublinear (quadratic) models

Three follow superlinear models (2 quadratic and 1exponential)

However this trends may be use carefully and they are usablejust for specific estimation model for the given project.


IntroductionA h


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ApproachResults


Growth Models

Table: Best fit models for monthly growth trends in netFILES

system best growth trend model R2

Blender linear 0.99

Eclipse linear 0.99FPC linear 0.98GCC superlinear 0.99GCL sublinear 0.98GDB linear 0.99

GIMP sublinear 0.99GNUBinUtils linear 0.99

NCBITools superlinear 0.98WireShark superlinear 0.99

XEmacs sublinear 0.98


IntroductionA h


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ApproachResults


Comparison FLOSS data vs COCOMO model

10 ouf of 11 projects display EFFORT lower than predicted byCOCOMO.

The smallest project (GIMP) is close to COCOMO prediction.

8 out of 11 projects display higher values of DUR thanCOCOMO predictions.

6 out of 11 projects display higher values of DEV than

COCOMO predictions.




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ApproachResults


Comparison FLOSS data vs COCOMO model

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ApproachResults


Index

1 Introduction

2 Approach

3 Results





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ApproachResults


Conclusions

The comparison between data from 11 FLOSS projects andthe COCOMO model suggests that generic FLOSSproductivity and effort is needed.

Better models may be obtained by means of refinement andimprovement of the approach.

FLOSS projects with linear or superlinear growth seem to defythat the growth rate remains stable or decreases due to

excessive accumulated complexity.Thus, if several cost models may be suitable for linearly,superlinearly or sublinearly projects is an open question.




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ApproachResults


Further Research

Study additional number of projects (COCOMO is based on

at least 61)Regression models are too simple and do not reflect anytheory about FLOSS

Measure further attributes may increase the accuracy (e.g.:just measure source code files).




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ppResults


Thanks for your attention

Thanks for your attention!


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OSS 2009: What does it take to develop a million lines of code?