Factors that Contribute to Open Source Software Project Success

04/11/23Slide [email protected]

Factors that contribute to open source software project success

Rizwan Ur Rehman

Telecommunications Technology Management ProgramFebruary 13, 2006


Objective

• To examine the factors that affect the success of open source software projects

• Factors examined:– Number of developers – Experience of developers– Target users type – Programming language type– Software type– License type


Relevance

Who is interested? Why?

Company managers and entrepreneurs who wish to set up OSS projects

To avoid costly mistakes and reduce the risk of failure

Project managers who wish to incorporate OSS into their development projects

To reduce the cost of having to change an OSS component due to the failure of OSS project


Literature review

Literature Factors References

Product development

Development team, target market, product type, product success

Brown & Eisenhardt (1995); Caramel & Sawyer (1998); Cooper & Kleinschmidt (1987); Curtis (1981); Curtis et al. (1988); Griffin & Page (1993,1996); Johne & Snelson (1988); Krishnan (1998); Page (1993); Storey & Easingwood (1996); Story et al. (2001); Thomke & von Hippel (2002); Maidique & Zirger (1985); Zirger & Maidique (1990)

Open source software development

Number and experience of software developers, targeted users of OSS, software type, license type, OSS project success

Bates et al. (2002); Bonaccorsi & Rossi (2003); Comino et al. (2005); Crowston et al. (2003, 2004); Crowston & Scozzi (2002); Duijnhouwer & Widdows (2003); Evers (2000); Freshtman & Gandal (2004); Healy & Schussman (2003); Hertel et al. (2003); Koch (2004); Lakhani et al. (2002); Lerner & Tirole (2002, 2005); Nissila (2004); O’Mahony (2003); Paulson et al. (2004); Peng (2004); Raymond (1999);Rossi & Bonaccorsi (2005); Stewart et al. (2005); West & O’Mahony (2005); Zhao (2003)


Lessons learned from literature review

• OSS projects with greater number of experienced developers seem successful

• OSS projects that target user-developers will be more successful

• OSS projects that address needs and solve problems of user-developers will be more successful

• Success of OSS projects seems to depend on the continued contribution of volunteer developers

• Lack of empirical research on OSS projects success


Hypotheses

• Hypothesis 1: Number of developers is positively associated with the success of OSS projects

• Hypothesis 2: Experience of developers is positively associated with the success of OSS projects

• Hypothesis 3: Targeting developers as users is positively associated with the success of OSS projects

• Hypothesis 4: Using a commonly used programming language is positively associated with the success of OSS projects

• Hypothesis 5: Development of application development and deployment tools is positively associated with the success of OSS projects

• Hypothesis 6: Use of non-restrictive OSS licenses is positively associated with the success of OSS projects


Variables

Independent variables

• Number of developers

• Experience of developers

• Target users type

• Programming language type

• Software type

• License type

Dependent variable

• Success*

- Number of downloads

- Number of releases

* 700 developers were asked via email to define success of their OSS projects, 70 replied. The two measures of success used in this research were the ones that had the most number of replies.


Unit of analysis, sample size, and data collection

• Unit of analysis– OSS project

• Sample size– 350 OSS projects; randomly drawn from 100,341 OSS

projects registered on sourceforge.net as of June 20, 2005

• Source of data

– www.sourceforge.net


Variable measurement

Variable Measurement

Number of developers

Number of developers taking part in the development of OSS project

Experience of developers

Total years of experience of developers taking part in the development of OSS project

Target users type Categorical variable measured on nominal scale with values: 1 = developers, 2 = system administrators, 3 = end-users

Programming language type

Categorical variable measured on nominal scale with values: 1 = commonly used programming languages (C, C++, Java, PHP), 2 = others (other than C, C++, Java, PHP)


Variable measurement (cont’d)

Variable Measurement

Type of software Categorical variable measured on nominal scale with values: 1 = application software, 2 = application development and deployment tools, 3 = system infrastructure software

Type of license Categorical variable measured on nominal scale with values: 1 = very restrictive licenses, 2 = moderately restrictive licenses, 3 = non-restrictive licenses

Number of downloads

Total number of downloads from the start of the OSS project to the date of data collection

Number of releases Total number of releases from the start of the OSS project to the date of data collection


Data analysis

Descriptive Histograms with normality curve, descriptive statistics and natural log transformations

Test for Hypotheses 1a, 1b, 2a, 2b

Pearson correlation

Test for Hypotheses 3a, 3b, 4a, 4b, 5a, 5b, 6a, 6b

Levene test of equality of variance

Tests for Hypotheses 3a, 3b, 5a, 5b

Welch and Brown-Forsythe robust F and Tamhane T2

Test for Hypotheses 4a, 4b, 6a, 6b

One-Way ANOVA and Bonferroni

Test for Hypotheses 1 to 6 Multivariate General Linear Model


Pearson correlation for Hypothesis 1

Number of downloads (LN)

Number of releases (LN)


(LN)

.606(***)

.000

.600(***)

.000

*p < 0.1, **p < 0.05, ***p < 0.01

Hypothesis 1: Number of developers is positively associated with the number of downloads and number of releases of OSS projects

Results support Hypothesis 1


Pearson correlation for Hypothesis 2

Number of downloads (LN)

Number of releases (LN)


(LN)

.609(***)

.000

.572(***)

.000

*p < 0.1, **p < 0.05, ***p < 0.01

Hypothesis 2: Experience of developers is positively associated with the number of downloads and number of releases of OSS projects

Results support Hypothesis 2


Welch and Brown-Forsythe tests for Hypothesis 3a

*p < 0.1, **p < 0.05, ***p < 0.01

Hypothesis 3a: Targeting developers as users is positively associated with the number of downloads of OSS projects

Results support Hypothesis 3a

Statistic df1 df2

Number of downloads

(LN)

Welch 12.157(***)

.000

2 229.655

Brown-Forsythe

11.366(***)

.000

2 339.11


Welch and Brown-Forsythe tests for Hypothesis 3b

*p < 0.1, **p < 0.05, ***p < 0.01

Hypothesis 3b: Targeting developers as users is positively associated with the number of releases of OSS projects

Results support Hypothesis 3b

Statistic df1 df2

Number of releases

(LN)

Welch 20.812(***)

.000

2 227.575

Brown-Forsythe

20.169(***)

.000

2 341.452


One-Way ANOVA test for Hypothesis 4a

*p < 0.1, **p < 0.05, ***p < 0.01

Hypothesis 4a: Using a commonly used programming language is positively associated with the number of downloads of OSS projects

Results do not support Hypothesis 4a

Sum of squares

df Mean square

F

Number of

downloads (LN)

Between groups

.306 1 .306 .035

.852

Within groups

3070.641 348 8.824

Total 3070.948 349


One-Way ANOVA test for Hypothesis 4b

*p < 0.1, **p < 0.05, ***p < 0.01

Hypothesis 4b: Using a commonly used programming language is positively associated with the number of releases of OSS projects

Results do not support Hypothesis 4b

Sum of squares

df Mean square

F

Number of

releases (LN)

Between groups

1.203 1 1.203 .703

.402

Within groups

595.261 348 1.711

Total 596.464 349


Welch and Brown-Forsythe tests for Hypothesis 5a

*p < 0.1, **p < 0.05, ***p < 0.01

Hypothesis 5a: Development of application development and deployment tools is positively associated with the number of downloads of OSS projects

Results support Hypothesis 5a

Statistics df1 df2

Number of downloads

(LN)

Welch 14.526(***)

.000

2 230.826

Brown-Forsythe

14.336(***)

.000

2 340.009


Welch and Brown-Forsythe tests for Hypothesis 5b

*p < 0.1, **p < 0.05, ***p < 0.01

Hypothesis 5b: Development of application development and deployment tools is positively associated with the number of releases of OSS projects


Statistics df1 df2

Number of releases

(LN)

Welch 26.720(***)

.000

2 229.869

Brown-Forsythe

25.553(***)

.000

2 344.207


One-Way ANOVA test for Hypothesis 6a

*p < 0.1, **p < 0.05, ***p < 0.01

Hypothesis 6a: Use of non-restrictive OSS license is positively associated with the number of downloads of OSS projects

Results do not support Hypothesis 6a

Sum of squares

df Mean square

F

Number of

downloads (LN)

Between groups

50.915 2 25.458 2.925(*)

.055

Within groups

3020.032 347 8.703

Total 3070.948 349


One-Way ANOVA test for Hypothesis 6b

*p < 0.1, **p < 0.05, ***p < 0.01

Hypothesis 6b: Use of non-restrictive OSS license is positively associated with the number of releases of OSS projects


Sum of squares

df Mean square

F

Number of

releases (LN)

Between groups

26.330 2 13.165 8.013(***)

.000

Within groups

570.134 347 1.643

Total 596.464 349


Multivariate general linear model

Effect Value F Observed power


(LN)

Pillai’s trace

.050 8.44(***)

.000

.983

Wilk’s lambda

.950 8.44(***)

.000

.983

Hotelling’s trace

.052 8.44(***)

.000

.983

Roy’s largest root

.052 8.44(***)

.000

.983


Multivariate general linear model (cont’d)



(LN)

Pillai’s trace

.032 5.38(***)

.005

.906

Wilk’s lambda

.968 5.38(***)

.005

.906

Hotelling’s trace

.033 5.38(***)

.005

.906


.033 5.38(***)

.005

.906




Target users type

Pillai’s trace

.037 3.01(**)

.018

.877

Wilk’s lambda

.964 3.02(**)

.018

.878

Hotelling’s trace

.038 3.02(**)

.017

.879


.033 5.32(***)

.005

.903




Programming language

type

Pillai’s trace

.002 .299

(.742)

.169

Wilk’s lambda

.998 .299

(.742)

.169

Hotelling’s trace

.002 .299

(.742)

.169


.002 .299

(.742)

.169




Software type Pillai’s trace

.044 3.64(***)

.006

.931

Wilk’s lambda

.956 3.63(***)

.006

.930

Hotelling’s trace

.045 3.62(***)

.006

.929


.026 4.15(**)

.017

.825




Type of license

Pillai’s trace

.007 .588

(.671)

.300

Wilk’s lambda

.993 .587

(.672)

.300

Hotelling’s trace

.007 .586

(.673)

.299


.007 1.134

(.323)

.363


Test results

Hypothesis Outcome

Hypothesis 1a Number of developers is positively associated with the number of downloads of OSS projects

supported

Hypothesis 1b Number of developers is positively associated with the number of releases of OSS projects

supported

Hypothesis 2a Experience of developers is positively associated with the number of downloads of OSS projects

supported

Hypothesis 2b Experience of developers is positively associated with the number of releases of OSS projects

supported

Hypothesis 3a Targeting developers as users is positively associated with the number of downloads of OSS projects

supported

Hypothesis 3b Targeting developers as users is positively associated with the number of releases of OSS projects

supported

Hypothesis 4a Using a commonly used programming language is positively associated with the number of downloads of OSS projects

Not supported


Test results

Hypothesis Outcome

Hypothesis 4b Using a commonly used programming language is positively associated with the number of releases of OSS projects

Not supported

Hypothesis 5a Development of application development and deployment tools is positively associated with the number of downloads of OSS projects

supported

Hypothesis 5b Development of application development and deployment tools is positively associated with the number of releases of OSS projects

supported

Hypothesis 6a Use of non-restrictive OSS license is positively associated with the number of downloads of OSS projects

Not supported

Hypothesis 6b Use of non-restrictive OSS license is positively associated with the number of releases of OSS projects

Not supported


Conclusions

• Recommendations to people who setup and operate communities that develop OSS– Set up mechanisms to motivate a large number of experienced

developers to continuously contribute to the OSS project– Target developers as users who will benefit from advancing the

code of the OSS project– Set up software development projects for development of application

development and deployment tools

• Recommendations to project managers of companies planning to incorporate open source into their products– Use OSS project to develop software that solves problems of both

your target customers and target developers– Hire people like developers of OSS projects you wish to have

participate in the OSS community– Target users and customers who look like developers of the OSS

project, i.e., have high software knowledge


Contribution

This research:

• Identifies and examines the factors including number of developers, years of experience of developers, targeting developers as users, and developing application development and deployment tools that contribute to the success of OSS projects. However, using commonly used programming language and a particular type of license does not affect the success of OSS projects

• Indicates that developers who contribute to OSS projects define success in ways not traditionally used to measure the success of software development projects


Limitations and future research

• Limitations– Measures of success are crude and not agreed upon– Operationalization of experience of developers– Only six factors are examined

• Future research– Examine more factors– Examine other success measures– Collect data using questionnaires– Examine effect of the factors contribute to the success

OSS projects for each particular stage of development

Factors that Contribute to Open Source Software Project Success

Technology

Transcript of Factors that Contribute to Open Source Software Project Success