Factors that Contribute to Open Source Software Project Success

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    Factors that Contribute to Open Source Software Project Success - Presentation Transcript

    1. Factors that contribute to open source software project success Rizwan Ur Rehman Telecommunications Technology Management Program February 13, 2006
    2. 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
    3. Relevance Company managers and entrepreneurs who wish to set up OSS projects To reduce the cost of having to change an OSS component due to the failure of OSS project Project managers who wish to incorporate OSS into their development projects To avoid costly mistakes and reduce the risk of failure Why? Who is interested?
    4. Literature review Number and experience of software developers, targeted users of OSS, software type, license type, OSS project success Development team, target market, product type, product success Factors 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) Open source software development 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) Product development References Literature
    5. 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
    6. 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
    7. 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.
    8. 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
    9. Variable measurement Total years of experience of developers taking part in the development of OSS project Experience of developers Categorical variable measured on nominal scale with values: 1 = developers, 2 = system administrators, 3 = end-users Target users 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) Number of developers taking part in the development of OSS project Measurement Variable Programming language type Number of developers
    10. Variable measurement (cont’d) Categorical variable measured on nominal scale with values: 1 = application software, 2 = application development and deployment tools, 3 = system infrastructure software Type of software Total number of releases from the start of the OSS project to the date of data collection Number of releases Total number of downloads from the start of the OSS project to the date of data collection Number of downloads Categorical variable measured on nominal scale with values: 1 = very restrictive licenses, 2 = moderately restrictive licenses, 3 = non-restrictive licenses Measurement Variable Type of license
    11. Data analysis Multivariate General Linear Model Test for Hypotheses 1 to 6 One-Way ANOVA and Bonferroni Test for Hypotheses 4a, 4b, 6a, 6b Welch and Brown-Forsythe robust F and Tamhane T2 Tests for Hypotheses 3a, 3b, 5a, 5b Levene test of equality of variance Test for Hypotheses 3a, 3b, 4a, 4b, 5a, 5b, 6a, 6b Pearson correlation Test for Hypotheses 1a, 1b, 2a, 2b Histograms with normality curve, descriptive statistics and natural log transformations Descriptive
    12. Pearson correlation for Hypothesis 1
      • *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
      .600(***) .000 .606(***) .000 Number of developers (LN) Number of releases (LN) Number of downloads (LN)
    13. Pearson correlation for Hypothesis 2
      • *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
      .572(***) .000 .609(***) .000 Experience of developers (LN) Number of releases (LN) Number of downloads (LN)
    14. 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
      339.11 2 11.366(***) .000 Brown-Forsythe 229.655 2 12.157(***) .000 Welch Number of downloads (LN) df2 df1 Statistic
    15. 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
      341.452 2 20.169(***) .000 Brown-Forsythe 227.575 2 20.812(***) .000 Welch Number of releases (LN) df2 df1 Statistic
    16. 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
      349 3070.948 Total 8.824 348 3070.641 Within groups .035 .852 .306 1 .306 Between groups Number of downloads (LN) F Mean square df Sum of squares
    17. 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
      349 596.464 Total 1.711 348 595.261 Within groups .703 .402 1.203 1 1.203 Between groups Number of releases (LN) F Mean square df Sum of squares
    18. 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
      340.009 2 14.336(***) .000 Brown-Forsythe 230.826 2 14.526(***) .000 Welch Number of downloads (LN) df2 df1 Statistics
    19. 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
      • Results support Hypothesis 5b
      344.207 2 25.553(***) .000 Brown-Forsythe 229.869 2 26.720(***) .000 Welch Number of releases (LN) df2 df1 Statistics
    20. 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
      349 3070.948 Total 8.703 347 3020.032 Within groups 2.925(*) .055 25.458 2 50.915 Between groups Number of downloads (LN) F Mean square df Sum of squares
    21. 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
      • Results support Hypothesis 6b
      349 596.464 Total 1.643 347 570.134 Within groups 8.013(***) .000 13.165 2 26.330 Between groups Number of releases (LN) F Mean square df Sum of squares
    22. Multivariate general linear model .983 8.44(***) .000 .052 Hotelling’s trace .983 8.44(***) .000 .950 Wilk’s lambda .983 8.44(***) .000 .052 Roy’s largest root .983 8.44(***) .000 .050 Pillai’s trace Number of developers (LN) Observed power F Value Effect
    23. Multivariate general linear model (cont’d) .906 5.38(***) .005 .033 Hotelling’s trace .906 5.38(***) .005 .968 Wilk’s lambda .906 5.38(***) .005 .033 Roy’s largest root .906 5.38(***) .005 .032 Pillai’s trace Experience of developers (LN) Observed power F Value Effect
    24. Multivariate general linear model (cont’d) .879 3.02(**) .017 .038 Hotelling’s trace .878 3.02(**) .018 .964 Wilk’s lambda .903 5.32(***) .005 .033 Roy’s largest root .877 3.01(**) .018 .037 Pillai’s trace Target users type Observed power F Value Effect
    25. Multivariate general linear model (cont’d) .169 .299 (.742) .002 Hotelling’s trace .169 .299 (.742) .998 Wilk’s lambda .169 .299 (.742) .002 Roy’s largest root .169 .299 (.742) .002 Pillai’s trace Programming language type Observed power F Value Effect
    26. Multivariate general linear model (cont’d) .929 3.62(***) .006 .045 Hotelling’s trace .930 3.63(***) .006 .956 Wilk’s lambda .825 4.15(**) .017 .026 Roy’s largest root .931 3.64(***) .006 .044 Pillai’s trace Software type Observed power F Value Effect
    27. Multivariate general linear model (cont’d) .299 .586 (.673) .007 Hotelling’s trace .300 .587 (.672) .993 Wilk’s lambda .363 1.134 (.323) .007 Roy’s largest root .300 .588 (.671) .007 Pillai’s trace Type of license Observed power F Value Effect
    28. Test results supported Targeting developers as users is positively associated with the number of releases of OSS projects Hypothesis 3b Not supported Using a commonly used programming language is positively associated with the number of downloads of OSS projects Hypothesis 4a Outcome Hypothesis supported Targeting developers as users is positively associated with the number of downloads of OSS projects Hypothesis 3a supported Experience of developers is positively associated with the number of releases of OSS projects Hypothesis 2b supported Experience of developers is positively associated with the number of downloads of OSS projects Hypothesis 2a supported Number of developers is positively associated with the number of releases of OSS projects Hypothesis 1b supported Number of developers is positively associated with the number of downloads of OSS projects Hypothesis 1a
    29. Test results Outcome Hypothesis Not supported Use of non-restrictive OSS license is positively associated with the number of releases of OSS projects Hypothesis 6b Not supported Use of non-restrictive OSS license is positively associated with the number of downloads of OSS projects Hypothesis 6a supported Development of application development and deployment tools is positively associated with the number of releases of OSS projects Hypothesis 5b supported Development of application development and deployment tools is positively associated with the number of downloads of OSS projects Hypothesis 5a Not supported Using a commonly used programming language is positively associated with the number of releases of OSS projects Hypothesis 4b
    30. 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
    31. 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
    32. 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

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