Determinants of open source software project success: A longitudinal study

Subramaniam, Chandrasekar; Sen, Ravi; Nelson, Matthew L.

doi:10.1016/j.dss.2008.10.005

Cited by 186 publications

(255 citation statements)

References 23 publications

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“…Recent research on OSS success factors has focused on enlarging the scope of influences, common elements found cite factors such as user/developer interest [2], the critical number of active developers [8] and software quality [5]. Ever since the publication of the Information Systems Success model by Delone and Mclean [1] researchers have attempted to define in what way factors that influence Open Source Software differ from those of commercial software.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Previous research has focused on using three well known metrics to determine project success with the added benefit they have corresponding metrics on SourceForge [2,3].…”

Section: Success Factorsmentioning

confidence: 99%

“…They propose that the structure of the OSS community determines the health of the community and state that an onion structure is one of the better OSS community structures. Subramanian et al [2] measure an Open Source project's success by measuring user interest, project interest and developer interest. They measure user interest by calculating the number of project downloads and to measure the developer interest in the project, Subramanian et al [2] count the number of active developers in the project.…”

Section: Open Source Healthmentioning

confidence: 99%

“…They measure user interest by calculating the number of project downloads and to measure the developer interest in the project, Subramanian et al [2] count the number of active developers in the project. Finally, they measure project activity by calculating the number of files released in the project [2].…”

Section: Open Source Healthmentioning

confidence: 99%

“…Attempts at remedying this gap have focused on statistical models that focus on certain aspects of a software development lifecycle. Only recently, has historical data been used to determine the changing nature of factors for success during a projects lifecycle [2]. In this paper we use machine learning in the form of decision trees, to predict the development stage of an Open Source project based on project metrics 1 , project constraints and circumstance.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

How Healthy Is My Project? Open Source Project Attributes as Indicators of Success

Piggot

Amrit

2013

Open Source Software: Quality Verification

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Abstract. Determining what factors can influence the successful outcome of a software project has been labeled by many scholars and software engineers as a difficult problem. In this paper we use machine learning to create a model that can determine the stage a software project has obtained with some accuracy. Our model uses 8 Open Source project metrics to determine the stage a project is in. We validate our model using two performance measures; the exact success rate of classifying an Open Source Software project and the success rate over an interval of one stage of its actual performance using different scales of our dependent variable. In all cases we obtain an accuracy of above 70% with one away classification (a classification which is away by one) and about 40% accuracy with an exact classification. We also determine the factors (according to one classifier) that uses only eight variables among all the variables available in SourceForge, that determine the health of an OSS project. IntroductionDetermining what makes a software project successful has been a research topic for well over 20 years. The first model that defined the factors influencing software success was published in 1992 by Delone and McLean [1], as the Information Systems Success Model. Since then there has been a considerable effort in research to determine what can be done to minimize project failure. However, factors that influence commercial projects differ from those known as FLOSS or Free/Libre Open Source Software. Attempts at remedying this gap have focused on statistical models that focus on certain aspects of a software development lifecycle. Only recently, has historical data been used to determine the changing nature of factors for success during a projects lifecycle [2]. In this paper we use machine learning in the form of decision trees, to predict the development stage of an Open Source project based on project metrics 1 , project constraints and circumstance. This model will serve as an indicator of OSS project health that will enable developers to determine accurately in what stage their 1 We use the terms metric and attribute to mean the same concept in this paper.

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Section: Literature Reviewmentioning

confidence: 99%

“…Previous research has focused on using three well known metrics to determine project success with the added benefit they have corresponding metrics on SourceForge [2,3].…”

Section: Success Factorsmentioning

confidence: 99%

Section: Open Source Healthmentioning

confidence: 99%

Section: Open Source Healthmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

How Healthy Is My Project? Open Source Project Attributes as Indicators of Success

Piggot

Amrit

2013

Open Source Software: Quality Verification

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Differential impacts of social influence on initial and sustained participation in open source software projects

Yang

et al. 2021

Asso for Info Science & Tech

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Social networking tools and visible information about developer activities on open source software (OSS) development platforms can leverage developers' social influence to attract more participation from their peers. However, the differential impacts of such social influence on developers' initial and sustained participation behaviors were largely overlooked in previous research. We empirically studied the impacts of two social influence mechanisms—word‐of‐mouth (WOM) and observational learning (OL)—on these two types of participation, using data collected from a large OSS development platform called Open Hub. We found that action (OL) speaks louder than words (WOM) with regard to sustained participation. Moreover, project age positively moderates the impacts of social influence on both types of participation. For projects with a higher average workload, the impacts of OL are reduced on initial participation but are increased on sustained participation. Our study provides a better understanding of how social influence affects OSS developers' participation behaviors. It also offers important practical implications for designing software development platforms that can leverage social influence to attract more initial and sustained participation.

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Joint pricing and openness decisions in software markets with reinforcing loops

Rahmandad

Sibdari

2012

System Dynamics Review

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In software markets, pricing and openness interact to determine profitability through the direct effect of openness on feasible prices and costs, and their impact on different reinforcing feedback loops such as network effects. We develop a simulation model of the inner workings of a software firm, product life cycle and market aggregation, and analyze their interactions in a competitive market. The reinforcing loops increase the value of early market lead and put pressure on the competing firms to seek such advantage. The competitive equilibrium under strong reinforcing loops calls for highly open software products with early discounts that significantly compromise the profitability of the players. Proprietary platforms and higher prices are favored when those mechanisms are weak. We discuss the implications of this analysis for the dynamics of different competitive markets as well as the contributions to the system dynamics literature in analyzing Nash equilibrium of differential games. Copyright © 2012 System Dynamics Society.

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Determinants of open source software project success: A longitudinal study

Cited by 186 publications

References 23 publications

How Healthy Is My Project? Open Source Project Attributes as Indicators of Success

How Healthy Is My Project? Open Source Project Attributes as Indicators of Success

Differential impacts of social influence on initial and sustained participation in open source software projects

Joint pricing and openness decisions in software markets with reinforcing loops

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