Information systems success in free and open source software development: theory and measures

Abstract. This paper presents a novel method and algorithm to measure the size of an open source project's user base and the level of potential user interest that it generates. Previously unavailable download data at a daily resolution confirms hypothesized patterns related to release cycles. In short, regular users rapidly download the software after a new release giving a way to measure the active user base. In contrast, potential new users download the application independently of the release cycle, and the daily download figures tend to plateau at this rate when a release has not been made for some time. An algorithm for estimating these measures from download time series is demonstrated and the measures are examined over time in two open source projects.

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“…While readily available, downloads figures are known to be problematic for a number of reasons [1]. First, the data sources may be unreliable.…”

Section: Discussionmentioning

confidence: 99%

“…Measuring FLOSS project success has been a subject of intense research interest; it is the most common dependent variable in FLOSS research [1]. One of the most desirable measures of external success is a measurement of software use, representing the volume of active users of the software.…”

Section: Introductionmentioning

confidence: 99%

Heartbeat: Measuring Active User Base and Potential User Interest in FLOSS Projects

Wiggins

Howison

Crowston

2009

IFIP Advances in Information and Communication Technology

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“…Consequently, how to mitigate the productivity impact of distributed work has become a key concern in methods literature FLOSSD differs from traditional software engineering in its transparency (Scacchi et al 2006), lack of formal SDMs, PMFs, budgets, schedules or rule structures (Scacchi 2007;Scacchi et al 2006), developer self-assignment of tasks (Crowston et al 2007) and success measures (Crowston et al 2004;Crowston et al 2006). For example, Crowston et al (2006) FLOSSD projects are organized into layers -a small group of core developers, a larger group of ad hoc developers who make minor changes and bug fixes and an even larger group who report problems (Mockus et al 2002). Core developers create extensive but incomplete shared mental models (Scozzi et al 2008).…”

Section: The Scope Of the Design Agentmentioning

confidence: 99%

The Sensemaking-Coevolution-Implementation Theory of software design

Ralph

2015

Science of Computer Programming

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Understanding software design practice is critical to understanding modern information systems development. New developments in empirical software engineering, information systems design science and the interdisciplinary design literature combined with recent advances in process theory and testability have created a situation ripe for innovation. Consequently, this paper utilizes these breakthroughs to formulate a process theory of software design practice: SensemakingCoevolution-Implementation Theory explains how complex software systems are created by collocated software development teams in organizations. It posits that an independent agent (design team) creates a software system by alternating between three activities: organizing their perceptions about the context, mutually refining their understandings of the context and design space, and manifesting their understanding of the design space in a technological artifact. This theory development paper defines and illustrates Sensemaking-Coevolution-Implementation Theory, grounds its concepts and relationships in existing literature, conceptually evaluates the theory and situates it in the broader context of information systems development.

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“…Crowston et al [3] collect data on the bug tracker and the mailing list of the projects to determine the health of the projects. 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.…”

Section: Open Source Healthmentioning

confidence: 99%

“…Previous research has tried to understand which indicators influence a project's success and how these indicators are interrelated but there have been very few working models [3]. What this paper proposes is that, through machine learning we can model which available project metrics are of importance in determining OSS project health.…”

Section: Introductionmentioning

confidence: 99%

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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Information systems success in free and open source software development: theory and measures

Cited by 236 publications

References 29 publications

Heartbeat: Measuring Active User Base and Potential User Interest in FLOSS Projects

Heartbeat: Measuring Active User Base and Potential User Interest in FLOSS Projects

The Sensemaking-Coevolution-Implementation Theory of software design

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

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