Event-Based Monitoring of Open Source Software Projects (original) (raw)
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2010
Abstract—Many open source software (OSS) development projects use tools and models that come from heterogeneous sources. A project manager, who wants to analyze indicators for the state of the project based on these data sources, faces the challenge of how to link semi-structured information on common concepts across heterogeneous data sources, eg, source code versions, mailing list entries, and bug reports. Unfortunately, manual analysis is costly, error-prone, and often yields results late for decision making.
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Trends in Information Management, 2012
Purpose: This paper attempts to present a set of basic metrics which can be used to measure basic development processes in an OSS environment. Design/Methodology/Approach: Reviewing the earlier literature helped in exploring the metrics for measuring the development processes in OSS environment. Results: The OSSD is different from traditional software development because of its open development environment. The development processes are different and the measures required to assess them have to be different.
OSSMETER: a software measurement platform for automatically analysing open source software projects
Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering, 2015
Deciding whether an open source software (OSS) project meets the required standards for adoption in terms of quality, maturity, activity of development and user support is not a straightforward process as it involves exploring various sources of information. Such sources include OSS source code repositories, communication channels such as newsgroups, forums, and mailing lists, as well as issue tracking systems. OSSMETER is an extensible and scalable platform that can monitor and incrementally analyse a large number of OSS projects. The results of this analysis can be used to assess various aspects of OSS projects, and to directly compare different OSS projects with each other.
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International Journal of Web Information Systems, 2007
initiatives and the increasing demand of OSS products as alternative solutions by industries, it is important for particular stakeholders such as the project host/supporter (e.g., Apache Foundation, Sourceforge), project leading teams, and prospective customers to determine whether a (new) project initiative is likely to sustain and worthwhile to support. From a software project management point of view, a typical web-based OSS project can be viewed as a web engineering process, since most OSS projects exploit the benefits of a web platform and enable the project community to collaborate using web-based project tools and repositories such as mailing lists, bug trackers, and versioning systems (CVS/SVN) to deliver web systems and applications. These repositories can provide rich collections of process data, and artifacts which can be analyzed to better understand the project status. This paper proposes a concept of "health" indicators and an evaluation process that can help to get a status overview of OSS projects in a timely fashion and predict project survivability based on the project data available on web repositories. For initial empirical evaluation of the concept, we apply the indicators to well known web-based OSS projects (Apache Tomcat and Apache HTTP Server) and compare the results with challenged projects (Apache Xindice and Apache Slide). We discuss the results with OSS experts to investigate the external validity of the indicators.
2013
Based on the empirical evidence that the ratio of email messages in public mailing lists to versioning system commits has remained relatively constant along the history of the Apache Software Foundation (ASF), this paper has as goal to study what can be inferred from such a metric for projects of the ASF. We have found that the metric seems to be an intensive metric as it is independent of the size of the project, its activity, or the number of developers, and remains relatively independent of the technology or functional area of the project. Our analysis provides evidence that the metric is related to the technical effervescence and popularity of project, and as such can be a good candidate to measure its healthy evolution. Other, similar metrics -like the ratio of developer messages to commits and the ratio of issue tracker messages to commits- are studied for several projects as well, in order to see if they have similar characteristics
A Metric Based Approach for Analysis of Software Development Processes in Open Source Environment
American Journal of Software Engineering and Applications, 2013
Open source software (OSS) is a software program whose source code is available to anyone under a license which gives them freedom to run the program, to study, modify and redistribute the copies of original or modified program. Its objective is to encourage the involvement in the form of improvement, modification and distribution of the licensed work. OSS proved itself highly suited, both as a software product and as a development methodology. The main challenge in the open source software development (OSSD) is to collect and extract data. This paper presents various aspects of open source software community, role of different types of users as well as developers. A metric-based approach for analysis of software development processes in open source environment is suggested and validated through a case study by studying the various development processes undertaken by developers for about fifty different open -source software's.
Data Mining Project History in Open Source Software Communities
2000
Understanding the Open Source Software (OSS) movement came into focus for many researchers due to the recent fast expansion of OSS communities. SourceForge, which is the data source of this research, is one of the biggest OSS communities. While most of the existing research about OSS communities is focused on the community itself, our research is focused on one of the components in the community -the project. With a full database dump from SourceForge, we are able to represent the project history using monthly statistics. Our goal is to find the critical statistical features for the history of the individual projects. Thus based on these features we can generate the rules to predict the future of a project. In this paper, we present the method in three steps. First, we prepare the data for data mining, which include data formatting and feature extraction. Then, we use the non-negative matrix factorization (NMF) method to select independent significant features. In particular, we reduce the size of the feature set from 63 to 10. By using the feature selection method, we can improve the performance of the following step dramatically without sacrificing accuracy. Finally, we use data mining techniques (clustering and summarization) to find the rules and relative features. This research uses the Oracle Data Mining toolkit. By using the method described in this paper, we are able to predict the future of a project with up to 78% confidence.
Observations on patterns of development in open source software projects
Proceedings of the fifth workshop on Open source software engineering - 5-WOSSE, 2005
This paper discusses a project aimed at understanding how open source software evolves by examining patterns of development and changes in releases over time. The methodological approach of the research and initial observations are described. These include descriptions of release cycles and categorization of projects based on the overall changes in size and complexity exhibited across releases. Implications of these observations are discussed in light of prior and future work on understanding OSS evolution.
Observations on Patterns of Development in Open Source Software Projects 1
2012
This paper discusses a project aimed at understanding how open source software evolves by examining patterns of development and changes in releases over time. The methodological approach of the research and initial observations are described. These include descriptions of release cycles and categorization of projects based on the overall changes in size and complexity exhibited across releases. Implications of these observations are discussed in light of prior and future work on understanding OSS evolution.