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What Temporal Analysis Of Contribution Pattern Says About Stability Of Software Maintenance

588 words - 3 pages

In this section, we state and justify the choices we made in the study, its influence and also discuss the future scope of this work. In this paper, we present a case study on one real-world, large, popular and open source project namely Google Chromium Project. Google Chromium has reportedly observed an increase in popularity\footnote{} (with respect to other similar projects) from 2009 to 2012 that is the duration of the study. This increase in popularity is also evident from the increase in issue count reported for the project. The insights present in the study brings out the trends in popular project, however due to limited space availability we do not bring out the contrast by comparison with projects that heads towards instability or looses its contributors in time. However, the research questions and observed trends presented on Google Chromium project sets benchmark for comparisons and ...view middle of the document...

The results conducted on one repository namely ITS are encouraging and in future we plan to extend the idea by integrating multiple software repositories. The integrated repositories will capture a complete statistical picture to forecast the impact of contributor participation patterns on stability of project.

In FLOSS projects contributor participation follows Pareto Distribution that is 20\% of contributors do 80\% of work. Moreover, not all roles are equally relevant. Thus one may argue that it is the contribution pattern of core participants and not all contributors that matters for the project. However, success of FLOSS projects is driven by masses and not individuals where each contributor has a unique role to serve. For instance, approximately 70\% of contributors in FLOSS projects are one time contributors. However, their presence ensures popularity and interest in project, and is appreciated. So if contributors cease to file issues it is an indicator of decreasing popularity and influences the age of project. With decreasing contribution the project diminishes and slowly dies out.

In this study we assume absence of activities in three months as an indicator of contributors leaving the project. This assumption is localized. For
instance, an owner who stop participating for three months indicates that the contributor has left the project while a reporter may continue participation even after a year. However, for planning this assumption holds as it indicates the trend of participation. Also the data available in ITS may not answer all RQs that are of relevance to decision makers. However, it gives justifiable understanding of stability of the project in a data-driven and objective manner.

Another extension of this work will be to study environmental factors that influences stability of project by examining contributor participation patterns. We present study on trends of participation, however inclusion of factors like increasing popularity or market share of similar projects seems to bring promising insights on stability of project.

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