Name
Abstract | ||
|---|---|---|
Methods for predicting issue lifetime can help software project managers to prioritize issues and allocate resources accordingly. Previous studies on issue lifetime prediction have focused on models built from static features, meaning features calculated at one snapshot of the issue's lifetime based on data associated to the issue itself. However, during its lifetime, an issue typically receives comments from various stakeholders, which may carry valuable insights into its perceived priority and difficulty and may thus be exploited to update lifetime predictions. Moreover, the lifetime of an issue depends not only on characteristics of the issue itself, but also on the state of the project as a whole. Hence, issue lifetime prediction may benefit from taking into account features capturing the issue's context (contextual features). In this work, we analyze issues from more than 4000 GitHub projects and build models to predict, at different points in an issue's lifetime, whether or not the issue will close within a given calendric period, by combining static, dynamic and contextual features. The results show that dynamic and contextual features complement the predictive power of static ones, particularly for long-term predictions.
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Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2901739.2901751 | MSR |
Keywords | Field | DocType |
issue lifetime prediction, issue tracking, mining software repositories | Data science,Data mining,Data modeling,Predictive power,Computer science,Software bug,Feature extraction,Context model,Software,Snapshot (computer storage),Mining software repositories | Conference |
ISBN | Citations | PageRank |
978-1-4503-4186-8 | 9 | 0.48 |
References | Authors | |
29 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Riivo Kikas | 1 | 51 | 4.19 |
| Marlon Dumas | 2 | 25 | 2.54 |
| Dietmar Pfahl | 3 | 1078 | 106.14 |