| Publisher | University of California | ||
|---|---|---|---|
| Format | 111.0KB PDF | Date added | 17 Apr 2004 |
| Topics | Data Mining - Analysis, Linux - Open Source | ||
| Downloads | 41 | ||
Software process discovery has historically been an intensive task, either done through exhaustive empirical studies or in an automated fashion using techniques such as logging and analysis of command shell operations. While empirical studies have been fruitful, data collection has proven to be tedious and time consuming. Existing automated approaches have expedited collection of fine-grained data, but do so at the cost of impinging on the developer's work environment, few of who may be observed. This paper explores techniques for discovering development processes from publicly available open source software development repositories that exploit advances in artificial intelligence.
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