Bibliographic Details
| Title: |
Wikifying software artifacts. |
| Authors: |
Nassif, Mathieu1 (AUTHOR) mnassif@cs.mcgill.ca, Robillard, Martin P.1 (AUTHOR) |
| Source: |
Empirical Software Engineering. Mar2021, Vol. 26 Issue 2, p1-42. 42p. |
| Abstract: |
Context: The computational linguistics community has developed tools, called wikifiers, to identify links to Wikipedia articles from free-form text. Software engineering research can leverage wikifiers to add semantic information to software artifacts. However, no empirically-grounded basis exists to choose an effective wikifier and to configure it for the software domain, on which wikifiers were not specifically trained. Objective: We conducted a study to guide the selection of a wikifier and its configuration for applications in the software domain, and to measure what performance can be expected of wikifiers. Method: We applied six wikifiers, with multiple configurations, to a sample of 500 Stack Overflow posts. We manually annotated the 41 124 articles identified by the wikifiers as correct or not to compare their precision and recall. Results: Each wikifier, in turn, achieved the highest precision, between 13% and 82%, for different thresholds of recall, from 60% to 5%. However, filtering the wikifiers’ output with a whitelist can considerably improve the precision above 79% for recall up to 30%, and above 47% for recall up to 60%. Conclusions: Results reported in each wikifier’s original article cannot be generalized to software-specific documents. Given that no wikifier performs universally better than all others, we provide empirically grounded insights to select a wikifier for different scenarios, and suggest ways to further improve their performance for the software domain. [ABSTRACT FROM AUTHOR] |
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| Database: |
Engineering Source |
