Abstract
The runtime environment and workload of software are constantly changing, requiring users to make appropriate adjustments to accommodate these changes. The runtime configuration, however, as the interface for users to manipulate software behavior often requires domain-specific knowledge to understand. This usually results in users spending a considerable amount of time wading through document and user manuals trying to understand the runtime configuration. In this paper, we study the possibility of understanding the intention of runtime configuration options through their documents, even sometimes it is difficult for users to understand. Based on these studies, we classify the runtime configuration option's intention into six categories. Accordingly, we design runtime Configuration Intention Classifier (CIC), a supervised approach based on CNN to classify the runtime configuration option's intention according to its document. CIC integrates the features of runtime configuration names and descriptions according to different levels of granularity and predicts the intention of runtime configuration options accordingly. Extensive experiments show that our approach can achieve an accuracy of 85.6% and outperform nine comparative approaches by up to 16.6% over the dataset we customized.
| Original language | English |
|---|---|
| Pages (from-to) | 775-802 |
| Number of pages | 28 |
| Journal | International Journal of Software Engineering and Knowledge Engineering |
| Volume | 31 |
| Issue number | 6 |
| DOIs | |
| State | Published - Jun 2021 |
| Externally published | Yes |
Keywords
- Runtime configuration intention
- deep learning
- text classification
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