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Deep Understanding of Runtime Configuration Intention

  • Chenglong Zhou
  • , Haoran Liu*
  • , Yuanliang Zhang
  • , Zhipeng Xue
  • , Qing Liao
  • , Jinjing Zhao
  • , Ji Wang
  • *Corresponding author for this work
  • National University of Defense Technology
  • Harbin Institute of Technology Shenzhen
  • National Key Laboratory of Science and Technology on Information System

Research output: Contribution to journalArticlepeer-review

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 languageEnglish
Pages (from-to)775-802
Number of pages28
JournalInternational Journal of Software Engineering and Knowledge Engineering
Volume31
Issue number6
DOIs
StatePublished - Jun 2021
Externally publishedYes

Keywords

  • Runtime configuration intention
  • deep learning
  • text classification

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