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An Approach for Auto Generation of Labeling Functions for Software Engineering Chatbots

Created by
  • Haebom

Author

Ebube Alor, Ahmad Abdellatif, SayedHassan Khatoonabadi, Emad Shihab

Outline

This paper proposes an automatic labeling function (LF) generation method to solve the difficulties of data labeling process for learning natural language understanding (NLU) platform of software engineering (SE) chatbot. In order to solve the time and resource consumption problems of existing manual labeling method, we present an approach to automatically generate LF by extracting patterns from existing labeled user queries. Experimental results using four SE datasets show that the generated LF achieves up to 85.3% AUC score and up to 27.2% NLU performance improvement. In addition, we confirmed that the number of generated LF affects the labeling performance. This study enables efficient data labeling in the SE chatbot development process, allowing developers to focus on developing core functions.

Takeaways, Limitations

Takeaways:
Presenting an efficient method to reduce data labeling costs and time in the software engineering chatbot development process.
Suggesting the possibility of improving NLU performance through automatic LF generation.
Provides analysis on the impact of the number of generated LFs on labeling performance.
Limitations:
Performance may vary depending on the type and size of the dataset used.
Since the quality of the generated LF directly affects the actual labeling performance, the accuracy and generalization performance of the LF generation algorithm need to be improved.
Need to verify generalization performance for various types of SE queries.
Further analysis is needed on the practical impact of improving AUC scores and NLU performance.
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