Multi-Label Requirements Classification with Large Taxonomies

Context and motivation: Classification aids software development activities by organizing requirements in classes for easier access and retrieval. The majority of requirements classification research has, so far, focused on binary or multi-class classification. Question/problem: Multi-label classification with large taxonomies could aid requirements traceability but is prohibitively costly with supervised training. Hence, we investigate zero-short learning to evaluate the feasibility of multi-label requirements classification with large taxonomies. Principal ideas/results: We associated, together with domain experts from the industry, 129 requirements with 769 labels from taxonomies ranging between 250 and 1183 classes. Then, we conducted a controlled experiment to study the impact of the type of classifier, the hierarchy, and the structural characteristics of taxonomies on the classification performance. The results show that: (1) The sentence-based classifier had a significantly higher recall compared to the word-based classifier; however, the precision and F1-score did not improve significantly. (2) The hierarchical classification strategy did not always improve the performance of requirements classification. (3) The total and leaf nodes of the taxonomies have a strong negative correlation with the recall of the hierarchical sentence-based classifier. Contribution: We investigate the problem of multi-label requirements classification with large taxonomies, illustrate a systematic process to create a ground truth involving industry participants, and provide an analysis of different classification pipelines using zero-shot learning.