CiRA: A Tool for the Automatic Detection of Causal Relationships in Requirements Artifacts

Abstract

Requirements often specify the expected system behavior by using causal relations (e.g., If A, then B). Automatically extracting these relations supports, among others, two prominent RE use cases: automatic test case derivation and dependency detection between requirements. However, existing tools fail to extract causality from natural language with reasonable performance. In this paper, we present our tool CiRA (Causality detection in Requirements Artifacts), which represents a first step towards automatic causality extraction from requirements. We evaluate CiRA on a publicly available data set of 61 acceptance criteria (causal: 32; non-causal: 29) describing the functionality of the German Corona-Warn-App. We achieve a macro 𝐹 1 score of 83 %, which corroborates the feasibility of our approach.

CiRA: An Open-Source Python Package for Automated Generation of Test Case Descriptions from Natural Language Requirements

“…To demonstrate the usability and performance of the CiRA tool, we evaluate its capability on an unseen set of natural language requirements 4 . Similar to the previous version of the tool [7], we chose the requirements of the German Corona Warn App 5 as a target system due to its recency, openness, and realism. The system contains ten epics, 32 user stories connected to these epics, and in total, 61 acceptance criteria.…”

“…Then, we executed both the CiRA classifier and the CiRA Test Suite Generator and compared the automatically generated results with the manual ones. We evaluate the classifier using the macro F1-score [7] and the test suite generator regarding its ability to infer correct events from the causal sentence and its ability to determine the correct test value configurations.…”