3 citations · 3 across the 5 of their papers we have counts for
6 papers
How Do Practitioners Interpret Conditionals in Requirements?
Jannik Fischbach, Julian Frattini, Daniel Mendez +3
Context: Conditional statements like "If A and B then C" are core elements for describing software requirements. However, there are many ways to express such conditionals in natura…
Transfer Learning for Mining Feature Requests and Bug Reports from Tweets and App Store Reviews
Pablo Restrepo Henao, Jannik Fischbach, Dominik Spies +2
Identifying feature requests and bug reports in user comments holds great potential for development teams. However, automated mining of RE-related information from social media and…
CATE: CAusality Tree Extractor from Natural Language Requirements
Noah Jadallah, Jannik Fischbach, Julian Frattini +1
Causal relations (If A, then B) are prevalent in requirements artifacts. Automatically extracting causal relations from requirements holds great potential for various RE activities…
Fine-Grained Causality Extraction From Natural Language Requirements Using Recursive Neural Tensor Networks
Jannik Fischbach, Tobias Springer, Julian Frattini +3
[Context:] Causal relations (e.g., If A, then B) are prevalent in functional requirements. For various applications of AI4RE, e.g., the automatic derivation of suitable test cases…
CiRA: A Tool for the Automatic Detection of Causal Relationships in Requirements Artifacts
Jannik Fischbach, Julian Frattini, Andreas Vogelsang
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 promine…
Automatic Detection of Causality in Requirement Artifacts: the CiRA Approach
Jannik Fischbach, Julian Frattini, Arjen Spaans +4
System behavior is often expressed by causal relations in requirements (e.g., If event 1, then event 2). Automatically extracting this embedded causal knowledge supports not only r…