Analyzing and Debugging Normative Requirements via Satisfiability Checking
January 11, 2024 Β· Declared Dead Β· π International Conference on Software Engineering
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Nick Feng, Lina Marsso, Sinem Getir Yaman, Yesugen Baatartogtokh, Reem Ayad, VictΓ³ria Oldemburgo de Mello, Beverley Townsend, Isobel Standen, Ioannis Stefanakos, Calum Imrie, GenaΓna Nunes Rodrigues, Ana Cavalcanti, Radu Calinescu, Marsha Chechik
arXiv ID
2401.05673
Category
cs.SE: Software Engineering
Citations
20
Venue
International Conference on Software Engineering
Last Checked
3 months ago
Abstract
As software systems increasingly interact with humans in application domains such as transportation and healthcare, they raise concerns related to the social, legal, ethical, empathetic, and cultural (SLEEC) norms and values of their stakeholders. Normative non-functional requirements (N-NFRs) are used to capture these concerns by setting SLEEC-relevant boundaries for system behavior. Since N-NFRs need to be specified by multiple stakeholders with widely different, non-technical expertise (ethicists, lawyers, regulators, end users, etc.), N-NFR elicitation is very challenging. To address this challenge, we introduce N-Check, a novel tool-supported formal approach to N-NFR analysis and debugging. N-Check employs satisfiability checking to identify a broad spectrum of N-NFR well-formedness issues (WFI), such as conflicts, redundancy, restrictiveness, insufficiency, yielding diagnostics which pinpoint their causes in a user-friendly way that enables non-technical stakeholders to understand and fix them. We show the effectiveness and usability of our approach through nine case studies in which teams of ethicists, lawyers, philosophers, psychologists, safety analysts, and engineers used N-Check to analyse and debug 233 N-NFRs comprising 62 issues for the software underpinning the operation of systems ranging from assistive-care robots and tree-disease detection drones to manufacturing collaborative robots.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Software Engineering
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
GraphCodeBERT: Pre-training Code Representations with Data Flow
R.I.P.
π»
Ghosted
DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars
R.I.P.
π»
Ghosted
Microservices: yesterday, today, and tomorrow
R.I.P.
π»
Ghosted
Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks
R.I.P.
π»
Ghosted
A Survey of Machine Learning for Big Code and Naturalness
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Language Models are Few-Shot Learners
R.I.P.
π»
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
π»
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
π»
Ghosted