NFR-to-Code Traceability in a Blockchain-IoT System: An Empirical Study

• URL: http://arxiv.org/abs/2607.25325v1
• Date: Tue, 28 Jul 2026 06:20:33 GMT
• Title: NFR-to-Code Traceability in a Blockchain-IoT System: An Empirical Study
• Abstract summary: This paper studies NFR-to-Code traceability based on a real-world blockchain-IoT project.<n>We design an annotation protocol for constructing trace links across heterogeneous artifacts and build a manually curated subset containing both FR and NFR links.<n>The results show that FR-to-Code tracing is consistently easier than NFR-to-Code tracing, while security-related NFRs are the most difficult subset.
• Score: 14.238775682979513
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Requirement-to-Code traceability has been widely studied, yet existing research and public benchmarks remain largely centered on functional requirements (FRs). In contrast, traceability for non-functional requirements (NFRs) remains more difficult and underexplored, which hinders the verification of critical quality concerns such as security and reliability.This paper studies NFR-to-Code traceability based on a real-world blockchain-IoT project. We design an annotation protocol for constructing trace links across heterogeneous artifacts and build a manually curated subset containing both FR and NFR links. Using this subset, we examine the characteristics of NFR traceability and further evaluate four representative retrieval baselines: TF-IDF, BM25, LSI, and WMD. The results show that FR-to-Code tracing is consistently easier than NFR-to-Code tracing, while security-related NFRs are the most difficult subset. They further indicate that the main challenge of NFR traceability lies not in requirement availability, but in implementation evidence that is distributed and not clearly localized in code.

Related papers

• Scalable and Verifiable Federated Learning for Cross-Institution Financial Fraud Detection [0.0]
  We propose Dynamic Sharded Learning (DSFL), a verifiable secure aggregation framework for fraud detection.<n>DSFL replaces topologies with Dynamic Sharding, reducing complexity from O(N2) to m(N2)<n>To mitigate insider threats, we introduce Linear Integrity Tags, an additive-homomorphic commitment mechanism.
  arXiv  Detail & Related papers  (2026-04-25T20:49:22Z)
• NERFIFY: A Multi-Agent Framework for Turning NeRF Papers into Code [49.610331036334316]
  We introduce NERFIFY, a framework that reliably converts NeRF research papers into trainable Nerfstudio plugins.<n>Code, data and implementations will be publicly released.
  arXiv  Detail & Related papers  (2026-02-28T20:57:32Z)
• RealSec-bench: A Benchmark for Evaluating Secure Code Generation in Real-World Repositories [58.32028251925354]
  Large Language Models (LLMs) have demonstrated remarkable capabilities in code generation, but their proficiency in producing secure code remains a critical, under-explored area.<n>We introduce RealSec-bench, a new benchmark for secure code generation meticulously constructed from real-world, high-risk Java repositories.
  arXiv  Detail & Related papers  (2026-01-30T08:29:01Z)
• Advancing Neural Network Verification through Hierarchical Safety Abstract Interpretation [52.626086874715284]
  We introduce a novel problem formulation called Abstract DNN-Verification, which verifies a hierarchical structure of unsafe outputs.<n>By leveraging abstract interpretation and reasoning about output reachable sets, our approach enables assessing multiple safety levels during the formal verification process.<n>Our contributions include a theoretical exploration of the relationship between our novel abstract safety formulation and existing approaches.
  arXiv  Detail & Related papers  (2025-05-08T13:29:46Z)
• RobuNFR: Evaluating the Robustness of Large Language Models on Non-Functional Requirements Aware Code Generation [52.87427601131587]
  We propose RobuNFR for evaluating the robustness of LLMs in NFR-aware code generation.<n>Our experiments show that RobuNFR reveals issues in the tested LLMs when considering NFRs in code generation.
  arXiv  Detail & Related papers  (2025-03-28T20:05:33Z)
• FRAG: Toward Federated Vector Database Management for Collaborative and Secure Retrieval-Augmented Generation [1.3824176915623292]
  This paper introduces textitFederated Retrieval-Augmented Generation (FRAG), a novel database management paradigm tailored for the growing needs of retrieval-augmented generation (RAG) systems. FRAG enables mutually-distrusted parties to collaboratively perform Approximate $k$-Nearest Neighbor (ANN) searches on encrypted query vectors and encrypted data stored in distributed vector databases.
  arXiv  Detail & Related papers  (2024-10-17T06:57:29Z)
• Non-negative Contrastive Learning [34.857412784865645]
  Deep representations have shown promising performance when transferred to downstream tasks in a black-box manner. Yet, their inherent lack of interpretability remains a significant challenge. We propose Non-negative Contrastive Learning to deriving interpretable features.
  arXiv  Detail & Related papers  (2024-03-19T05:30:50Z)
• UFed-GAN: A Secure Federated Learning Framework with Constrained Computation and Unlabeled Data [50.13595312140533]
  We propose a novel framework of UFed-GAN: Unsupervised Federated Generative Adversarial Network, which can capture user-side data distribution without local classification training. Our experimental results demonstrate the strong potential of UFed-GAN in addressing limited computational resources and unlabeled data while preserving privacy.
  arXiv  Detail & Related papers  (2023-08-10T22:52:13Z)
• Zero-Shot Learning for Requirements Classification: An Exploratory Study [6.855054517723465]
  Requirements Engineering (RE) researchers have been experimenting Machine Learning (ML) and Deep Learning (DL) approaches for a range of RE tasks. Most of today's ML-DL approaches are based on supervised learning techniques, meaning that they need to be trained using annotated datasets. This paper proposes an approach that employs the embedding-based unsupervised Zero-Shot Learning (ZSL) technique to perform requirements classification.
  arXiv  Detail & Related papers  (2023-02-09T16:05:01Z)
• Validation Diagnostics for SBI algorithms based on Normalizing Flows [55.41644538483948]
  This work proposes easy to interpret validation diagnostics for multi-dimensional conditional (posterior) density estimators based on NF. It also offers theoretical guarantees based on results of local consistency. This work should help the design of better specified models or drive the development of novel SBI-algorithms.
  arXiv  Detail & Related papers  (2022-11-17T15:48:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.