Automating the Detection of Requirement Dependencies Using Large Language Models

• URL: http://arxiv.org/abs/2602.22456v1
• Date: Wed, 25 Feb 2026 22:33:27 GMT
• Title: Automating the Detection of Requirement Dependencies Using Large Language Models
• Authors: Ikram Darif, Feifei Niu, Manel Abdellatif, Lionel C. Briand, Ramesh S., Arun Adiththan,
• Abstract summary: We introduce LEREDD, an LLM-based approach for automated detection of requirement dependencies.<n>It is designed to identify diverse dependency types directly from Natural Language (NL) requirements.<n>We empirically evaluate LEREDD against two state-of-the-art baselines.
• Score: 5.561866904930191
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Requirements are inherently interconnected through various types of dependencies. Identifying these dependencies is essential, as they underpin critical decisions and influence a range of activities throughout software development. However, this task is challenging, particularly in modern software systems, given the high volume of complex, coupled requirements. These challenges are further exacerbated by the ambiguity of Natural Language (NL) requirements and their constant change. Consequently, requirement dependency detection is often overlooked or performed manually. Large Language Models (LLMs) exhibit strong capabilities in NL processing, presenting a promising avenue for requirement-related tasks. While they have shown to enhance various requirements engineering tasks, their effectiveness in identifying requirement dependencies remains unexplored. In this paper, we introduce LEREDD, an LLM-based approach for automated detection of requirement dependencies that leverages Retrieval-Augmented Generation (RAG) and In-Context Learning (ICL). It is designed to identify diverse dependency types directly from NL requirements. We empirically evaluate LEREDD against two state-of-the-art baselines. The results show that LEREDD provides highly accurate classification of dependent and non-dependent requirements, achieving an accuracy of 0.93, and an F1 score of 0.84, with the latter averaging 0.96 for non-dependent cases. LEREDD outperforms zero-shot LLMs and baselines, particularly in detecting fine-grained dependency types, where it yields average relative gains of 94.87% and 105.41% in F1 scores for the Requires dependency over the baselines. We also provide an annotated dataset of requirement dependencies encompassing 813 requirement pairs across three distinct systems to support reproducibility and future research.

Related papers

• Type-Aware Retrieval-Augmented Generation with Dependency Closure for Solver-Executable Industrial Optimization Modeling [0.42753669499145647]
  We propose a type-aware retrieval-augmented generation (RAG) method that enforces modeling entity types and minimal dependency closure to ensure executability.<n>We validate the method on two constraint-intensive industrial cases: demand response optimization in battery production and flexible job shop scheduling.
  arXiv  Detail & Related papers  (2026-03-03T17:41:34Z)
• Relatron: Automating Relational Machine Learning over Relational Databases [50.94254514286021]
  We present a study that unifies RDL and DFS in a shared design space and conducts architecture-centric searches across diverse RDB tasks.<n>Our analysis yields three key findings: (1) RDL does not consistently outperform DFS, with performance being highly task-dependent; (2) no single architecture dominates across tasks, underscoring the need for task-aware model selection; and accuracy is an unreliable guide for choice architecture.
  arXiv  Detail & Related papers  (2026-02-26T02:45:22Z)
• The Path of Self-Evolving Large Language Models: Achieving Data-Efficient Learning via Intrinsic Feedback [51.144727949988436]
  Reinforcement learning (RL) has demonstrated potential to enhance the reasoning capabilities of large language models (LLMs)<n>In this work, we explore improving LLMs through RL with minimal data.<n>To minimize data dependency, we introduce two novel mechanisms grounded in self-awareness.
  arXiv  Detail & Related papers  (2025-10-03T06:32:10Z)
• Learning to Route: A Rule-Driven Agent Framework for Hybrid-Source Retrieval-Augmented Generation [55.47971671635531]
  Large Language Models (LLMs) have shown remarkable performance on general Question Answering (QA)<n>Retrieval-Augmented Generation (RAG) addresses this limitation by enriching LLMs with external knowledge.<n>Existing systems primarily rely on unstructured documents, while largely overlooking relational databases.
  arXiv  Detail & Related papers  (2025-09-30T22:19:44Z)
• Data Dependency-Aware Code Generation from Enhanced UML Sequence Diagrams [54.528185120850274]
  We propose a novel step-by-step code generation framework named API2Dep.<n>First, we introduce an enhanced Unified Modeling Language (UML) API diagram tailored for service-oriented architectures.<n>Second, recognizing the critical role of data flow, we introduce a dedicated data dependency inference task.
  arXiv  Detail & Related papers  (2025-08-05T12:28:23Z)
• CoRe: Benchmarking LLMs Code Reasoning Capabilities through Static Analysis Tasks [14.408364047538578]
  Large language models (LLMs) have been widely adopted across diverse domains of software engineering.<n>This work presents CORE, a benchmark designed to evaluate LLMs on fundamental static analysis tasks.
  arXiv  Detail & Related papers  (2025-07-03T01:35:58Z)
• TVR: Automotive System Requirement Traceability Validation and Recovery Through Retrieval-Augmented Generation [6.254217675711076]
  We introduce TVR, a requirement Traceability Validation and Recovery approach primarily targeting automotive systems.<n>TVR is designed to validate existing traceability links and recover missing ones with high accuracy.
  arXiv  Detail & Related papers  (2025-04-21T20:37:23Z)
• Goal-Driven Query Answering over First- and Second-Order Dependencies with Equality [9.880191856609581]
  We present what we believe to be the first technique for goal-driven query answering over first- and second-order dependencies with equality reasoning.<n>Our technique transforms the input dependencies so that applying the chase to the output avoids many inferences that are irrelevant to the query.<n>We also present the results of an extensive empirical evaluation, which show that goal-driven query answering can be orders of magnitude faster than computing the full universal model.
  arXiv  Detail & Related papers  (2024-12-12T10:02:16Z)
• Unconditional Truthfulness: Learning Unconditional Uncertainty of Large Language Models [104.55763564037831]
  We train a regression model that leverages attention maps, probabilities on the current generation step, and recurrently computed uncertainty scores from previously generated tokens.<n>Our evaluation shows that the proposed method is highly effective for selective generation, achieving substantial improvements over rivaling unsupervised and supervised approaches.
  arXiv  Detail & Related papers  (2024-08-20T09:42:26Z)
• Requirements' Characteristics: How do they Impact on Project Budget in a Systems Engineering Context? [3.2872885101161318]
  Controlling and assuring the quality of natural language requirements (NLRs) is challenging. We investigated with the Swedish Transportation Agency (STA) to what extent the characteristics of requirements had an influence on change requests and budget changes in the project.
  arXiv  Detail & Related papers  (2023-10-02T17:53:54Z)
• Variable Importance Matching for Causal Inference [73.25504313552516]
  We describe a general framework called Model-to-Match that achieves these goals. Model-to-Match uses variable importance measurements to construct a distance metric. We operationalize the Model-to-Match framework with LASSO.
  arXiv  Detail & Related papers  (2023-02-23T00:43:03Z)
• The BP Dependency Function: a Generic Measure of Dependence between Random Variables [0.0]
  Measuring and quantifying dependencies between random variables (RV's) can give critical insights into a data-set. Common practice of data analysis is that most data analysts use the Pearson correlation coefficient (PCC) to quantify dependence between RV's. We propose a new dependency function that meets all these requirements.
  arXiv  Detail & Related papers  (2022-03-23T11:14:40Z)
• Leveraging Semantic Parsing for Relation Linking over Knowledge Bases [80.99588366232075]
  We present SLING, a relation linking framework which leverages semantic parsing using AMR and distant supervision. SLING integrates multiple relation linking approaches that capture complementary signals such as linguistic cues, rich semantic representation, and information from the knowledgebase. experiments on relation linking using three KBQA datasets; QALD-7, QALD-9, and LC-QuAD 1.0 demonstrate that the proposed approach achieves state-of-the-art performance on all benchmarks.
  arXiv  Detail & Related papers  (2020-09-16T14:56:11Z)

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.