Requirements Quality Research: a harmonized Theory, Evaluation, and Roadmap

• URL: http://arxiv.org/abs/2309.10355v1
• Date: Tue, 19 Sep 2023 06:27:23 GMT
• Title: Requirements Quality Research: a harmonized Theory, Evaluation, and Roadmap
• Authors: Julian Frattini, Lloyd Montgomery, Jannik Fischbach, Daniel Mendez, Davide Fucci, Michael Unterkalmsteiner
• Abstract summary: High-quality requirements minimize the risk of propagating defects to later stages of the software development life cycle. This requires a clear definition and understanding of requirements quality.
• Score: 4.147594239309427
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: High-quality requirements minimize the risk of propagating defects to later stages of the software development life cycle. Achieving a sufficient level of quality is a major goal of requirements engineering. This requires a clear definition and understanding of requirements quality. Though recent publications make an effort at disentangling the complex concept of quality, the requirements quality research community lacks identity and clear structure which guides advances and puts new findings into an holistic perspective. In this research commentary we contribute (1) a harmonized requirements quality theory organizing its core concepts, (2) an evaluation of the current state of requirements quality research, and (3) a research roadmap to guide advancements in the field. We show that requirements quality research focuses on normative rules and mostly fails to connect requirements quality to its impact on subsequent software development activities, impeding the relevance of the research. Adherence to the proposed requirements quality theory and following the outlined roadmap will be a step towards amending this gap.

Related papers

• Leveraging LLMs for the Quality Assurance of Software Requirements [40.55044936397561]
  We introduce and assess the capabilities of a Large Language Model (LLM) to evaluate the quality characteristics of software requirements according to the ISO 29148 standard. We show how an LLM can assess requirements, explain its decision-making process, and examine its capacity to propose improved versions of requirements.
  arXiv  Detail & Related papers  (2024-08-20T14:17:50Z)
• Q-Ground: Image Quality Grounding with Large Multi-modality Models [61.72022069880346]
  We introduce Q-Ground, the first framework aimed at tackling fine-scale visual quality grounding. Q-Ground combines large multi-modality models with detailed visual quality analysis. Central to our contribution is the introduction of the QGround-100K dataset.
  arXiv  Detail & Related papers  (2024-07-24T06:42:46Z)
• Identifying relevant Factors of Requirements Quality: an industrial Case Study [0.5603839226601395]
  We conduct a case study considering data from both interview transcripts and issue reports to identify relevant factors of requirements quality. The results contribute empirical evidence that (1) strengthens existing requirements engineering theories and (2) advances industry-relevant requirements quality research.
  arXiv  Detail & Related papers  (2024-02-01T13:45:06Z)
• Quality Requirements for Code: On the Untapped Potential in Maintainability Specifications [5.342931064962865]
  This position paper proposes a synergistic approach, combining code-oriented research with Requirements Engineering expertise, to create meaningful industrial impact. Preliminary findings indicate that the established QUPER model, designed for setting quality targets, does not adequately address the unique aspects of maintainability.
  arXiv  Detail & Related papers  (2024-01-19T17:29:12Z)
• Towards Verifiable Generation: A Benchmark for Knowledge-aware Language Model Attribution [48.86322922826514]
  This paper defines a new task of Knowledge-aware Language Model Attribution (KaLMA) First, we extend attribution source from unstructured texts to Knowledge Graph (KG), whose rich structures benefit both the attribution performance and working scenarios. Second, we propose a new Conscious Incompetence" setting considering the incomplete knowledge repository. Third, we propose a comprehensive automatic evaluation metric encompassing text quality, citation quality, and text citation alignment.
  arXiv  Detail & Related papers  (2023-10-09T11:45:59Z)
• Requirements Quality Assurance in Industry: Why, What and How? [3.6142643912711794]
  We propose a taxonomy of requirements quality assurance complexity that characterizes cognitive load of verifying a quality aspect from the human perspective. Once this taxonomy is realized and validated, it can serve as the basis for a decision framework of automated requirements quality assurance support.
  arXiv  Detail & Related papers  (2023-08-24T14:31:52Z)
• Challenges in aligning requirements engineering and verification in a large-scale industrial context [7.92131557859946]
  This paper presents preliminary findings of interviews that identify key challenges in aligning requirements and verification processes. The findings of this study can be used by practitioners as a basis for investigating alignment in their organizations.
  arXiv  Detail & Related papers  (2023-07-23T20:08:49Z)
• Machine Learning with Requirements: a Manifesto [114.97965827971132]
  We argue that requirements definition and satisfaction can go a long way to make machine learning models even more fitting to the real world. We show how the requirements specification can be fruitfully integrated into the standard machine learning development pipeline.
  arXiv  Detail & Related papers  (2023-04-07T14:47:13Z)
• GLUECons: A Generic Benchmark for Learning Under Constraints [102.78051169725455]
  In this work, we create a benchmark that is a collection of nine tasks in the domains of natural language processing and computer vision. We model external knowledge as constraints, specify the sources of the constraints for each task, and implement various models that use these constraints.
  arXiv  Detail & Related papers  (2023-02-16T16:45:36Z)
• Image Quality Assessment in the Modern Age [53.19271326110551]
  This tutorial provides the audience with the basic theories, methodologies, and current progresses of image quality assessment (IQA) We will first revisit several subjective quality assessment methodologies, with emphasis on how to properly select visual stimuli. Both hand-engineered and (deep) learning-based methods will be covered.
  arXiv  Detail & Related papers  (2021-10-19T02:38:46Z)
• AI Techniques for Software Requirements Prioritization [91.3755431537592]
  The prioritization approaches discussed in this paper are based on different Artificial Intelligence (AI) techniques that can help to improve the overall quality of requirements prioritization processes.
  arXiv  Detail & Related papers  (2021-08-02T12:43:00Z)

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.