Quality Requirements for Code: On the Untapped Potential in
Maintainability Specifications
- URL: http://arxiv.org/abs/2401.10833v1
- Date: Fri, 19 Jan 2024 17:29:12 GMT
- Title: Quality Requirements for Code: On the Untapped Potential in
Maintainability Specifications
- Authors: Markus Borg
- Abstract summary: 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.
- Score: 5.342931064962865
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Quality requirements are critical for successful software engineering, with
maintainability being a key internal quality. Despite significant attention in
software metrics research, maintainability has attracted surprisingly little
focus in the Requirements Engineering (RE) community. This position paper
proposes a synergistic approach, combining code-oriented research with RE
expertise, to create meaningful industrial impact. We introduce six
illustrative use cases and propose three future research directions.
Preliminary findings indicate that the established QUPER model, designed for
setting quality targets, does not adequately address the unique aspects of
maintainability.
Related papers
- Teaching Requirements Engineering for AI: A Goal-Oriented Approach in Software Engineering Courses [4.273966905160028]
It is crucial to prepare software engineers with the abilities to specify high-quality requirements for AI-based systems.
This research aims to evaluate the effectiveness and applicability of Goal-Oriented Requirements Engineering (GORE) in facilitating requirements elicitation.
arXiv Detail & Related papers (2024-10-26T23:44:01Z) - Engineering Trustworthy AI: A Developer Guide for Empirical Risk Minimization [53.80919781981027]
Key requirements for trustworthy AI can be translated into design choices for the components of empirical risk minimization.
We hope to provide actionable guidance for building AI systems that meet emerging standards for trustworthiness of AI.
arXiv Detail & Related papers (2024-10-25T07:53:32Z) - Trustworthiness in Retrieval-Augmented Generation Systems: A Survey [59.26328612791924]
Retrieval-Augmented Generation (RAG) has quickly grown into a pivotal paradigm in the development of Large Language Models (LLMs)
We propose a unified framework that assesses the trustworthiness of RAG systems across six key dimensions: factuality, robustness, fairness, transparency, accountability, and privacy.
arXiv Detail & Related papers (2024-09-16T09:06:44Z) - Requirements Quality Research: a harmonized Theory, Evaluation, and
Roadmap [4.147594239309427]
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.
arXiv Detail & Related papers (2023-09-19T06:27:23Z) - 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) - Using Machine Learning To Identify Software Weaknesses From Software
Requirement Specifications [49.1574468325115]
This research focuses on finding an efficient machine learning algorithm to identify software weaknesses from requirement specifications.
Keywords extracted using latent semantic analysis help map the CWE categories to PROMISE_exp. Naive Bayes, support vector machine (SVM), decision trees, neural network, and convolutional neural network (CNN) algorithms were tested.
arXiv Detail & Related papers (2023-08-10T13:19:10Z) - A New Perspective on Evaluation Methods for Explainable Artificial
Intelligence (XAI) [0.0]
We argue that it is best approached in a nuanced way that incorporates resource availability, domain characteristics, and considerations of risk.
This work aims to advance the field of Requirements Engineering for AI.
arXiv Detail & Related papers (2023-07-26T15:15:44Z) - Mitigating Risks in Software Development through Effective Requirements
Engineering [0.0]
This article provides an overview of the importance of requirements gathering in secure software development.
It explains the crucial role of Requirements Engineers in defining and understanding the customer's needs and desires.
The article emphasizes the need to mitigate the risks of vagueness and ambiguity early on and provides techniques for evaluating, negotiating, and prioritizing requirements.
arXiv Detail & Related papers (2023-05-09T23:12:28Z) - Design Space Exploration and Explanation via Conditional Variational
Autoencoders in Meta-model-based Conceptual Design of Pedestrian Bridges [52.77024349608834]
This paper provides a performance-driven design exploration framework to augment the human designer through a Conditional Variational Autoencoder (CVAE)
The CVAE is trained on 18'000 synthetically generated instances of a pedestrian bridge in Switzerland.
arXiv Detail & Related papers (2022-11-29T17:28:31Z) - 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) - Towards CRISP-ML(Q): A Machine Learning Process Model with Quality
Assurance Methodology [53.063411515511056]
We propose a process model for the development of machine learning applications.
The first phase combines business and data understanding as data availability oftentimes affects the feasibility of the project.
The sixth phase covers state-of-the-art approaches for monitoring and maintenance of a machine learning applications.
arXiv Detail & Related papers (2020-03-11T08:25:49Z)
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.