arXiv subjects

Hina Saeeda

Publications and source records attributed to Hina Saeeda.

5 recordsLinked to original sources

arXiv2026

Requirements Debt in AI-Enabled Perception Systems Development: An Industrial RE4AI Perspective

AI integration in automotive perception systems shifts requirements from static specifications to continuously evolving entities shaped by data, models, and operating contexts. When such changes are not consistently documented, validated, and traced, they accumulate as Requirements Debt (ReD), an underexplored but critical subtype of technical debt. This study conceptualises and empirically investigates how evolving functional and non-functional requirements create and propagate ReD across the AI-enabled automotive perception system lifecycle. We conducted 16 semi-structured interviews with experts from 13 international automotive companies and 3 European research institutes, and analysed the data using thematic analysis. As one of the first empirical studies connecting technical debt theory with RE4AI, the work identifies key ReD mechanisms. Evolving functional requirements (e.g., algorithm updates, sensor fusion, architectural changes, real-time constraints) drive semantic drift, validation backlogs, and integration debt when verification lags behind rapid iteration. In parallel, evolving non-functional requirements (e.g., safety, cybersecurity, reliability, scalability, transparency, trustworthiness) create assurance lag, compliance misalignment, and transparency and reliability debt as standards and ethical expectations shift. These interacting mechanisms propagate ReD across data, models, and system artefacts, undermining auditability, reliability, and certification readiness in safety-critical perception systems.

cs.SE↗

arXiv2026

Deriving and Validating Requirements Engineering Principles for Large-Scale Agile Development: An Industrial Longitudinal Study

In large scale agile systems development, the lack of a unified requirements engineering (RE) process is a major challenge, exacerbated by the absence of high level guiding principles for effective requirements management. To address this challenge, we conducted a five year longitudinal case study with Grundfos AB, in collaboration with the Software Centre in Sweden. RE principles were first derived through qualitative data collection spanning more than 25 sprints, approximately 320 weekly synchronisation meetings, and seven cross-company, company-specific workshops between 2019 and 2024. These activities engaged practitioners from diverse roles, representing several hundred developers across domains. In late 2024, five in depth focus groups with senior leaders at Grundfos provided retrospective validation of the principles and assessed their strategic impact. We aim to (1) empirically examine RE principles in large scale agile system development, (2) explore their benefits in practice within the case company, and (3) identify a set of transferable RE principles for large scale contexts. Using thematic analysis, six key RE principles architectural context, stakeholder-driven validation and alignment, requirements practices in large-scale agile organisations. evolution with lightweight documentation, delegated requirements management, organisational roles and responsibilities, and a shared understanding of requirements are derived. The study was further validated through crosscompany expert evaluation with three additional multinational organisations (Bosch, Ericsson, and Volvo Cars), which are directly responsible for largescale requirements management. Together, these efforts provide a scalable and adaptable foundation for improving requirements practices in largescale agile organisations.

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arXiv2025

A Data Annotation Requirements Representation and Specification (DARS)

With the rise of AI-enabled cyber-physical systems, data annotation has become a critical yet often overlooked process in the development of these intelligent information systems. Existing work in requirements engineering (RE) has explored how requirements for AI systems and their data can be represented. However, related interviews with industry professionals show that data annotations and their related requirements introduce distinct challenges, indicating a need for annotation-specific requirement representations. We propose the Data Annotation Requirements Representation and Specification (DARS), including an Annotation Negotiation Card to align stakeholders on objectives and constraints, and a Scenario-Based Annotation Specification to express atomic and verifiable data annotation requirements. We evaluate DARS with an automotive perception case related to an ongoing project, and a mapping against 18 real-world data annotation error types. The results suggest that DARS mitigates root causes of completeness, accuracy, and consistency annotation errors. By integrating DARS into RE, this work improves the reliability of safety-critical systems using data annotations and demonstrates how engineering frameworks must evolve for data-dependent components of today's intelligent information systems.

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arXiv2025

Data Annotation Quality Problems in AI-Enabled Perception System Development

Data annotation is essential but highly error-prone in the development of AI-enabled perception systems (AIePS) for automated driving, and its quality directly influences model performance, safety, and reliability. However, the industry lacks empirical insights into how annotation errors emerge and spread across the multi-organisational automotive supply chain. This study addresses this gap through a multi-organisation case study involving six companies and four research institutes across Europe and the UK. Based on 19 semi-structured interviews with 20 experts (50 hours of transcripts) and a six-phase thematic analysis, we develop a taxonomy of 18 recurring annotation error types across three data-quality dimensions: completeness (e.g., attribute omission, missing feedback loops, edge-case omissions, selection bias), accuracy (e.g., mislabelling, bounding-box inaccuracies, granularity mismatches, bias-driven errors), and consistency (e.g., inter-annotator disagreement, ambiguous instructions, misaligned hand-offs, cross-modality inconsistencies). The taxonomy was validated with industry practitioners, who reported its usefulness for root-cause analysis, supplier quality reviews, onboarding, and improving annotation guidelines. They described it as a failure-mode catalogue similar to FMEA. By conceptualising annotation quality as a lifecycle and supply-chain issue, this study contributes to SE4AI by offering a shared vocabulary, diagnostic toolset, and actionable guidance for building trustworthy AI-enabled perception systems.

cs.SE↗

arXiv2025

RE for AI in Practice: Managing Data Annotation Requirements for AI Autonomous Driving Systems

High-quality data annotation requirements are crucial for the development of safe and reliable AI-enabled perception systems (AIePS) in autonomous driving. Although these requirements play a vital role in reducing bias and enhancing performance, their formulation and management remain underexplored, leading to inconsistencies, safety risks, and regulatory concerns. Our study investigates how annotation requirements are defined and used in practice, the challenges in ensuring their quality, practitioner-recommended improvements, and their impact on AIePS development and performance. We conducted $19$ semi-structured interviews with participants from six international companies and four research organisations. Our thematic analysis reveals five main key challenges: ambiguity, edge case complexity, evolving requirements, inconsistencies, and resource constraints and three main categories of best practices, including ensuring compliance with ethical standards, improving data annotation requirements guidelines, and embedded quality assurance for data annotation requirements. We also uncover critical interrelationships between annotation requirements, annotation practices, annotated data quality, and AIePS performance and development, showing how requirement flaws propagate through the AIePS development pipeline. To the best of our knowledge, this study is the first to offer empirically grounded guidance on improving annotation requirements, offering actionable insights to enhance annotation quality, regulatory compliance, and system reliability. It also contributes to the emerging fields of Software Engineering (SE for AI) and Requirements Engineering (RE for AI) by bridging the gap between RE and AI in a timely and much-needed manner.

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