Reliance on data-centric machine learning (ML) models in complex systems has posed numerous challenges in the software engineering process, especially when the system is deployed in a high-risk environment Due to the inherent uncertainty of such ML models, the safety assurance of these systems is now a primary concern. Recently, many researchers are focusing on assuring safe outcomes of ML models. However, not enough attention is paid to evaluating the training data uncertainty before indulging in ML training. Currently, there are no specific guidelines on how to perceive, elicit and specify data requirements and assure the data quality depending on the ML objective and problem domain. To address these gaps, this research provides guidelines for a systematic data requirements engineering and data uncertainty assessment process involving diverse stakeholders. A three-layered framework is proposed that helps to explore the data space and elicit verifiable data requirements. Such requirements can facilitate the evaluation of the collective confidence of the experts in data quality. To accommodate epistemic uncertainty of such assessment (inconclusive due to lack of knowledge) Dempster Shafer’s theory of evidence is used. The application of this theory within the proposed framework aims to address the identified research gaps.

The growing complexity and dynamics of the execution environment have been major motivation for designing self-adaptive systems. Although significant work can be found in the field of formalizing or modeling the requirements of adaptive system, not enough attention has been paid towards the requirements elicitation techniques for the same. It is still an open challenge to elicit the users' requirements in the light of various contexts and introduce the required flexibility in the system's behavior at an early phase of requirements engineering. We explore the idea of using a cognitive technique, repertory grid, to acquire the knowledge of various stakeholders along multiple dimensions of problem space and design space. We aim at discovering the scope of variations in the features of the system by capturing the intentional and technical variability in the problem space and design space respectively. A stepwise methodology for finding the right set of features in the changing context has also been provided in this work. We evaluate the proposed idea by a preliminary case study using smart home system domain.