Generative Artificial Intelligence (GenAI) is being rapidly adopted in software engineering, introducing a paradigm shift toward human-AI co-creation. However, the non-deterministic, probabilistic, and often black-box nature of GenAI models presents challenges for traditional software quality assurance. Conventional verification and validation techniques are insufficient to handle outputs that are neither predictably correct nor incorrect, but rather stochastically plausible. This discrepancy creates an urgent need for practical processes, metrics, and new governance frameworks to evaluate and manage the quality of GenAI systems in industrial environments.This thesis examines how industrial organizations adopt GenAI, identify metrics, and evaluate system qualities in alignment with ISO quality standards. Case studies were employed to explore real-world adoption processes, identify context-specific industrial metrics, and uncover practical insights within organizations. A snowballing literature review was conducted to systematically identify, categorize, and synthesize academic metrics for evaluating the output of GenAI systems. Finally, a controlled experiment was designed to quantitatively test the efficiency (e.g., E2E generation time) and effectiveness (e.g., accuracy) of GenAI agent choices. The main contributions of this thesis are a synthesized actionable model and framework grounded in both industrial practice and quality standards. The first contribution is a four-stage adoption model, denoted as the IMRM model (Innovate → considerations, Measure → metrics, Realize → values, Manage → improvements) that integrates early-stage risk assessment (e.g., legal, security, and licensing) andquality evaluation throughout the GenAI adoption and usage.The second contribution presents a detailed framework that connects risks andmetrics to concrete decision support, justifying the business value (e.g., quality gates) and technical trade-offs of GenAI solutions. The third contribution provides a structured mapping of GenAI quality to ISO/IEC 25010, 25023, and 25059 characteristics, attempting to ground practical evaluation needs within a standardized vocabulary. This thesis concludes that a structured quality evaluation process, which prioritizes risks and context, is a valuable approach intended to support building the business confidence required to leverage GenAI for efficient and effective software engineering in industry.

Today, increased global competition and regulatory demands push organizations to find innovative ways to design and create value. One way is to examine Product-Service System offerings, which enable more stable revenues and foster tighter collaboration and value co-creation between the provider and customer. However, this often means adopting a System-of-Systems (SoS) approach where the value creation is dependent on successful collaboration between constituent systems and actors. SoS also leads to higher complexity and uncertainty, making them more challenging to design, especially in the early stages where knowledge is limited. Therefore, this thesis investigates how to leverage simulations and operational data to explore the value creation in SoS design.

Methodologically, the work adopts a Design Research Methodology and Participatory Action Research approach, combining systematic literature reviews with six industrial case studies in the construction machinery and climate resilience sectors. The case studies originate from a series of research projects conducted in collaboration with various industrial and public partners between 2020 and 2025. 

The research introduces two main contributions. First, the Value–Data Framework, which operationalizes Value-Driven Design by linking value modeling to operational datasets, enabling transparent and quantitative assessment of value creation. The value model introduces a two-step value hierarchy to enhance transparency and address the growing complexity of SoS. Operational data is defined and classified clearly to show where and how it can be leveraged. The second contribution, an Integrated Simulation Platform, equips design teams with the ability to develop effective simulations for SoS modeling in early design stages. The platform explicitly addresses multi-fidelity to guide developers in balancing the computational complexity and accuracy. Finally, both contributions are demonstrated in quarry case studies to validate and highlight their usefulness. Lastly, the SoS simulations are contextualized for Digital Twinning, and the use cases and needs for deploying Digital Twins in climate resilience efforts are examined.

The findings advance theory by bridging Value-Driven Design and SoS simulations, and provide practical guidelines for managing fidelity trade-offs and data–value integration. Industrial implications include improved decision support for electrification, autonomy, and resilience strategies. 

Context: Producers of reusable software assets frequently encounter a high volume of feature requests and bug reports from consumers. InnerSource (IS) offers a potential solution through Participatory Reuse (PR), where consumers participate in the development and maintenance of reusable assets.

Objective: This thesis aims to understand, organize, and improve PR by: 1) understanding the state of the art and practice regarding the costs, benefits, and challenges of software reuse; 2) organizing existing knowledge on PR; and 3) supporting companies in understanding and improving their PR practices.

Methods: This thesis used a mixed-methods approach (an SLR, a case survey, and four case studies) to investigate PR. The SLR explored the reported costs and benefits of software reuse. An exploratory case study assessed reuse practices at a medium-sized company, followed by an improving case study on its readiness for PR. The case survey synthesized existing knowledge into a PR catalog and taxonomy, which was validated and refined through expert evaluations and two additional case studies.

Results: The results of SLR and exploratory case study showed that improved product quality and productivity are the primary benefits of software reuse, though not without associated costs and challenges. The results of two case studies with the same company demonstrated that IS, particularly PR, can help address reuse challenges such as discoverability and ownership of reusable assets. We developed and tested an instrument to assess the company's readiness to adopt PR, identifying areas for improvement and potential solutions. To organize the PR body of knowledge, we developed a PR catalog and taxonomy. The catalog consolidated PR challenges, solutions, and lessons from industrial cases, while the taxonomy provides a mechanism to characterize PR. Finally, we developed a checklist based on the taxonomy for practitioners to assess their current PR practices and identify desired changes.

Conclusion: This thesis advances the field of PR by proposing and validating interventions to improve and characterize PR. The proposed readiness instrument helped the case company to reflect on its current PR practices and identify the areas for improvement. The PR catalog was found to be valuable by experts for providing a clear mapping from PR challenges to the associated solutions and lessons. With the help of two case studies, this thesis demonstrates the utility of the PR taxonomy and its associated checklist in characterizing PR and identifying areas for improvement.