COVID-19 accelerated the digital-health market, resetting user expectations at a pace that makes traditional release planning brittle. In partnership with Curatio, we conducted a 12-month longitudinal case study to evaluate and improve the release process for their flagship app, Stronger Together . First, we mined public app store data to map the product space and quantify the reuse value of 134 candidate features. Next, we formulated a bi-objective optimization that maximizes reuse value while preserving feature cohesion, yielding a data-driven “Plan K” for the next release. Finally, we compared this plan with the two releases Curatio shipped, combining artifact analysis with semi-structured interviews. Five of the seven recommended features were adopted, capturing 80% of the predicted reuse value; two high-impact items were deferred because of technical and regulatory constraints. The evaluation identifies four recurrent “release traps” that explain why theoretically optimal plans can fail in practice and demonstrates how good-enough, continuously replanned roadmaps can deliver most of the benefits with significantly less risk. The study contributes a replicable appraisal method for feature-reuse decisions and offers actionable guidance for industrial teams facing the same balance of legacy maintenance, rapid innovation, and market uncertainty.

As Artificial Intelligence (AI) becomes increasingly embedded in healthcare technologies, understanding the maturity of AI in patient-centric applications is critical for evaluating its trustworthiness, transparency, and real-world impact. In this study, we investigate the integration and maturity of AI feature integration in 116 patient-centric healthcare applications. Using Large Language Models (LLMs), we extracted key functional features, which are then categorized into different stages of the Gartner AI maturity model. Our results show that over 86.21% of applications remain at the early stages of AI integration, while only 13.79% demonstrate advanced AI integration.

Context: GitHub, renowned for facilitating collaborative code version control and software production in software teams, expanded its services in 2017 by introducing GitHub Marketplace. This online platform hosts automation tools to assist developers with the production of their GitHub-hosted projects, and it has become a valuable source of information on the tools used in the Open Source Software (OSS) community. Objective: In this exploratory study , we introduce GitHub Marketplace as a software marketplace by exploring the Characteristics, Features, and Policies of the platform comprehensively, identifying common themes in production automation. Further, we explore popular tools among practitioners and researchers and highlight disparities in the approach to these tools between industry and academia. Method: We adopted the conceptual framework of software app stores from previous studies and used that to examine 8,318 automated production tools (440 Apps and 7,878 Actions) across 32 categories on GitHub Marketplace. We explored and described the policies of this marketplace as a unique platform where developers share production tools for the use of other developers. Furthermore, we conducted a systematic mapping of 515 research papers published from 2000 to 2021 and compared open-source academic production tools with those available in the marketplace. Results: We found that although some of the automation topics in literature are widely used in practice, they have yet to align with the state-of-practice for automated production. We discovered that practitioners often use automation tools for tasks like “Continuous Integration” and “Utilities”, while researchers tend to focus more on “Code Quality” and “Testing”. Conclusion: Our study illuminates the landscape of open-source tools for automation production. We also explored the disparities between industry trends and researchers’ priorities. Recognizing these distinctions can empower researchers to build on existing work and guide practitioners in selecting tools that meet their specific needs. Bridging this gap between industry and academia helps with further innovation in the field and ensures that research remains pertinent to the evolving challenges in software production.