Understanding the emergence of collective behaviors of multi-agent systems requires investigating the learning dynamics. However, the theoretical analysis of large-scale graph-structured multi-agent reinforcement learning (MARL) systems remains challenging due to agent heterogeneity and the intrinsic coupling between state transitions and individual Q-value updates. In this work, we develop a unified theoretical framework that captures the evolution of agent behaviors at both individual and population levels. By leveraging the pair approximation technique from statistical physics, we derive a closed set of evolution equations that accurately describe the temporal dynamics of the system. Our analysis also reveals a separation of time scales. For small learning rates, state transitions equilibrate rapidly, while Q-value updates evolve slowly with stationary state distributions. Through extensive agent-based simulations, we validate the robustness of our theoretical results and explain the mechanisms that lead to the emergence of cooperation in social dilemmas. Our framework offers new perspectives for bridging complex systems science and MARL, providing insights for the design of cooperative and resilient AI.

Organizations are increasingly adopting Participatory Reuse, an InnerSource practice where consumers of the reusable assets also contribute to their development and maintenance. While existing literature has reported various challenges, solutions, and lessons learned from organizations engaging in participatory reuse, a consolidated synthesis of these findings remains lacking. This study aims to collect and analyze the challenges, solutions, and lessons learned in participatory reuse from empirical works conducted within industrial settings. We conducted a case survey of $\mathbf{3 4}$ primary studies in $\mathbf{1 5}$ case organizations that have implemented participatory reuse practices. Based on the core characteristics of software reuse, we categorized the identified challenges, solutions, and lessons learned into three themes: developing reusable assets, using reusable assets, and managing participatory reuse. We organized the identified evidence as a catalog of participatory reuse challenges, solutions, and lessons learned. This catalog is intended to support both practitioners and researchers in identifying relevant strategies to address specific participatory reuse challenges within their organizations. We also pilot tested the catalog with four industry experts, who found it valuable in identifying solutions and lessons learned from the challenges.

Abstract Contemporary practices such as InnerSource (adopting open source practices within an organization), continuous integration and delivery (CI/CD), and the use of microservices promote software reuse. Although the benefits of individual contemporary practices on reuse may be known, the implications of collective contemporary practices, mainly challenges and improvements to mitigate the challenges, are to a large extent unknown. In this study, we investigate the additional effort (cost factors), benefits, challenges, and potential improvements in contemporary reuse at Ericsson. We performed the study in two phases: a) the initial data collection based on a combination of data collection methods (e.g., interviews, discussions, company portals), and b) a follow-up group discussion after a year to understand the status of the challenges and improvements identified in the first phase. Our results indicate that developing reusable assets resulted in upfront cost factors, such as additional effort in ensuring compliance. Furthermore, development with reuse also resulted in cost factors, for example, additional effort in integrating and understanding reusable assets. Ericsson perceived the cost factors as an investment resulting in long-term benefits such as improved quality, productivity, customer experience, and way of working. The main challenge faced by Ericsson was the pressure on the producers of reusable assets. Our study shows how InnerSource can lead to an increase in contributions to reusable assets. Furthermore, Ericsson implemented measures like automating compliance checks that improved the maturity of reusable assets, resulting in an increase in their reuse. In summary, effective use of contemporary practices such as InnerSource and CI/CD, along with the use of microservices, can facilitate large scale reuse.

Declines in vaccination coverage for vaccine-preventable diseases, such as measles and chickenpox, have enabled their surprising comebacks and pose significant public health challenges in the wake of growing vaccine hesitancy. Vaccine opt-outs and refusals are often fueled by beliefs concerning perceptions of vaccine effectiveness and exaggerated risks. Here, we quantify the impact of competing beliefs - vaccine-averse versus vaccine-neutral - on social imitation dynamics of vaccination, alongside the epidemiological dynamics of disease transmission. These beliefs may be pre-existing and fixed, or coevolving attitudes. This interplay among beliefs, behaviors, and disease dynamics demonstrates that individuals are not perfectly rational; rather, they base their vaccine uptake decisions on beliefs, personal experiences, and social influences. We find that the presence of a small proportion of fixed vaccine-averse beliefs can significantly exacerbate the vaccination dilemma, making the tipping point in the hysteresis loop more sensitive to changes in individuals' perceived costs of vaccination and vaccine effectiveness. However, in scenarios where competing beliefs spread concurrently with vaccination behavior, their double-edged impact can lead to self-correction and alignment between vaccine beliefs and behaviors. The results show that coevolution of vaccine beliefs and behaviors makes populations more sensitive to abrupt changes in perceptions of vaccine cost and effectiveness compared to scenarios without beliefs. Our work provides valuable insights into harnessing the social contagion of even vaccine-neutral attitudes to overcome vaccine hesitancy.

Software reuse costs and benefits have been investigated in several primary studies, which have been aggregated in multiple secondary studies as well. However, existing secondary studies on software reuse have not critically appraised the evidence in primary studies. Moreover, there has been relatively less focus on how software reuse costs and benefits were measured in the primary studies, and the aggregated evidence focuses more on software reuse benefits than reuse costs. This study aims to cover the gaps mentioned in the context above by synthesizing and critically appraising the evidence reported on software reuse costs and benefits from industrial cases. We used a systematic literature review (SLR) to conduct this study. The results of this SLR are based on a final set of 30 primary studies. We identified nine software reuse benefits and six software reuse costs, in which better quality and improved productivity were investigated the most. The primary studies mostly used defect-based and development time-based metrics to measure reuse benefits and costs. Regarding the reuse practices, the results show that software product lines, verbatim reuse, and systematic reuse were the top investigated ones, contributing to more reuse benefits. The quality assessment of the primary studies showed that most of them are either of low (20%) or moderate (67%) quality. Based on the number and quality of the studies, we conclude that the strength of evidence for better quality and improved productivity as reuse benefits is high. There is a need to conduct more high quality studies to investigate, not only other reuse costs and benefits, but also how relatively new reuse-related practices, such as InnerSource and microservices architecture, impact software reuse.

Background: InnerSource consists of the use of open source development techniques within the corporation. It helps improve software reuse through increased transparency and inter-team collaboration. Companies need to understand their context and specific needs before deciding to adopt any specific InnerSource practices since they cannot apply all InnerSource practices at once. Aim: This study aims to support the case company in assessing its readiness for adopting InnerSource practices to improve its internal reuse, identify and prioritize the improvement areas, and identify suitable solutions. Method: We performed a case study using a questionnaire and a workshop to check the current and desired status of adopting InnerSource practices and collect potential solutions. Results: The study participants identified that the company needs to prioritize the improvements related to the discoverability, communication channels, and ownership of the reusable assets. In addition, they identified certain InnerSource practices as solutions for the prioritized improvement areas, such as better structured repositories for storing and searching the reusable assets and standardized documentation of the reusable assets. Conclusion: The questionnaire instrument aids the case company in identifying the improvement areas related to InnerSource and reuse practices. InnerSource practices could improve the development and maintenance of reusable assets. Keywords: InnerSource, software reuse, readiness

Abstract Model-based testing (MBT) is a method that supports the design and execution of test cases by models that specify the intended behaviors of a system under test. While systematic literature reviews on MBT in general exist, the state of the art on modeling and testing performance requirements has seen much less attention. Therefore, we conducted a systematic mapping study on model-based performance testing. Then, we studied natural language software requirements specifications in order to understand which and how performance requirements are typically specified. Since none of the identified MBT techniques supported a major benefit of modeling, namely identifying faults in requirements specifications, we developed the Performance Requirements verificatiOn and Test EnvironmentS generaTion approach (PRO-TEST). Finally, we evaluated PRO-TEST on 149 requirements specifications. We found and analyzed 57 primary studies from the systematic mapping study and extracted 50 performance requirements models. However, those models don’t achieve the goals of MBT, which are validating requirements, ensuring their testability, and generating the minimum required test cases. We analyzed 77 Software Requirements Specification (SRS) documents, extracted 149 performance requirements from those SRS, and illustrate that with PRO-TEST we can model performance requirements, find issues in those requirements and detect missing ones. We detected three not-quantifiable requirements, 43 not-quantified requirements, and 180 underspecified parameters in the 149 modeled performance requirements. Furthermore, we generated 96 test environments from those models. By modeling performance requirements with PRO-TEST, we can identify issues in the requirements related to their ambiguity, measurability, and completeness. Additionally, it allows to generate parameters for test environments.