Autonomous systems, such as drones and rescue robots, are increasingly used during emergencies. They deliver services and provide situational awareness that facilitate emergency management and response. To do so, they need to interact and cooperate with humans in their environment. Human behaviour is uncertain and complex, so it can be difficult to reason about it formally. In this article, we propose IDEA: an adaptive software architecture that enables cooperation between humans and autonomous systems, by leveraging the social identity approach. This approach establishes that group membership drives human behaviour. Identity and group membership are crucial during emergencies, as they influence cooperation among survivors. IDEA systems infer the social identity of surrounding humans, thereby establishing their group membership. By reasoning about groups, we limit the number of cooperation strategies the system needs to explore. IDEA systems select a strategy from the equilibrium analysis of game-theoretic models that represent interactions between group members and the IDEA system. We demonstrate our approach using a search-and-rescue scenario, in which an IDEA rescue robot optimises evacuation by collaborating with survivors. Using an empirically validated agent-based model, we show that the deployment of the IDEA system can reduce median evacuation time by 13.6%.

We explore the role of reciprocity in code review processes. Reciprocity manifests itself in two ways: 1) reviewing code for others translates to accepted code contributions, and 2) having contributions accepted increases the reviews made for others. We use vector autoregressive (VAR) models to explore the causal relation between reviews performed and accepted contributions. After fitting VAR models for 24 active open-source developers, we found evidence of reciprocity in 6 of them. These results suggest reciprocity does play a role in code review, that can potentially be exploited to increase reviewer participation.

Priority inflation occurs when a Quality-Assurance (QA) engineer or a project manager requesting a feature inflates the priority of their task so that developers deliver the fix or the new functionality more quickly. We survey developers and show that priority inflation occurs and misallocates developer time. We are the first to apply empirical game-theoretic analysis (EGTA) to a software engineering problem, specifically priority inflation. First, we extract prioritisation strategies from 42,620 issues from Apache's JIRA, then useTaskAssessor, our EGTA-based modelling approach, to confirm conventional wisdom and show that the common process of a QA engineer assigning priority labels is susceptible to priority inflation. We then show that the common mitigation strategy of having a bug triage team assigning priorities does not resolve priority inflation and slows development. We then use mechanism design to deviseassessor-throttling, a new, lightweight prioritization process, immune to priority inflation. We show that assessor-throttling resolves 97 percent of high priority tasks, 69 percent better than simply relying on those filing tasks to assign priorities. Finally, we present The Fed, a browser extension for Chrome that supports assessor-throttling.