Delivery robots are an emerging technology that promise benefits (reducing reliance on human-driven delivery vehicles and minimizing the impact of local traffic conditions). One limitation of such robots is their restricted range. A potential solution is to integrate these robots into existing public transport (PT) systems. One underexplored direction is extending their mobility through PT like buses. This scenario introduces distinct human–robot interaction (HRI) challenges: close physical proximity, shifting attention, implicit social norms, and multiple stakeholders occupying the same confined space.

Reducing energy consumption of software is an increasingly important objective, and there has been extensive research for data centers, smartphones, and embedded systems. However, when it comes to software, we lack working tools and methods to directly reduce energy consumption. For performance, we can resort to configuration options for tuning response time or throughput of a software system. For energy, it is still unclear whether the underlying assumption that runtime performance correlates with energy consumption holds, especially when it comes to optimization via configuration. To evaluate whether and to what extent this assumption is valid for configurable software systems, we conducted the largest empirical study of this kind to date. First, we searched the literature for reports on whether and why runtime performance correlates with energy consumption. We obtained a mixed, even contradicting picture from positive to negative correlation, and that configurability has not been considered yet as a factor for this variance. Second, we measured and analyzed both the runtime performance and energy consumption of 14 real-world software systems. We found that, in many cases, it depends on the software system's configuration whether runtime performance and energy consumption correlate and that, typically, only few configuration options influence the degree of correlation. A fine-grained analysis at the function level revealed that only few functions are relevant to obtain an accurate proxy for energy consumption and that, knowing them, allows one to infer individual transfer factors between runtime performance and energy consumption.

Many modern software systems are highly configurable, allowing the user to tune them for performance and more. Current performance modeling approaches aim at finding performance-optimal configurations by building performance models in a black-box manner. While these models provide accurate estimates, they cannot pinpoint causes of observed performance behavior to specific code regions. This does not only hinder system understanding, but it also complicates tracing the influence of configuration options to individual methods. We propose a white-box approach that models configuration-dependent performance behavior at the method level. This allows us to predict the influence of configuration decisions on individual methods, supporting system understanding and performance debugging. The approach consists of two steps: First, we use a coarse-grained profiler and learn performance-influence models for all methods, potentially identifying some methods that are highly configuration-and performance-sensitive, causing inaccurate predictions. Second, we re-measure these methods with a fine-grained profiler and learn more accurate models, at higher cost, though. By means of 9 real-world Java software systems, we demonstrate that our approach can efficiently identify configuration-relevant methods and learn accurate performance-influence models.