In both academia and industry, multiagent systems (MASs) are increasingly employed to model and implement complex distributed applications, due to their natural alignment with intelligent, autonomous, and interactive entities. However, the development of large-scale MAS-based solutions introduces challenges related to maintainability, testability, and performance. Real-world deployments involving numerous agents and distributed devices require thorough testing under conditions that closely approximate the final operational environment, but such testing can be costly in terms of both computational and physical resources. While MAS simulation tools could alleviate this burden, their employment can be tricky and time consuming. In this paper, we introduce SimJade, a simulation framework based on the PeerSim peer-to-peer simulator, designed to execute on a single machine physically distributed JADE systems, without requiring multiple JADE installations or the presence of physical devices. SimJade enables the simulation of complex multi-platform agent-based systems without requiring any modification to the original JADE source code, thereby allowing the testing of a realistic emulation of field deployments. JADE was selected as the reference platform due to its widespread adoption, particularly in industrial contexts where reliable and scalable testing is critical. We demonstrate the effectiveness of SimJade through a series of evaluations involving a real-world MAS intended for deployment across thousands of end-user devices, reporting promising results in terms of scalability and fidelity.

Failure data collected from the field (e.g., failure traces, bug reports, and memory dumps) represent an invaluable source of information for developers who need to reproduce and analyze failures. Unfortunately, field data may include sensitive information and thus cannot be collected indiscriminately. Privacy-preserving techniques can address this problem anonymizing data and reducing the risk of disclosing personal information. However, collecting anonymized information may harm reproducibility, that is, the anonymized data may not allow the reproduction of a failure observed in the field. In this paper, we present an empirical investigation about the impact of privacy-preserving techniques on the reproducibility of failures. In particular, we study how five privacy-preserving techniques may impact reproducibilty for 19 bugs in 17 Android applications. Results provide insights on how to select and configure privacy-preserving techniques.

Bike-sharing systems (BSSs) have become integral to urban mobility, improving accessibility, multimodality of transportation, and sustainability. This paper presents a novel approach to supporting decisions on the positioning of docking stations for dock-based BSSs by leveraging real-world historical mobility data to estimate mobility demand. In particular, we used taxi travels data as a proxy to generate a synthetic mobility demand dataset that was exploited to estimate the locations of a dock-based BSS stations through clustering techniques. This study aims to improve the practicality of station positioning. By addressing challenges related to station placement, this research offers insights into the practical implementation of data-driven approaches in BSS planning and management, advancing the efficiency and sustainability of urban bike-sharing systems.

Abstract Cellular Automata have successfully been successfully applied to the modeling and simulation of pedestrian and crowd dynamics. In particular, the investigated scenarios have often been focused on the evaluation of medium–high population density situations, in which the motivation of pedestrians to reach a certain location overcomes their tendency to naturally respect proxemic distances. The global COVID-19 outbreak, though, has shown that sometimes it is crucial to contemplate how proxemic tendencies are emphasized and amplified by the affective state of the individuals involved in the scenario, representing an important factor to take into consideration when investigating the behaviour of a crowd. In this paper we present a research effort aimed at integrating results of quantitative analyses regarding the effects of affective states on the perception of distances maintained by different types of pedestrians with the modeling of pedestrian movement choices in a cellular automata framework.

Abstract Collecting and analyzing data about developers working on their development tasks can help improve development practices, finally increasing the productivity of teams. Indeed, monitoring and analysis tools have already been used to collect data from productivity tools. Monitoring inevitably consumes resources and, depending on their extensiveness, may significantly slow down software systems, interfering with developers’ activity. There is thus a challenging trade-off between monitoring and validating applications in their operational environment and preventing the degradation of the user experience. The lack of studies about when developers perceive an overhead introduced in an application makes it extremely difficult to fine-tune techniques working in the field. In this paper, we address this challenge by presenting an empirical study that quantifies how developers perceive overhead. The study consists of three replications of an experiment that involved 99 computer science students in total, followed by a small-scale experimental assessment of the key findings with 12 professional developers. Results show that non-negligible overhead can be introduced for a short period into applications without developers perceiving it and that the sequence in which complex operations are executed influences the perception of the system response time. This information can be exploited to design better monitoring techniques.

Abstract The volume at hand presents the Special Issue on “Distributed Complex Systems: Governance, Engineering, and Maintenance”. The Special Issue has been originally conceived within the context of the 2nd International Workshop on Governing Adaptive and Unplanned Systems of Systems (GAUSS 2020), one of the co‐located events of the 31st International Symposium on Software Reliability Engineering (ISSRE 2020). The authors of the best papers at GAUSS 2020 have been invited to submit an extended version of their previous work. In addition, the editors opened the submission also to all the other researches working on technical and managerial solution for governing Distributed Complex Systems. Ultimate objective of the editors with this Special Issue is to promote discussions focusing on anticipating, mitigating, or reacting to scenarios that were unplanned or under‐specified at design time.

Collecting traces from software running in the field is both useful and challenging. Traces may indeed help revealing unexpected usage scenarios, detecting and reproducing failures, and building behavioral models that reflect how the software is actually used. On the other hand, recording traces is an intrusive activity that may annoy users, negatively affecting the usability of the applications, if not properly designed.In this paper we address field monitoring by introducing Controlled Burst Recording, a monitoring solution that can collect comprehensive runtime data without compromising the quality of the user experience. The technique encodes the knowledge extracted from the monitored application as a finite state model that both represents the sequences of operations that can be executed by the users and the corresponding internal computations that might be activated by each operation.Our initial assessment with information extracted from ArgoUML shows that Controlled Burst Recording can reconstruct behavioral information more effectively than competing sampling techniques, with a low impact on the system response time.

We present OntoScene, a framework aimed at understanding the semantics of visual scenes starting from the semantics of their elements and the spatial relations holding between them. OntoScene exploits ontologies for representing knowledge and Prolog for specifying the interpretation rules that domain experts may adopt, and for implementing the SceneInterpreter engine. Ontologies allow the designer to formalize the domain in a reusable way and make the system modular and interoperable with existing multiagent systems, while Prolog provides a solid basis to define complex rules of interpretation in a way that can be affordable even for people with no background in Computational Logics. The domain selected for experimenting OntoScene is that of prehistoric rock art, which provides us with a fascinating and challenging testbed.

Summary The operational environment can be a valuable source of information about the behavior of software applications and their usage context. Although a single instance of an application has limited evidence of the range of the possible behaviors and situations that might be experienced in the field, the collective knowledge composed by the evidence gathered by the many instances of a same application running in several diverse user environments (eg, a browser) might be an invaluable source of information. This information can be exploited by applications able to autonomously analyze how they behave in the field and adjust their behavior accordingly. Augmenting applications with the capability to collaborate and directly share information about their behavior is challenging because it requires the definition of a fully decentralized and dependable networked infrastructure whose nodes are the user machines. The nodes of the infrastructure must be collaborative, to share information, and autonomous, to exploit the available information to change their behavior, for instance, to better accommodate the needs of the users to prevent known problems. This paper describes the initial results that we obtained with the design and the development of an infrastructure that can enable the execution of collaborative scenarios in a fully decentralized way. Our idea is to combine the agent‐based paradigm, which is well suited to design collaborative and autonomous nodes, and the peer‐to‐peer paradigm, which is well suited to design distributed and dynamic network infrastructures. To demonstrate our idea, we augmented the popular JADE agent‐based platform with a software layer that supports both the creation of a fully decentralized peer‐to‐peer network of JADE platforms and the execution of services within that network, thus enabling JADE multiagent systems (MASs) to behave as peer‐to‐peer networks. The resulting platform can be used to study the design of collaborative applications running in the field.

Summary This paper describes IndianaMAS, a multiagent system able to automatically classify and manage images, sketches, and multilingual documents in a cultural heritage domain. The latter has been formalized by means of an ontology, which enables the semantic integration of heterogeneous data from different sources, drives the agent communication with the internal and external environment, and provides an abstract and human‐readable interface between the system and the user. IndianaMAS is able to expose to the world the classified data via a digital library. Modularity and reusability are the key engineering principles followed in the system design and implementation. We present the details of the IndianaMAS system and discuss how its architecture can be generalized to create – with the minimal effort – systems addressing similar classification, storage, and management problems, but operating in different domains and driven by different ontologies. The concrete problems we faced and their solutions are described to share our lesson learned and, at the same time, to show the applicability and reusability of our modular approach based on ontologies, agents, and digital libraries. Copyright © 2017 John Wiley & Sons, Ltd.

This paper presents the “Ontologica” system, a forefront project born from a joint effort between university and industry in the design of advanced information systems. The aim of the project is twofold: to adopt ontologies and new technologies to manage the Centralized Traffic Control logic in a railway and all the rules making the physical elements of a rail station acting as desired; to improve the user interface with a mechanism to interact with the system using natural language queries. The first results we obtained are very promising. In this paper we discuss them, with the “Ontologica” rationale and architecture.