Daniela Briola

Rafael C. Cardoso, Angelo Ferrando, Daniela Briola et al.

This volume contains the proceedings of the First Workshop on Agents and Robots for reliable Engineered Autonomy (AREA 2020), co-located with the 24th European Conference on Artificial Intelligence (ECAI 2020). AREA brings together researchers from autonomous agents, software engineering and robotic communities, as combining knowledge coming from these research areas may lead to innovative approaches that solve complex problems related with the verification and validation of autonomous robotic systems.

Oscar Cornejo, Daniela Briola, Daniela Micucci et al.

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.

Oscar Cornejo, Daniela Briola, Daniela Micucci et al.

Collecting data about the sequences of function calls executed by an application while running in the field can be useful to a number of applications, including failure reproduction, profiling, and debugging. Unfortunately, collecting data from the field may introduce annoying slowdowns that negatively affect the quality of the user experience. So far, the impact of monitoring has been mainly studied in terms of the overhead that it may introduce in the monitored applications, rather than considering if the introduced overhead can be really recognized by users. In this paper we take a different perspective studying to what extent collecting data about sequences of function calls may impact the quality of the user experience, producing recognizable effects. Interestingly we found that, depending on the nature of the executed operation and its execution context, users may tolerate a non-trivial overhead. This information can be potentially exploited to collect significant amount of data without annoying users.

Daniela Briola, Viviana Mascardi, Massimiliano Gioseffi

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. Under consideration in Theory and Practice of Logic Programming (TPLP)

Oscar Cornejo, Daniela Briola, Daniela Micucci et al.

Field data is an invaluable source of information for testers and developers because it witnesses how software systems operate in real environments, capturing scenarios and configurations relevant to end-users. Unfortunately, collecting traces might be resource-consuming and can significantly affect the user experience, for instance causing annoying slowdowns. Existing monitoring techniques can control the overhead introduced in the applications by reducing the amount of collected data, for instance by collecting each event only with a given probability. However, collecting fewer events limits the amount of information extracted from the field and may fail in providing a comprehensive picture of the behavior of a program. In this paper we present fragmented monitoring, a monitoring technique that addresses the issue of collecting information from the field without annoying users. The key idea of fragmented monitoring is to reduce the overhead by recording partial traces (fragments) instead of full traces, while annotating the beginning and the end of each fragment with state information. These annotations are exploited offline to derive traces that might be likely observed in the field and that could not be collected directly due to the overhead that would be introduced in a program.

Oscar Cornejo, Daniela Briola, Daniela Micucci et al.

Monitoring techniques can extract accurate data about the behavior of software systems. When used in the field, they can reveal how applications behave in real-world contexts and how programs are actually exercised by their users. Nevertheless, since monitoring might need significant storage and computational resources, it may interfere with users activities degrading the quality of the user experience. While the impact of monitoring has been typically studied by measuring the overhead that it may introduce in a monitored application, there is little knowledge about how monitoring solutions may actually impact on the user experience and to what extent users may recognize their presence. In this paper, we present our investigation on how collecting data in the field may impact the quality of the user experience. Our initial results show that non-trivial overhead can be tolerated by users, depending on the kind of activity that is performed. This opens interesting opportunities for research in monitoring solutions, which could be designed to opportunistically