Mutation Testing for Jason Agents

Li, Mingguang; Alexander, Robert; Clark, John A.

doi:10.1007/978-3-319-14484-9_16

Cited by 8 publications

(6 citation statements)

References 14 publications

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“…In [118], mutation testing is used to test Jason specifications. The authors propose a set of mutation operators for Jason, and present a mutation testing framework for individual Jason agents based on these mutation operators.…”

Section: Applications Of Standard Testing Techniques From Software Engineeringmentioning

confidence: 99%

Agents and Robots for Reliable Engineered Autonomy:A Perspective from the Organisers of AREA 2020

Cardoso

Ferrando

Briola

et al. 2021

JSAN

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Multi-agent systems, robotics and software engineering are large and active research areas with many applications in academia and industry. The First Workshop on Agents and Robots for reliable Engineered Autonomy (AREA), organised the first time in 2020, aims at encouraging cross-disciplinary collaborations and exchange of ideas among researchers working in these research areas. This paper presents a perspective of the organisers that aims at highlighting the latest research trends, future directions, challenges, and open problems. It also includes feedback from the discussions held during the AREA workshop. The goal of this perspective is to provide a high-level view of current research trends for researchers that aim at working in the intersection of these research areas.

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Section: Applications Of Standard Testing Techniques From Software Engineeringmentioning

confidence: 99%

Agents and Robots for Reliable Engineered Autonomy:A Perspective from the Organisers of AREA 2020

Cardoso

Ferrando

Briola

et al. 2021

JSAN

View full text Add to dashboard Cite

show abstract

“…A simulation-based testing process can be improved by using tests that not only stimulate the system but can also find faults, using so-called mutation-based test generation (Huang et al, 2014b). A system's safety and liveness requirements model, crucial in a V&V task, can also be verified for consistency, correctness, and completeness, e.g., using a combination of formal methods, static analysis, and simulation, as in Heitmeyer (2007).…”

Section: Automated Software Toolsmentioning

confidence: 99%

A corroborative approach to verification and validation of human–robot teams

Webster

Western

Araiza-Illan

et al. 2019

The International Journal of Robotics Research

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We present an approach for the verification and validation (V&V) of robot assistants in the context of human-robot interactions (HRI), to demonstrate their trustworthiness through corroborative evidence of their safety and functional correctness. Trust in robot assistants will allow them to transition from the laboratory into our everyday lives. Key challenges include the complex and unpredictable nature of the real world in which assistant and service robots operate, the limitations on available V&V techniques when used individually, and the consequent lack of confidence in the V&V results. Our approach, called corroborative V&V, addresses these challenges by combining several different V&V techniques; in this paper we use formal verification (model checking), simulation-based testing, and user validation in experiments with a real robot. We demonstrate our corroborative V&V approach through a handover task, the most critical part of a complex cooperative manufacturing scenario, for which we propose some safety and liveness requirements to verify and validate. We construct formal models, simulations and an experimental test rig for the HRI. To capture requirements we use temporal logic properties, assertion checkers and textual descriptions. This combination of approaches allows V&V of the HRI task at different levels of modelling detail and thoroughness of exploration, thus overcoming the individual limitations of each technique. Should the resulting V&V evidence present discrepancies, an iterative process between the different V&V techniques takes place until corroboration between the V&V techniques is gained from refining and improving the assets (i.e., system and requirement models) to represent the HRI task in a more truthful manner. Therefore, corroborative V&V affords a systematic approach to "'meta-V&V," in which different V&V techniques can be used to corroborate and check one another, increasing the level of certainty in the results of V&V.

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“…In the first work [25], the authors propose a testing approach based on mutation analysis to test the goals of an agent. In the second work [26], the authors propose error patterns to test the agent of the platform Jason.…”

Section: Introductionmentioning

confidence: 99%

A Novel Mutation Analysis-Based Approach for Testing Parallel Behavioural Scenarios in Multi-Agent Systems

2022

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In this work, we propose a new test case generation approach that can cover behavioural scenarios individually in a multi-agent system. The purpose is to identify, in the case of the detection of an error, the scenario that caused the detected error, among the scenarios running in parallel. For this, the approach used, in the first stage, the technique of mutation analysis and parallel genetic algorithms to identify the situations in which the agents perform the interactions, presented in the sequence diagram, of the scenario under test only; these situations will be considered as inputs of the test case. In the second stage, the approach used the activities presented in the activity diagram to identify the outputs of the test case expected for its inputs. Subsequently, the generated test cases will be used for the detection of possible errors. The proposed approach is supported by a formal framework in order to automate its phases, and it is applied to a concrete case study to illustrate and demonstrate its usefulness.

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Mutation Testing for Jason Agents

Cited by 8 publications

References 14 publications

Agents and Robots for Reliable Engineered Autonomy:A Perspective from the Organisers of AREA 2020

Agents and Robots for Reliable Engineered Autonomy:A Perspective from the Organisers of AREA 2020

A corroborative approach to verification and validation of human–robot teams

A Novel Mutation Analysis-Based Approach for Testing Parallel Behavioural Scenarios in Multi-Agent Systems

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