Model based testing for agent systems

Although agent technology is gaining world wide popularity, a hindrance to its uptake is the lack of proper testing mechanisms for agent based systems. While many traditional software testing methods can be generalized to agent systems, there are many aspects that are different and which require an understanding of the underlying agent paradigm. In this paper we present certain aspects of a testing framework that we have developed for agent based systems. The testing framework is a model based approach using the design models of the Prometheus agent development methodology. In this paper we focus on unit testing and identify the appropriate units, present mechanisms for generating suitable test cases and for determining the order in which the units are to be tested, present a brief overview of the unit testing process and an example. Although we use the design artefacts from Prometheus the approach is suitable for any plan and event based agent system

Using proof failures to help debugging MAS

International audienceFor several years, we have worked on the usage of theorem proving techniques to validate Multi-Agent Systems. In this article, we present a preliminary case study, that is part of larger work whose long-term goal is to determine how proof tools can be used to help to develop error-free Multi-Agent Systems. This article describes how an error caused by a synchronisation problem between several agents can be identied by a proof failure. We also show that analysing proof failures can help to nd bugs that may occur only in a very particular context, which makes it dicult to analyse by standard debugging techniques. 1 Introduction This article takes place in the general context of the validation of Multi-Agents Systems, and more specically in the tuning stage. Indeed, for several years now, we have worked on the validation of MAS thanks to proof techniques. This is why the designed the GDT4MAS model (Mermet and Simon, 2009) has been designed, which provides both formal tools to speciy Multi-Agent Systems and a proof system that generates automatically , from a formal specication, a set of Proof Obligations that must be proven to guarantee the correctness of the system. In the same time, we have begun to study how to answer to the following question: What happens if the theorem prover does not manage to carry out the proof ?. More precisely, is it possible to learn anything from this failures (that we call in the sequel proof failures), in order to de-bug the MAS ? Answering to this question in a general context is tricky. Indeed, a rst remark is that a proof failure may occur in three dierent cases: • rst case: a true theorem is not provable (Gödel Incompleteness Theorem). Indeed, theorems generated by GDT4MAS are rst-order logic formulae, with arithmetic, which is typically the contexy where Gödel has established that there are non provable true theorems; • second case: a true theorem can not be automatically proven by the prover because rst-ordre logic is semidecidable. It means that there is not any automatic strategy that can prove all probable theorems. An ad hoc strategy must be provided by an expert. • third case: an error in the MAS specication has led to generate a false theorem that, hence, cannot be proven. So, when a proof failure is considered, the rst problem is to determine the case it corresponds to. It would be rather long and o-topic to give complete explanations here. However, it is important to knwow that the proof system has been designed to generate theorems that have good chances to be proven by standard strategies of provers, without requiring the expertise of a human. Moreover, unprovable true theorems generally do not correspond to real cases. Thus, in most cases, a proof failure will correspond to a mistake in the specication, and this is the context that is considered in the sequel. The subject of our study is then the following: if some generated proof obligations are note proven automatically, can we learn from that in order to help to correct the specication of the MAS ? So, the main idea is to check wether proof failures can be used to detect, even correct bugs in the specication of the MAS. Indeed, contrary to what is presented in (Das-tani and Meyer, 2010), where authors conside

Scenarios for system requirements traceability and testing

Scenarios in current design methodologies, provide a natural way for the users to identify the inputs and outputs of the system revolving around a particular interaction process. A scenario typically consists of a sequence of steps which captures a particular run of the system and satisfies some aspect of the requirements. In this work we add additional structure to the scenarios used in the Prometheus agent development methodology. This additional structure then facilitates both traceability and automated testing. We describe our process for mapping the scenarios and their steps to the initial detailed design, where we then maintain the traceability as the design develops. The structured action lists that we define for both scenarios and their variations provides the basis for facilitating automated testing of system behavior. We describe how we use the newly defined structure within the scenarios to facilitate testing, describing how we automate test case generation, execution and analysis

An Approach to Model Based Testing of Multiagent Systems

Autonomous agents perform on behalf of the user to achieve defined goals or objectives. They are situated in dynamic environment and are able to operate autonomously to achieve their goals. In a multiagent system, agents cooperate with each other to achieve a common goal. Testing of multiagent systems is a challenging task due to the autonomous and proactive behavior of agents. However, testing is required to build confidence into the working of a multiagent system. Prometheus methodology is a commonly used approach to design multiagents systems. Systematic and thorough testing of each interaction is necessary. This paper proposes a novel approach to testing of multiagent systems based on Prometheus design artifacts. In the proposed approach, different interactions between the agent and actors are considered to test the multiagent system. These interactions include percepts and actions along with messages between the agents which can be modeled in a protocol diagram. The protocol diagram is converted into a protocol graph, on which different coverage criteria are applied to generate test paths that cover interactions between the agents. A prototype tool has been developed to generate test paths from protocol graph according to the specified coverage criterion

インタラクションに注目したマルチエージェントシステムの効率的な開発手法

マルチエージェントシステム(MAS) の開発の課題の一つは，インタラクションを適切に開発することが難しいことである．そこで，本研究ではインタラクションを一つのソフトウェアモジュールとして表現する記述言語IOM/T を提案する．IOM/T は，実装したインタラクションとシーケンス図による設計との等価性をπ計算を用いて可能にする．また．インタラクションに対する契約による設計の拡張や，インタラクションをソフトウェアモジュールと見做した単体テスト手法についても述べる．さらに，近年普及してきたアジャイル開発でMASを開発する場合において，インタラクションに注目して，要求(ユーザストーリ) から設計を導く手法について示す．IOM/T により，これまでのMAS 開発の課題が解決するのみならず，より堅牢性を高めたり，より容易に開発することが可能となる．電気通信大学201

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

From MDPI via Jisc Publications RouterHistory: accepted 2021-05-13, pub-electronic 2021-05-14Publication status: PublishedFunder: Engineering and Physical Sciences Research Council; Grant(s): EP/R026092, EP/R026173, EP/R026084, 694277Multi-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

Agents and Robots for Reliable Engineered Autonomy

This book contains the contributions of the Special Issue entitled "Agents and Robots for Reliable Engineered Autonomy". The Special Issue was based on the successful first edition of the "Workshop on Agents and Robots for reliable Engineered Autonomy" (AREA 2020), co-located with the 24th European Conference on Artificial Intelligence (ECAI 2020). The aim was to bring together researchers from autonomous agents, as well as software engineering and robotics communities, as combining knowledge from these three research areas may lead to innovative approaches that solve complex problems related to the verification and validation of autonomous robotic systems