FIPA-Based Semi-centralized Protocol for Negotiation

Hudaib, Amjad; Qasem, Mais Haj; Obeid, Nadim

doi:10.1007/978-3-319-67618-0_13

Cited by 6 publications

(5 citation statements)

References 15 publications

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“…They chose this protocol as it is a mature mechanism due to its wide use and continuous development. In [23], the authors propose a semi-centralized approach extending the Contract Net Protocol. Compared to a negotiation between an initiator and participants, the extension introduces an additional manager.…”

Section: Test Case Schedulingmentioning

confidence: 99%

“…An additional Coordination Agent (CA) organizes the test script allocation process. The CA bounds the communication between the TBAs and TSAs and, thus, is expected to optimize the time performance as shown in [23]. Setting a limit to the maximum number of communicating agents mitigates the risk of a communication overhead [5] due to a project-specific and possible high amount of test scripts and test benches.…”

Section: B Dynamic Execution Orderingmentioning

confidence: 99%

“…The proposed negotiation protocol (Fig. 6) is a modification of the Contract Net Protocol extended with a coordinator from [23]. For simplicity, the communication is limited to TSAs and TBAs with a matching configuration.…”

Section: B Dynamic Execution Orderingmentioning

confidence: 99%

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Framework for Flexible, Adaptive Support of Test Management by Means of Software Agents

Jordan

Maurer

Lowenberg³

et al. 2019

IEEE Robot. Autom. Lett.

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In the context of system testing on Hardware-in-the-Loop (HiL) test benches, performing functional tests on electrical control units (ECUs) comes with various challenges. Especially planning the execution of test cases for a test cycle and nightly batch runs is cumbersome. In order to support the test managers, this work proposes an adaptable and flexible test management assistance system. Therefore, a set of requirements for such an assistance system have been identified and proposed solutions from the literature have been recalled. Out of those, software agents appear to be a suitable method for building up the framework of the assistance system. The framework proposed in this paper is divided into two phases: static prioritization of test scripts and dynamic execution ordering. The agents of the former phase aggregate relevant available data from various sources to calculate a test script priority. In the latter the agents negotiate to solve the resource allocation problem for test scripts on test benches. In addition, test logs are taken into account at run-time for reprioritization. Finally, an industrial use case from the automotive industry is taken as an example to discuss the applicability and current obstacles on the way to application and qualitative evaluation of the expected benefits are discussed.

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Section: Test Case Schedulingmentioning

confidence: 99%

Section: B Dynamic Execution Orderingmentioning

confidence: 99%

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Framework for Flexible, Adaptive Support of Test Management by Means of Software Agents

Jordan

Maurer

Lowenberg³

et al. 2019

IEEE Robot. Autom. Lett.

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show abstract

“…Requirement Engineering includes a significant part named Requirement Prioritization that can be used in analysis and negotiation activities [37], [53]. Fig.…”

Section: Introductionmentioning

confidence: 99%

“…Existing requirements prioritization techniques suffer from several problems such as complexity of application, expensive in computations, lack of automation, scalability, ambiguity, and uncertainty [12], [37], [47], [53], [59]. Software initiatives might come with several risks, assumptions, and requirements that are incompatible.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Literature Review of AI-Based Software Requirements Prioritization Techniques

Anwar,

Bashir

2023

IEEE Access

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Software requirements show what the customer desires his software to do. They are the first stepping stone towards a successful software development project. With the increasing complexity of the software due to its size and feature base, it is vital to prioritize the requirements for efficient utilization of development resources. To achieve this, industrial organizations are devising new strategies and improved solutions even with the help of artificial intelligence (AI) tool set. Existing requirements prioritization techniques are human-intensive and suffer from several limitations like overlapping outcomes, scalability problems, time consumption, inaccuracy, and so on. Some of the problems can be solved with the inclusion of artificial intelligence algorithms and strategies. For this several AI-based requirements prioritization techniques have been proposed by applying Genetic Algorithms, Fuzzy Logic, Ant Colony Optimization, and Machine Learning. Literature witnesses some good review studies and surveys on conventional prioritization techniques but there exists none for AI-based techniques that identify not only their strengths but also their weaknesses, advantages of machine learning techniques over other AI-based requirements prioritization techniques, and limitations of applying AI-based techniques in requirements prioritization. This study presents a systematic literature review (SLR) of AI-based requirements prioritization approaches covering 46 papers published from 2000 to 2021. We have given this literature review a new dimension by conducting a parametric analysis of AI-based requirements prioritization techniques and we have identified these parameters after a thorough literature study. Some of the chosen parameters are generic (related to the prioritization process) and some are specific (related to AI techniques). This study has greatly helped us draw a clear line among AI-based techniques to show their domain of application to gain maximum advantage. Our findings will assist researchers, requirement analysts, and other stakeholders in making a wise decision to select the best requirements prioritization technique to gain optimal results.

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A Brokering-Based Interaction Protocol for Dynamic Resource Allocation in Steel Production Processes

Iannino

Mocci

Colla

2021

Advances in Intelligent Systems and Computing

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FIPA-Based Semi-centralized Protocol for Negotiation

Cited by 6 publications

References 15 publications

Framework for Flexible, Adaptive Support of Test Management by Means of Software Agents

Framework for Flexible, Adaptive Support of Test Management by Means of Software Agents

A Systematic Literature Review of AI-Based Software Requirements Prioritization Techniques

A Brokering-Based Interaction Protocol for Dynamic Resource Allocation in Steel Production Processes

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