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Adaptive architecture based on agents for assessing a web application

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Abstract

The personalization presents a key factor in the majority of web application. It improves the user experience by serving each user according to his desires and intentions. To ensure a good personalization process, the evaluation should be considered. However, in the literature, assessing this process is considered as a secondary purpose and treated as a lowcost test before the final deployment of a web application. Besides, the majority of assessments are domain dependent. They also report performance for a short time period and for specific users. Thus, we propose a solution, called RPMAS, designed with an adaptive architecture, based on agents. It is a reference system that attempts to personalize the same services offered by the assessed web application. Then, a comparison is performed to evaluate the results of the concerned web application. Following this comparison, RPMAS proposes improvements to the evaluated web application, or makes a self-evaluation to treat its proper weaknesses. In this article, we expose the detailed architecture of the proposed solution. Indeed, our solution is composed of three layers: the Observation Layer, the Modeling and Data Processing Layer and the Prediction, Recommendation and Evaluation Layer. Each layer has several intelligent and adaptive agents. We underline the various RPMAS advantages, compared to the state of the art. Then, we provide the proof of the efficiency of our contribution when evaluating different web applications. Finally, we developed a first scenario which illustrates the assessment made by our RPMAS regarding an online library. Also, we deploy a second scenario to assess an intelligent tutoring application. Regarding the accuracy measure, the gap between RPMAS and the first application is 17.316%. For the second scenario, using the AUC measure, the gap indicates 13.09%.

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Notes

  1. Please refer on abbreviations to get the meaning of all used acronyms

  2. https://github.com/khalilbenaissa10/WSKWebApp

Abbreviations

O-Layer:

Observation Layer

MDP-Layer:

Modeling and Data Processing Layer

PRE-Layer:

Prediction, Recommendation and Evaluation Layer

RPMAS:

Referential Personalized Multi-Agent System

ConvLSTM:

Convolution Long Short Term Memory

AIDKVMN:

Augmented Input Dynamic Key Value Memory Network

AUC:

Area Under the Curve

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Authors and Affiliations

  1. LARIA Lab, National School of Computer Sciences Tunisia, Manouba, Tunisia

    Amal Trifa, Aroua Hedhili & Wided Lejouad Chaari

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  1. Amal Trifa
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  2. Aroua Hedhili
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  3. Wided Lejouad Chaari
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Correspondence to Amal Trifa.

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Trifa, A., Hedhili, A. & Chaari, W.L. Adaptive architecture based on agents for assessing a web application. Multimed Tools Appl 81, 40581–40607 (2022). https://doi.org/10.1007/s11042-022-13059-9

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  • DOI: https://doi.org/10.1007/s11042-022-13059-9

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