Models@Runtime: The Development and Re-Configuration Management of Python Applications Using Formal Methods

Abstract

Models@runtime (models at runtime) are based on computation reflection. Runtime models can be regarded as a reflexive layer causally connected with the underlying system. Hence, every change in the runtime model involves a change in the reflected system, and vice versa. To the best of our knowledge, there are no runtime models for Python applications. Therefore, we propose a formal approach based on Petri Nets (PNs) to model, develop, and reconfigure Python applications at runtime. This framework is supported by a tool whose architecture consists of two modules connecting both the model and its execution. The proposed framework considers execution exceptions and allows users to monitor Python expressions at runtime. Additionally, the application behavior can be reconfigured by applying Graph Rewriting Rules (GRRs). A case study using Service-Level Agreement (SLA) violations is presented to illustrate our approach.

Service request monitoring using Petri nets and model at runtime

“…We focus on the PN formalism used in this work to model a service request. Then, we explain the notion of model@runtime, and we present the architecture of our model at-runtime framework introduced in our previous work [16]. This framework is used to develop the approach presented in this work.…”

“…The transitions represent real actions that calculate a student grade. Therefore, models@runtime will support running systems and decision-making by many things, such as reconfiguring systems [16]. This paradigm offers a model reflection of the running system at runtime.…”

“…It is based on computation reflection, in which the system state is reflected on the model and vice versa. In our previous work [16], we developed a model at-runtime framework that models Python applications at runtime using an extension of the PN formalism. In this work, we use this framework to model and monitor a service request.…”

“…• We implement the proposed model using the model atruntime framework proposed in our previous work [16]. This framework allows the monitoring of service requests.…”

Determinants of Non-Performing Loans (NPLs); Evidence from Banking Sector of Pakistan

“…Banking sector is the chief network for providing credit facility to numerous commercial segments (Kamran et al, 2018;Kamel, 2005;Richard, 2011;Rehman, Adzis, & Mohamed Arshad, 2020). However, it cannot be ignored that the reliability and performance of the banking sector are measured through its earning steadiness (Kim et al, 2019;Ghosh, 2017;Yin, 2019), enhancements in the profitability (Galdeano et al, 2019Trujillo-Ponce, 2013Tan et al, 2017), growing the volume of funds from investors to debtors (Jakab & Kumhof, 2015), and facility of eminence services for their clients (Bouhamed, Díaz, Chaoui, Kamel, & Nouara, 2021;Panigrahi, Azizan, & Waris, 2018;Rafiq & Siddiqui, 2018;Zameer, Tara, Kausar, & Mohsin, 2015). Though, risk administration practices are the gauge for the general performance of the bank.…”