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.
Highlights
This work is motivated by two interrelated necessities of software development: computational reflection and change control
The main benefit of our proposal is that maintainers and developers are able to make runtime decisions to attend new incoming requirements based on the state of the running system provided by the runtime Petri Nets (PNs) marking and the Python evaluated expressions
The proposed approach was implemented as a framework supported by a tool consisting of two components: a Model Execution Engine (MEE) and a Python Execution Engine (PEE)
Summary
This work is motivated by two interrelated necessities of software development: computational reflection and change control. These problems arise from the need for the software to be adapted to the new user requirements, leading to software changes that increase the complexity unless measures are taken The interrelation between both concepts, computational reflection and change control, can be observed when we analyze the connection between the initial model designs and the software itself. The main advantage of models@runtime is the use of an abstraction of the running software in the form of a model to enable its reconfiguration by changing its behavior while the software is still running To achieve this goal, the approach must enable the technology to make reflections in both directions possible. The proposed technology can change the behavior of a running Python program to attend new incoming requirements and to inspect the new execution to analyze whether these new requirements are being satisfied.
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