Machine learning for real-time prediction of complications induced by flexible uretero-renoscopy with laser lithotripsy

It is not always easy to predict the outcome of a surgery. Peculiarly, when talking about the risks associated to a given intervention or the possible complications that it may bring about. Thus, predicting those potential complications that may arise during or after a surgery will help minimize risks and prevent failures to the greatest extent possible. Therefore, the objectif of this article is to propose an intelligent system based on machine learning, allowing predicting the complications related to a flexible uretero-renoscopy with laser lithotripsy for the treatment of kidney stones. The proposed method achieved accuracy with 100% for training and, 94.33% for testing in hard voting, 100% for testing and 95.38% for training in soft voting, with only ten optimal features. Additionally, we were able to evaluted the machine learning model by examining the most significant features using the shpley additive explanations (SHAP) feature importance plot, dependency plot, summary plot, and partial dependency plots

An Analysis and New Methodology for Reverse Engineering of UML Behavioral

The emergence of Unified Modeling Language (UML) as a standard for modeling systems has encouraged the use of automated software tools that facilitate the development process from analysis through coding. Reverse Engineering has become a viable method to measure an existing system and reconstruct the necessary model from its original. The Reverse Engineering of behavioral models consists in extracting high-level models that help understand the behavior of existing software systems. In this paper we present an ongoing work on extracting UML diagrams from object-oriented programming languages. we propose an approach for the reverse engineering of UML behavior from the analysis of execution traces produced dynamically by an object-oriented application using formal and semi-formal techniques for modeling the dynamic behavior of a system. Our methods show that this approach can produce UML behavioral diagrams in reasonable time and suggest that these diagrams are helpful in understanding the behavior of the underlying application

Benefits of reverse engineering technologies in software development makerspace

In the recent decades, the amount of data produced by scientific, engineering, and life science applications has increased with several orders of magnitude. In parallel with this development, the applications themselves have become increasingly complex in terms of functionality, structure, and behavior. In the same time, development and production cycles of such applications exhibit a tendency of becoming increasingly shorter, due to factors such as market pressure and rapid evolution of supporting and enabling technologies. As a consequence, an increasing fraction of the cost of creating new applications and manufacturing processes shifts from the creation of new artifacts to the adaption of existing ones. A key component of this activity is the understanding of the design, operation, and behavior of existing manufactured artifacts, such as software code bases, hardware systems, and mechanical assemblies. For instance, in the software industry, it is estimated that maintenance costs exceed 80% of the total costs of a software product's lifecycle, and software understanding accounts for as much as half of these maintenance costs. To facilitate the software development process, it would be ideal to have tools that automatically generate or help to generate UML (Unified Modeling Language) models from source code. Reverse engineering the software architecture from source code provides a valuable service to software practitioners. Case tools implementing MDA and reverse-engineering constitute an important opportunity of software development engineers. So MDA and reverse engineering is an important key witch make makerspace more productive and more efficient

Benefits of reverse engineering technologies in software development makerspace

In the recent decades, the amount of data produced by scientific, engineering, and life science applications has increased with several orders of magnitude. In parallel with this development, the applications themselves have become increasingly complex in terms of functionality, structure, and behavior. In the same time, development and production cycles of such applications exhibit a tendency of becoming increasingly shorter, due to factors such as market pressure and rapid evolution of supporting and enabling technologies. As a consequence, an increasing fraction of the cost of creating new applications and manufacturing processes shifts from the creation of new artifacts to the adaption of existing ones. A key component of this activity is the understanding of the design, operation, and behavior of existing manufactured artifacts, such as software code bases, hardware systems, and mechanical assemblies. For instance, in the software industry, it is estimated that maintenance costs exceed 80% of the total costs of a software product's lifecycle, and software understanding accounts for as much as half of these maintenance costs. To facilitate the software development process, it would be ideal to have tools that automatically generate or help to generate UML (Unified Modeling Language) models from source code. Reverse engineering the software architecture from source code provides a valuable service to software practitioners. Case tools implementing MDA and reverse-engineering constitute an important opportunity of software development engineers. So MDA and reverse engineering is an important key witch make makerspace more productive and more efficient