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

Automatic Repair of Buggy If Conditions and Missing Preconditions with SMT

We present Nopol, an approach for automatically repairing buggy if conditions and missing preconditions. As input, it takes a program and a test suite which contains passing test cases modeling the expected behavior of the program and at least one failing test case embodying the bug to be repaired. It consists of collecting data from multiple instrumented test suite executions, transforming this data into a Satisfiability Modulo Theory (SMT) problem, and translating the SMT result -- if there exists one -- into a source code patch. Nopol repairs object oriented code and allows the patches to contain nullness checks as well as specific method calls.Comment: CSTVA'2014, India (2014

Optimizing Test Cases for Object-Oriented Software

- Testing object-oriented software is a challenging task. The inherent complexity in testing Object-oriented software is due to issues like inheritance and polymorphism. The behavior analysis and testing of object oriented software is significantly complicated because the state of the objects may cause faults that cannot be easily revealed with traditional testing techniques. This article proposes an improved technique for generating optimal number of test cases using mathematical techniques. The technique uses Colored Petri Nets (CPN), which is an extended version of Petri Nets. CPN’s are usually used for system modeling and simulation. The proposed method explores the problem to generate test cases that covers all instances of objects from different classes in the same hierarchy. It shows the effectiveness of technique by translating a specification represented by UML (unified modeling language) state chart into a CPN. The main solution of our approach will be implemented using CPN-tools

Modeling and simulation of continuous fiber-reinforced ceramic composites

Finite element modeling framework based on cohesive damage modeling, constitutive material behavior using user-material subroutines, and extended finite element method (XFEM), are developed for studying the failure behavior of continuous fiber-reinforced ceramic matrix composites (CFCCs) by the example of a silicon carbide matrix reinforced with silicon carbide fiber (SiC/SiCf) composite. This work deals with developing comprehensive numerical models for three problems: (1) fiber/matrix interface debonding and fiber pull-out, (2) mechanical behavior of a CFCC using a representative volume element (RVE) approach, and (3) microstructure image-based modeling of a CFCC using object oriented finite element analysis (OOF). Load versus displacement behavior during a fiber pull-out event was investigated using a cohesive damage model and an artificial neural network model. Mechanical behavior of a CFCC was investigated using a statistically equivalent RVE. A three-step procedure was developed for generating a randomized fiber distribution. Elastic properties and damage behavior of a CFCC were analyzed using the developed RVE models. Scattering of strength distribution in CFCCs was taken into account using a Weibull probability law. A multi-scale modeling framework was developed for evaluating the fracture behavior of a CFCC as a function of microstructural attributes. A finite element mesh of the microstructure was generated using an OOF tool. XFEM was used to study crack propagation in the microstructure and the fracture behavior was analyzed. The work performed provides a valuable procedure for developing a multi-scale framework for comprehensive damage study of CFCCs --Abstract, page iv

True-false lumen segmentation of aortic dissection using multi-scale wavelet analysis and generative-discriminative model matching

Computer aided diagnosis in the medical image domain requires sophisticated probabilistic models to formulate quantitative behavior in image space. In the diagnostic process detailed knowledge of model performance with respect to accuracy, variability, and uncertainty is crucial. This challenge has lead to the fusion of two successful learning schools namely generative and discriminative learning. In this paper, we propose a generative-discriminative learning approach to predict object boundaries in medical image datasets. In our approach, we perform probabilistic model matching of both modeling domains to fuse into the prediction step appearance and structural information of the object of interest while exploiting the strength of both learning paradigms. In particular, we apply our method to the task of true-false lumen segmentation of aortic dissections an acute disease that requires automated quantification for assisted medical diagnosis. We report empirical results for true-false lumen discrimination of aortic dissection segmentation showing superior behavior of the hybrid generative-discriminative approach over their non hybrid generative counterpart