Scenario-based testing from UML/OCL behavioral models

We present in this article a way to produce test suites applied to the POSIX mini-challenge based on a behavioral model of a file system manager written in UML/OCL. We illustrate the limitations of a fully automated test generation approach, which justifies the use of test scenarios as a complement to a functional testing approach. Scenarios are expressed through regular expressions describing sequences of operations, possibly punctuated by intermediate states that have to be reached by the execution of the model. Scenarios are unfolded into extended sequences of operations that are played on the model using symbolic animation techniques. We experimented our approach by testing the conformance of two different file systems w.r.t. the POSIX standard: a recent Linux distribution and a customized Java implementation of POSIX used to evaluate the relevance of our approach and its complementarity with a structural test generation approach.     

Demons非刚性配准算法拓扑保持性的研究

在基于配准的图像分割应用中, 拓扑保持性是非刚性图像配准算法的一个重要约束. 本文从矢量场特性出发, 分析了Demons非刚性图像配准算法导致目标拓扑改变时变形场的特点. 根据变形场特点与其雅可比行列式之间的关系, 给出了校正该算法拓扑保持性的方法. 实验表明, 改进后的变形场具有了拓扑保持性.     

A tabu search with an oscillation strategy for the discriminant analysis problem

This article proposes a tabu search approach to solve a mathematical programming formulation of the linear classification problem, which consists of determining an hyperplane that separates two groups of points as well as possible in ?^m. The tabu search approach proposed is based on a non-standard formulation using linear system infeasibility. The search space is the set of bases defined on the matrix that describes the linear system. The moves are performed by pivoting on a specified row and column. On real machine learning databases, our approach compares favorably with implementations based on parametric programming and irreducible infeasible constraint sets. Additional computational results for randomly generated instances confirm that our method provides a suitable alternative to the mixed integer programming formulation that is solved by a commercial code when the number of attributes m increases.     

On the Length and Area Regularization for Multiphase Level Set Segmentation

In this paper we introduce novel regularization techniques for level set segmentation that target specifically the problem of multiphase segmentation. When the multiphase model is used to obtain a partitioning of the image in more than two regions, a new set of issues arise with respect to the single phase case in terms of regularization strategies. For example, if smoothing or shrinking each contour individually could be a good model in the single phase case, this is not necessarily true in the multiphase scenario.     

Integrating customer's preferences in the QFD planning process using a combined benchmarking and imprecise goal programming model

Quality function deployment (QFD) is a customer-oriented design tool for developing new or improved products to achieve higher customer satisfaction by integrating various functions of an organization. The engineering characteristics (ECs) affecting the product performances are designed to match the customer attributes (CAs). However, from the viewpoint of the QFD team, product design processes are performed in imprecise environments, and more than one factor must be taken into account in determining the target levels of ECs, especially the limited resources and increased market competition. This paper presents an imprecise goal programming (GP) approach to determine the optimum target levels of ECs in QFD for maximizing customer satisfaction under resource limitation and considerations of market competition. Based on benchmarking data of CAs, the concept of satisfaction functions is utilized to formulate explicitly the customer's preferences and to integrate the competitive analysis of target market into the modelling and solution process. In addition, the relationships linking CAs and ECs and the ECs to each other are integrated by functional relationships. The proposed approach will be illustrated through a car door design example.     

Multiobjective evolutionary clustering of Web user sessions: a case study in Web page recommendation

In this study, we experiment with several multiobjective evolutionary algorithms to determine a suitable approach for clustering Web user sessions, which consist of sequences of Web pages visited by the users. Our experimental results show that the multiobjective evolutionary algorithm-based approaches are successful for sequence clustering. We look at a commonly used cluster validity index to verify our findings. The results for this index indicate that the clustering solutions are of high quality. As a case study, the obtained clusters are then used in a Web recommender system for representing usage patterns. As a result of the experiments, we see that these approaches can successfully be applied for generating clustering solutions that lead to a high recommendation accuracy in the recommender model we used in this paper.     

Toward a wider use of formal methods for aerospace systems design and verification

Nowadays, it is well accepted that the development of critical systems involves the use of formal methods. One of the major fields where these methods made a lot of progress are the avionics, aerospace and more generally transport areas. Several methods, tools and techniques have been applied for the development of such systems in different parts of the world, and they have been actually put into practice during the development of specific aircraft programs. The aim of this introductory article and of this STTT special issue is to propose a brief overview of the state-of-the-art of current researches in formal methods applied to the development of avionics and aerospace systems and more broadly to take stock of the integration of formal methods in the aerospace industry.     

Principles of adjustable autonomy: a framework for resilient human–machine cooperation

Unmanned ground vehicles tend to be more and more autonomous, but both complete teleoperation and full autonomy are not efficient enough to deal with all possible situations. To be efficient, the human–robot system must be able to anticipate, react and recover from errors of different kinds, i.e., to be resilient. From this observation, this paper proposes a survey on the resilience of a human–machine system and the means to control the resilience. The resilience of a system can be defined as the ability to maintain or recover a stable state when subject to disturbance. Adjustable autonomy and human–machine cooperation are considered as means of resilience for the system. This paper then proposes three indicators to assess different meanings of resilience of the system: foresight and avoidance of events, reaction to events and recovery from occurrence of events. The third of these metrics takes into consideration the concept of affordances that allows a common representation for the opportunities of action between the automated system and its environment.     

Bond graph based control of a three-phase inverter with LC filter – Connection to passive and active loads

This paper proposes a model of a three phase electrical inverter with a LC output filter in delta connection used in a renewable energy supply system. The concept of inverse bond graph via bicausality is used for the control law design. The control law robustness is tested by connecting passive and active (induction machine) loads.     

Randomized constraint solvers: a comparative study

The complexity of constraints is a major obstacle for constraint-based software verification. Automatic constraint solvers are fundamentally incomplete: input constraints often build on some undecidable theory or some theory the solver does not support. This paper proposes and evaluates several randomized solvers to address this issue. We compared the effectiveness of a symbolic solver (CVC3), a random solver, two heuristic search solvers, and seven hybrid solvers (i.e. mix of random, symbolic, and heuristic solvers). We evaluated the solvers on a benchmark generated with a concolic execution of 9 subjects. The performance of each solver was measured by its precision, which is the fraction of constraints that the solver can find solution out of the total number of constraints that some solver can find solution. As expected, symbolic solving subsumes the other approaches for the 4 subjects that only generate decidable constraints. For the remaining 5 subjects, which contain undecidable constraints, the hybrid solvers achieved the highest precision (fraction of constraints that a solver can find a solution out of the total number of satisfiable constraints). We also observed that the solvers were complementary, which suggests that one should alternate their use in iterations of a concolic execution driver.