Reliability of today’s software systems hinges on developers writing test cases that exercise as much of a program as possible. Writing and running such tests is inevitably subject to a time budget. This paper addresses the question of how to maximize quality of testing, given such a fixed time budget. We define a program-path scoring metric along with a way to measure a software component’s re...

For safety-critical systems testing the justification of test case selections is mandatory. In the case of systems under test (SUT) with large data types (floats, doubles etc.) test cases are identified using the equivalence class partitioning principle. While this heuristic is useful for reducing the otherwise infeasible number of test cases to be performed, justification of the class partitio...

A lot of the created AI Systems nd their end in the "wastebasket of academic solutions". The authors are convinced, that the main reason of this dilemma is the fact that nobody can really prove the validity of his system. This is no surprise, because expectations of users usually can't be formalized. One way out of this dilemma is using a test case methodology for system validation. The paper p...

A strategy for automatic test case selection based on the use of a similarity function is presented. Test case selection is a crucial activity to model-based testing since the number of automatically generated test cases is usually enormous and possibly unfeasible. Also, a considerable number of test cases are redundant, that is, they exercise similar features of the application and/or are capa...

This paper introduces two new methods for label ranking based on a probabilistic model of ranking data, called the Plackett-Luce model. The idea of the first method is to use the PL model to fit locally constant probability models in the context of instance-based learning. As opposed to this, the second method estimates a global model in which the PL parameters are represented as functions of t...

This paper presents preliminary work on the segmentation of Computed Tomography data using a model-based approach. Conventional image processing of CT data is aimed at the production of simple iso-surfaces for surgical planning or diagnosis — such methods are not suitable for the automated detection of fractures, which is the ultimate application of our work. To address these deficiencies a sur...

This paper proposes a conceptual framework for the reliability assessment of software components that incorporates test case execution and output evaluation. Determining an operational profile and test output evaluation are two difficult and important problems that must be addressed in such a framework. Determining an operational profile is difficult, because it requires anticipating the future...

G∀ST is a fully automatic test system. Given a logical property, stated as a function, it is able to generate appropriate test values, to execute tests with these values, and to evaluate the results of these tests. Many reactive systems, like automata and protocols, however, are specified by a model rather than in logic. There exist tools that are able to test software described by such a model...

A software component must be tested every time it is reused, to guarantee the quality of both the component itself and the system in which it is to be integrated. To reduce testing costs, we propose a model to build highly testable components by embedding testing and monitoring mechanisms inside them. The approach is useful to component developers, who can use these built-in test capabilities i...

A knowledge-based model that emulates human behavior in a Dynamic Decision Making task is proposed. The model, MAIDEN-DSF, uses a connectionist representation of knowledge and a value function to compute the best alternative. In order to validate MAIDEN-DSF, two data sets have been used: a training set and a test set that contain the behavior of participants that performed the task with differe...