Using Sensitivity as a Method for Ranking the Test Cases Classified by Binary Decision Trees

Usually, data mining projects that are based on decision trees for classifying test cases will use the probabilities provided by these decision trees for ranking classified test cases. We have a need for a better method for ranking test cases that have already been classified by a binary decision tree because these probabilities are not always accurate and reliable enough. A reason for this is that the probability estimates computed by existing decision tree algorithms are always the same for all the different cases in a particular leaf of the decision tree. This is only one reason why the probability estimates given by decision tree algorithms can not be used as an accurate means of deciding if a test case has been correctly classified. Isabelle Alvarez has proposed a new method that could be used to rank the test cases that were classified by a binary decision tree [Alvarez, 2004]. In this paper we will give the results of a comparison of different ranking methods that are based on the probability estimate, the sensitivity of a particular case or both.

Generation of Testing Source Code for Object-Oriented Programs

Красимир Манев, Антон Желязков, Станимир Бойчев - В статията е представена имплементацията на последната фаза на автоматичен генератор на тестови данни за структурно тестване на софтуер, написан на обектно-ориентиран език за програмиране – генерирането на изходен код на тестващия модул. Някои детайли от имплементацията на останалите фази, които са важни за имплементацията на последната фаза, са представени първо. След това е описан и алгоритъмът за генериране на кода на тестващия модул.

Automating Exersises Validation in e-Learning Environments

E-learning is supposing an innovation in teaching, raising from the development of new technologies. It is based in a set of educational resources, including, among others, multimedia or interactive contents accessible through Internet or Intranet networks. A whole spectrum of tools and services support e-learning, some of them include auto-evaluation and automated correction of test-like exercises, however, this sort of exercises are very constrained because of its nature: fixed contents and correct answers suppose a limit in the way teachers may evaluation students. In this paper we propose a new engine that allows validating complex exercises in the area of Data Structures and Algorithms. Correct solutions to exercises do not rely only in how good the execution of the code is, or if the results are same as expected. A set of criteria on algorithm complexity or correctness in the use of the data structures are required. The engine presented in this work covers a wide set of exercises with these characteristics allowing teachers to establish the set of requirements for a solution, and students to obtain a measure on the quality of their solution in the same terms that are later required for exams.

Extracting Business Rules through Static Analysis of the Source Code

Красимир Манев, Нели Манева, Хараламби Хараламбиев - Подходът с използване на бизнес правила (БП) беше въведен в края на миналия век, за да се улесни специфицирането на фирмен софтуер и да може той да задоволи по-добре нуждите на съответния бизнес. Днес повечето от целите на подхода са постигнати. Но усилията, в научно-изследователски и практически аспект, за постигане на „’формална основа за обратно извличане на БП от съществуващи системи “продължават. В статията е представен подход за извличане на БП от програмен код, базиран на методи за статичен анализ на кода. Посочени са някои предимства и недостатъци на такъв подход.

Automatized Generation of Metadata and Calibrated Test Item Banks for E-learning

Report published in the Proceedings of the National Conference on "Education in the Information Society", Plovdiv, May, 2013