Machine learning algorithms are widely used in the assessment of error-proneness in software.We conducted several experiments with error prediction on public PROMISE repository.We used Decision Tree and Random Forest algorithms.We also examined techniques aiming at the improvement of performance and accuracy of the model -such as oversampling, hyperparameter optimization or threshold adjustment.The outcome of our experiments suggests that Random Forest algorithm, with 100 -1000 trees, can be used to obtain high values of evaluation parameters such as accuracy and balanced accuracy.However, it has to be implemented with a set of techniques countering imbalance of the datasets used to assure high values of precision and recall that correspond with correct detection of erroneous software.Additionally, it was shown that the oversampling and hyperparameter optimization could be reliably applied to the algorithm, while threshold adjustment technique was not found to be consistent.

The Web Services technology has received a significant amount of attention in recent years because it allows to easily utilize and integrate existing software applications to create new business services.With the increase of interest and popularity of Web Services, web applications are developed.This way of software development causes new issues for Web Service testing to ensure the quality of service that are published.This paper describes the tool (named WSDLTest) for automatic testing of web services.The tool can be used for testing of web services for which WSDL 1.1 or WSDL 2.0 document are available.WSDLTest parses the WSDL document, and based on, it tests the web service by sending automatically generated messages.Some examples of the usage of our tool are given.