Šarūnas Packevičius

Software testing using imprecise OCL constraints as oracles

Many software testing techniques are targeted on test data generation. Only a few of them provide an automatic way to verify if software behaves correctly using generated test data. We propose to use software model as imprecise test oracle. UML modelling language extension OCL can be used as test oracle. Imprecise OCL constraints can be viewed as expressions which define expected results within some ranges of possible values. Imprecise OCL constraints can be viewed as expressions which define expected results within some ranges of possible values. When software is executed using generated test data the output is verified against imprecise OCL constraints. If output does not satisfy imprecise OCL constrains, unit under test definitely contains bugs. And if output satisfies imprecise OCL constraints the tester can assume that unit under test has no bugs.

The Testing Method Based on Image Analysis for Automated Detection of UI Defects Intended for Mobile Applications

Large amounts of defects found in applications are classified as user interface defects. As more and more applications are provided for smart phones, it is reasonable to test those applications on various possible configurations of mobile devices such as screen resolution, OS version and custom layer. However, the set of mobile devices configurations is quite large. Developers are limited to testing their applications on all possible configurations.

Unit tests construction based on business rules

Software unit testing involves generating or creating test data and specifying a test oracle. The test oracle verifies if software under testing behave correctly when it is given test data as an input.

Text Semantics and Layout Defects Detection in Android Apps Using Dynamic Execution and Screenshot Analysis.

The paper presents classification of the text defects. It provides a list of user interface text defects and the method based on static/dynamic code analysis for detecting defects in Android applications. This paper proposes a list of static analysis rules for detecting every defect and the tool model implementing those rules. The method and the tool are based on the application of multiple Android application emulators, execution of the application through certain execution paths on multiple hardware and software configurations while taking application screen-shots. The defects are identified by running analysis rules on each taken screen-shot and searching for defect patterns. The results are presented by testing sample Android application.

Automated Method for Software Integration Testing Based on UML Behavioral Models

Nowadays, testing is often considered more important than the code itself. Therefore, in order to test large and complex systems test automation methods are needed, which help evaluating whether the software is working properly. The main goal of the research is to improve effectiveness of integration testing by creating an automated method based on UML behavioral models. Test input data generation using symbolic execution was applied and it gave full structural coverage, which increased the quality of integration testing. Testing method allowed automating the testing process and increased the effectiveness of tests in comparison with other methods. Experiments showed that 96% of all mutations were successfully detected, and automated test data generation based on symbolic execution increased the detection of mutants by 6-19% in comparison to other model-based testing methods.

Platform Independent Unit Tests Generator

Unit tests are viewed as a coding result of software developers. These unit tests are usually created by developers and implemented directly using specific language and unit testing framework. The existing unit test generation tools usually do the same thing – generate tests for specific language using a specific unit testing framework. Thus such a generator is suitable for only one programming language and unit testing framework. Another drawback of these generators – they use the software code as a source for generation mainly.