Automated tests are often considered an indicator of project quality. In this paper, we performed a large analysis of 6.3 M public GitHub projects using Java as the primary programming language. We created an overview of tests occurrence in publicly available GitHub projects and the use of test frameworks in them. The results showed that 52% of the projects contain at least one test case. However, there is a large number of example tests that do not represent relevant production code testing. It was also found that there is only a poor correlation between the number of the word “test” in different parts of the project (e.g., file paths, file name, file content, etc.) and the number of test cases, creation date, date of the last commit, number of commits, or number of watchers. Testing framework analysis confirmed that JUnit is the most used testing framework with a 48% share. TestNG, considered the second most popular Java unit testing framework, occurred in only 3% of the projects.

The escalation of code plagiarism in computer science education has necessitated the development of more sophisticated detection methods, particularly for binary files which pose a unique challenge. This study introduces a novel approach to plagiarism detection through binary decomposition, which decompiles binary files back into high-level code to reveal similarities that may be hidden at the binary level. The methodology is crucial for scenarios where students submit compiled programs, enabling the application of traditional code similarity analysis methods to detect instances of plagiarism. Statistical data on code similarity among student submissions in a C programming course are presented, highlighting the urgent need for robust detection strategies. This paper explores the ethical implications of plagiarism, the technical challenges of binary file analysis, and the potential for applying this technique across various programming languages and educational contexts.

A peer-reviewed journal of applied informatics that covers a broad range of topics, especially dealing with the social and business aspects of informatics.

When testers design test cases based only on specification, they can introduce significant redundancies into them and also create gaps of untested software. More insight into the impact of test cases on implementation can guide testers to design more effective test cases to cover implementation and prevent redundancies to save time. In this paper we describe challenges of UI tester and how we can help them using coverage analysis of UI source code. We prepared infrastructure to automatically collect code coverage data from UI testing for further analysis.