Software copyright claims an exclusive right for the software copyright owner to determine whether and under what conditions others can modify, reuse, or redistribute this software. For Free and Open Source Software (FOSS), it is very important to identify the copyright owner who can control those activities with license compliance. Copyright notice is a few sentences mostly placed in the header part of a source file as a comment or in a license document in a FOSS project, and it is an important clue to establish the ownership of a FOSS project. Repositories of FOSS projects contain rich and varied information on the development including the source code contributors who are also an important clue to establish the ownership. In this paper, as a first step of understanding copyright owner, we will explore the situation of the software copyright in the Linux kernel, a typical example of FOSS, by analyzing and comparing two kinds of datasets, copyright notices in source files and source code contributors in the software repositories. The discrepancy between two kinds of analysis results is defined as copyright inconsistency. The analysis result has indicated that copyright inconsistencies are prevalent in the Linux kernel. We have also found that code reuse, affiliation change, refactoring, support function, and others' contributions potentially have impacts on the occurrence of the copyright inconsistencies in the Linux kernel. This study exposes the difficulty in managing software copyright in FOSS, highlighting the usefulness of future work to address software copyright problems.

For Free and Open Source Software (FOSS), identifying the copyright notices is important. However, both the collaborative manner of FOSS project development and the large number of source files increase its difficulty. In this paper, we aim at automatically identifying the copyright notices in source files based on machine learning techniques. The evaluation experiment shows that our method outperforms FOSSology, the only existing method based on regular expression.

This paper presents a method for comparing and detecting clones of Java programs by analyzing program stack flows. A stack flow denotes an operational behavior of a program by describing individual instructions and stack movements for performing specific operations. We analyze stack flows by simulating the operand stack movements during execution of a Java program. Two programs for detection of clones of Java programs are compared by matching similar pairs of stack flows in the programs. Experiments were performed on the proposed method and compared with the earlier approaches of comparing Java programs, the Tamada, k-gram, and stack pattern based methods. Their performance was evaluated with real-world Java programs in several categories collected from the Internet. The experimental results show that the proposed method is more effective than earlier methods of comparing and detecting clones of Java programs.