Code cloning has been very often indicated as a bad software development practice. However, many studies appearing in the literature indicate that this is not always the case. In fact, either changes occurring in cloned code are consistently propagated, or cloning is used as a sort of templating strategy, where cloned source code fragments evolve independently. This paper (i) proposes an automatic approach to classify the evolution of source code clone fragments, and (ii) reports a fine-grained analysis of clone evolution in four different Java and C software systems, aimed at investigating to what extent clones are consistently propagated or they evolve independently. Also, the paper investigates the relationship between the presence of clone evolution patterns and other characteristics such as clone raSuresh Thummalapenta North Carolina State University, Raleigh, USA E-mail: sthumma@ncsu.edu Luigi Cerulo, Lerina Aversano, Massimiliano Di Penta Department of Engineering -University of Sannio, Benevento, Italy E-mail: lcerulo@unisannio.it, aversano@unisannio.it, dipenta@unisannio.it 2 dius, clone size and the kind of change the clones underwent, i.e., corrective maintenance or enhancement.

No abstract

Programming languages such as Java and C++ provide exception-handling

Access Control Policies (ACP) specify which principals such as users have access to which resources. Ensuring the correctness and consistency of ACPs is crucial to prevent security vulnerabilities. However, in practice, ACPs are commonly written in Natural Language (NL) and buried in large documents such as requirements documents, not amenable for automated techniques to check for correctness and consistency. It is tedious to manually extract ACPs from these NL documents and validate NL functional requirements such as use cases against ACPs for detecting inconsistencies. To address these issues, we propose an approach, called Text2Policy, to automatically extract ACPs from NL software documents and resource-access information from NL scenario-based functional requirements. We conducted three evaluations on the collected ACP sentences from publicly available sources along with use cases from both open source and proprietary projects. The results show that Text2Policy effectively identifies ACP sentences with the precision of 88.7% and the recall of 89.4%, extracts ACP rules with the accuracy of 86.3%, and extracts action steps with the accuracy of 81.9%.

No abstract