If two or more program entities (such as files, classes, methods) co-change (i.e., change together) frequently during software evolution, then it is likely that these two entities are coupled (i.e., the entities are related). Such a coupling is termed as evolutionary coupling in the literature. The concept of traditional evolutionary coupling restricts us to assume coupling among only those entities that changed together in the past. The entities that did not co-change in the past might also have coupling. However, such couplings can not be retrieved using the current concept of detecting evolutionary coupling in the literature. In this paper, we investigate whether we can detect such couplings by applying transitive rules on the evolutionary couplings detected using the traditional mechanism. We call these couplings that we detect using our proposed mechanism as transitive evolutionary couplings. According to our research on thousands of revisions of four subject systems, transitive evolutionary couplings combined with the traditional ones provide us with 13.96% higher recall and 5.56% higher precision in detecting future co-change candidates when compared with a state-of-the-art technique.

A code clone is a pair of code fragments, within or between software systems that are similar. Since code clones often negatively impact the maintainability of a software system, a great many numbers of code clone detection techniques and tools have been proposed and studied over the last decade. To detect all possible similar source code patterns in general, the clone detection tools work on syntax level (such as texts, tokens, AST and so on) while lacking user-specific preferences. This often means the reported clones must be manually validated prior to any analysis in order to filter out the true positive clones from task or user-specific considerations. This manual clone validation effort is very time-consuming and often error-prone, in particular for large-scale clone detection. In this paper, we propose a machine learning based approach for automating the validation process. In an experiment with clones detected by several clone detectors in several different software systems, we found our approach has an accuracy of up to 87.4% when compared against the manual validation by multiple expert judges. The proposed method shows promising results in several comparative studies with the existing related approaches for automatic code clone validation. We also present our experimental results in terms of different code clone detection tools, machine learning algorithms and open source software systems.

In today's open source era, developers look forsimilar software applications in source code repositories for anumber of reasons, including, exploring alternative implementations, reusing source code, or looking for a better application. However, while there are a great many studies for finding similarapplications written in the same programming language, there isa marked lack of studies for finding similar software applicationswritten in different languages. In this paper, we fill the gapby proposing a novel modelCroLSimwhich is able to detectsimilar software applications across different programming lan-guages. In our approach, we use the API documentation tofind relationships among the API calls used by the differentprogramming languages. We adopt a deep learning based word-vector learning method to identify semantic relationships amongthe API documentation which we then use to detect cross-language similar software applications. For evaluating CroLSim, we formed a repository consisting of 8,956 Java, 7,658 C#, and 10,232 Python applications collected from GitHub. Weobserved thatCroLSimcan successfully detect similar softwareapplications across different programming languages with a meanaverage precision rate of 0.65, an average confidence rate of3.6 (out of 5) with 75% high rated successful queries, whichoutperforms all related existing approaches with a significantperformance improvement.