Software development is largely dependent on libraries to reuse existing functionalities instead of reinventing the wheel. Software developers often need to find analogical libraries (libraries similar to ones they are already familiar with) as an analogical library may offer improved or additional features. Developers also need to search for analogical libraries across programming languages when developing applications in different languages or for different platforms. However, manually searching for analogical libraries is a time-consuming and difficult task. This paper presents a technique, called XLibRec, that recommends analogical libraries across different programming languages. XLibRec collects Stack Overflow question titles containing library names, library usage information from Stack Overflow posts, and library descriptions from a third party website, Libraries.io. We generate word-vectors for each information and calculate a weight-based cosine similarity score from them to recommend analogical libraries. We performed an extensive evaluation using a large number of analogical libraries across four different programming languages. Results from our evaluation show that the proposed technique can recommend cross-language analogical libraries with great accuracy. The precision for the Top-3 recommendations ranges from 62-81% and has achieved 8-45% higher precision than the state-of-the-art technique.

Abstract While there are novel approaches for detecting and categorizing similar software applications, previous research focused on detecting similarity in applications written in the same programming language and not on detecting similarity in applications written in different programming languages. Cross-language software similarity detection is inherently more challenging due to variations in language, application structures, support libraries used, and naming conventions. In this paper we propose a novel model, CroLSim, to detect similar software applications across different programming languages. We define a semantic relationship among cross-language libraries and API methods (both local and third party) using functional descriptions and a word-vector learning model. Our experiments show that CroLSim can successfully detect cross-language similar software applications, which outperforms all existing approaches (mean average precision rate of 0.65, confidence rate of 3.6, and 75% highly rated successful queries). Furthermore, we applied CroLSim to a source code repository to see whether our model can recommend cross-language source code fragments if queried directly with source code. From our experiments we found that CroLSim can recommend cross-language functional similar source code when source code is directly used as a query (average precision=0.28, recall=0.85, and F-Measure=0.40).

Software clones are detrimental to software maintenance and evolution and as a result many clone detectors have been proposed. These tools target clone detection in software applications written in a single programming language. However, a software application may be written in different languages for different platforms to improve the application's platform compatibility and adoption by users of different platforms. Cross language clones (CLCs) introduce additional challenges when maintaining multi-platform applications and would likely go undetected using existing tools. In this paper, we propose CLCDSA, a cross language clone detector which can detect CLCs without extensive processing of the source code and without the need to generate an intermediate representation. The proposed CLCDSA model analyzes different syntactic features of source code across different programming languages to detect CLCs. To support large scale clone detection, the CLCDSA model uses an action filter based on cross language API call similarity to discard non-potential clones. The design methodology of CLCDSA is two-fold: (a) it detects CLCs on the fly by comparing the similarity of features, and (b) it uses a deep neural network based feature vector learning model to learn the features and detect CLCs. Early evaluation of the model observed an average precision, recall and F-measure score of 0.55, 0.86, and 0.64 respectively for the first phase and 0.61, 0.93, and 0.71 respectively for the second phase which indicates that CLCDSA outperforms all available models in detecting cross language clones.

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.