Programming is typically a difficult and repetitive task. Programmers encounter endless problems during programming, and they often need to write similar code over and over again. To prevent programmers from reinventing wheels thus increase their productivity, we propose a context-aware code-to-code recommendation tool named Lancer. With the support of a Library-Sensitive Language Model (LSLM) and the BERT model, Lancer is able to automatically analyze the intention of the incomplete code and recommend relevant and reusable code samples in real-time. A video demonstration of Lancer can be found at https://youtu.be/tO9nhqZY35g. Lancer is open source and the code is available at https://github.com/sfzhou5678/Lancer.

Mining search intents in vertical websites like IT service crowdsourcing platform relies heavily on domain knowledge.Meanwhile, it still remains a difficulty of searching services in crowdsourcing platforms, as these platforms do contain much insufficient information, for example, users tend to use images describing IT services for the purpose of advertisements.To solve these problems, we build and leverage a knowledge graph to enhance searching of crowdsourcing IT services.The key idea is to (1) build an IT service knowledge graph from StackOverflow tag synonym system, Wikipedia, StuQ and data in IT service crowdsourcing platforms, (2) plug two activities into the basic search processterm expansion and service re-ranking, (3) use superordinates, hypernyms, synonyms, descriptions and relations of entities in the knowledge graph to expand user query and service information, and (4) apply a learning-to-rank model with four features to re-rank the search results, enforcing those more relevant services have the higher-ranking position.We have conducted several experiments to evaluate our approach.The results show that our approach achieves an MRR 34.9% higher and a Recall@15 11% higher than those of a basic search approach.

Programming is typically a difficult and repetitive task. Programmers will encounter endless problems during programming, and they often need to write similar code over and over again. Over the years, many tools have been proposed to support programming. However, to the best of our knowledge, these approaches require high-quality queries or programming contexts, which are often difficult to be built or even unavailable. To address this challenge, we propose SLAMPA, a novel tool which takes advantage of statistical language model and clone detection techniques to recommend code snippets during programming. Given a piece of incomplete code, SLAMPA first infers its intention using a neural language model. Then it retrieves code snippets from codebase with the support of an efficient clone detection technology Hybrid-CD we proposed. Finally, it recommends the most similar code snippets to programmers. Our evaluation results demonstrate that Hybrid-CD precisely detects similar code snippets and it outperforms previous techniques. Our results also show that the snippets recommended by SLAMPA catch the intention of programmers and SLAMPA is capable of finding potential code reuse opportunities during programming.