At present, bots are still in their preliminary stages of development. Many are relatively simple, or developed ad-hoc for a very specific use-case. For this reason, they are typically programmed manually, or utilize machine-learning classifiers to interpret a fixed set of user utterances. In reality, real world conversations with humans require support for dynamically capturing users expressions. Moreover, bots will derive immeasurable value by programming them to invoke APIs for their results. Today, within the Web and Mobile development community, complex applications are being stringed together with a few lines of code -- all made possible by APIs. Yet, developers today are not as empowered to program bots in much the same way. To overcome this, we introduce BotBase, a bot programming platform that dynamically synthesizes natural language user expressions into API invocations. Our solution is two faceted: Firstly, we construct an API knowledge graph to encode and evolve APIs; secondly, leveraging the above we apply techniques in NLP, ML and Entity Recognition to perform the required synthesis from natural language user expressions into API calls.
Besides, there are some works focused on API recommendation techniques which are relevant to our work. RACK @cite_11 translates a natural language query into a list of relevant APIs by mapping keywords to APIs, Portfolio @cite_5 recommends relevant API methods for a given natural language query by employing indexing and graph based algorithms, proposed technique in @cite_22 recommends a graph of API methods from precise textual phrase matching.
@cite_5 @cite_22 @cite_11
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Different studies show that programmers are more interested in finding definitions of functions and their uses than variables, statements, or arbitrary code fragments [30, 29, 31]. Therefore, programmers require support in finding relevant functions and determining how those functions are used. Unfortunately, existing code search engines do not provide enough of this support to developers, thus reducing the effectiveness of code reuse. We provide this support to programmers in a code search system called Portfolio that retrieves and visualizes relevant functions and their usages. We have built Portfolio using a combination of models that address surfing behavior of programmer and sharing Related concepts among functions. We conducted an experiment with 49 professional programmers to compare Portfolio to Google Code Search and Koders using a standard methodology. The results show with strong statistical significance that users find more relevant functions with higher precision with Portfolio than with Google Code Search and Koders. Reusing APIs of existing libraries is a common practice during software development, but searching suitable APIs and their usages can be time-consuming [6]. In this paper, we study a new and more practical approach to help users find usages of APIs given only simple text phrases, when users have limited knowledge about an API library. We model API invocations as an API graph and aim to find an optimum connected subgraph that meets users' search needs. The problem is challenging since the search space in an API graph is very huge. We start with a greedy subgraph search algorithm which returns a connected subgraph containing nodes with high textual similarity to the query phrases. Two refinement techniques are proposed to improve the quality of the returned subgraph. Furthermore, as the greedy subgraph search algorithm relies on online query of shortest path between two graph nodes, we propose a space-efficient compressed shortest path indexing scheme that can efficiently recover the exact shortest path. We conduct extensive experiments to show that the proposed subgraph search approach for API recommendation is very effective in that it boosts the average F1-measure of the state-of-the-art approach, Portfolio [15], on two groups of real-life queries by 64 and 36 respectively. Traditional code search engines often do not perform well with natural language queries since they mostly apply keyword matching. These engines thus need carefully designed queries containing information about programming APIs for code search. Unfortunately, existing studies suggest that preparing an effective code search query is both challenging and time consuming for the developers. In this paper, we propose a novel API recommendation technique -- RACK that recommends a list of relevant APIs for a natural language query for code search by exploiting keyword-API associations from the crowdsourced knowledge of Stack Overflow. We first motivate our technique using an exploratory study with 11 core Java packages and 344K Java posts from Stack Overflow. Experiments using 150 code search queries randomly chosen from three Java tutorial sites show that our technique recommends correct API classes within the top 10 results for about 79 of the queries which is highly promising. Comparison with two variants of the state-of-the-art technique also shows that RACK outperforms both of them not only in Top-K accuracy but also in mean average precision and mean recall by a large margin.