Developers often prefer dynamically typed programming languages, such as JavaScript, because such languages do not require explicit type declarations. However, such a feature hinders software engineering tasks, such as code completion, type related bug fixes and so on. Deep learning-based techniques are proposed in the literature to infer the types of code elements in JavaScript snippets. These techniques are computationally expensive. While several type inference techniques have been developed to detect types in code snippets written in statically typed languages, it is not clear how effective those techniques are for inferring types in dynamically typed languages, such as JavaScript. In this paper, we investigate the type inference techniques of JavaScript to understand the above two issues further. While doing that we propose a new technique that considers the locally specific code tokens as the context to infer the types of code elements. The evaluation result shows that the proposed technique is 20-47% more accurate than the statically typed language-based techniques and 5–14 times faster than the deep learning techniques without sacrificing accuracy. Our analysis of sensitivity, overlapping of predicted types and the number of training examples justify the importance of our technique.

Developers often reuse code snippets from online forums, such as Stack Overflow, to learn API usages of software frameworks or libraries. These code snippets often contain ambiguous undeclared external references. Such external references make it difficult to learn and use those APIs correctly. In particular, reusing code snippets containing such ambiguous undeclared external references requires significant manual efforts and expertise to resolve them. Manually resolving fully qualified names (FQN) of API elements is a non-trivial task. In this paper, we propose a novel context-sensitive technique, called COSTER, to resolve FQNs of API elements in such code snippets. The proposed technique collects locally specific source code elements as well as globally related tokens as the context of FQNs, calculates likelihood scores, and builds an occurrence likelihood dictionary (OLD). Given an API element as a query, COSTER captures the context of the query API element, matches that with the FQNs of API elements stored in the OLD, and rank those matched FQNs leveraging three different scores: likelihood, context similarity, and name similarity scores. Evaluation with more than 600K code examples collected from GitHub and two different Stack Overflow datasets shows that our proposed technique improves precision by 4-6% and recall by 3-22% compared to state-of-the-art techniques. The proposed technique significantly reduces the training time compared to the StatType, a state-of-the-art technique, without sacrificing accuracy. Extensive analyses on results demonstrate the robustness of the proposed technique.