Island Parsing

Luca Ponzanelli · Doctor of Philosophy in Informatics

Stack Overflow is the de facto Question and Answer (Q&A) website for developers, and it has been used in many approaches by software engineering researchers to mine useful data. However, the contents of a Stack Overflow discussion are inherently heterogeneous, mixing natural language, source code, stack traces and configuration files in XML or JSON format. We constructed a full island grammar capable of modeling the set of 700,000 Stack Overflow discussions talking about Java, building a heterogeneous abstract syntax tree (H-AST) of each post (question, answer or comment) in a discussion. The resulting dataset models every Stack Overflow discussion, providing a full H-AST for each type of structured fragment (i.e., JSON, XML, Java, Stack traces), and complementing this information with a set of basic meta-information like term frequency to enable natural language analyses. Our dataset allows the end-user to perform combined analyses of the Stack Overflow by visiting the H-AST of a discussion.

Heterogeneous abstract syntax trees for Stack Overflow: a Scala development kit, a ready-made JSON dataset, and an island-parsing web service

The design and evolution of a software system leave traces in various kinds of artifacts. In software, produced by humans for humans, many artifacts are written in natural language by people involved in the project. Such entities contain structured information which constitute a valuable source of knowledge for analyzing and comprehending a system's design and evolution. However, the ambiguous and informal nature of narrative is a serious challenge in gathering such information, which is scattered throughout natural language text. We present an approach-based on island parsing-to recognize and enable the parsing of structured information that occur in natural language artifacts. We evaluate our approach by applying it to mailing lists pertaining to three software systems. We show that this approach allows us to extract structured data from emails with high precision and recall.