A Lightweight Approach to Uncover Technical Artifacts in Unstructured Data

Abstract

Developer communication through email, chat, or issue report comments consists mostly of largely unstructured data, i.e., natural language text, mixed with technical artifacts such as project-specific jargon, abbreviations, source code patches, stack traces and identifiers. These technical artifacts represent a valuable source of knowledge on the technical part of the system, with a wide range of applications from establishing traceability links to creating project-specific vocabularies. However, the lack of well-defined boundaries between natural language and technical content make the automated mining of technical artifacts challenging. As a first step towards a general-purpose technique to extracting technical artifacts from unstructured data, we present a lightweight approach to untangle technical artifacts and natural language text. Our approach is based on existing spell checking tools, which are well-understood, fast, readily available across platforms and impartial to different kinds of textual data. Through a handcrafted benchmark, we demonstrate that our approach is able to successfully uncover a wide range of technical artifacts in unstructured data.