IRISH: A Hidden Markov Model to detect coded information islands in free text

Developers' communication, as contained in emails, issue trackers, and forums, is a precious source of information to support the development process. For example, it can be used to capture knowledge about development practice or about a software project itself. Thus, extracting the content of developers' communication can be useful to support several software engineering tasks, such as program comprehension, source code analysis, and software analytics. However, automating the extraction process is challenging, due to the unstructured nature of free text, which mixes different coding languages (e.g., source code, stack dumps, and log traces) with natural language parts. We conduct an extensive evaluation of IRISH (InfoRmation ISlands Hmm), an approach we proposed to extract islands of coded information from free text at token granularity, with respect to the state of art approaches based on island parsing or island parsing combined with machine learners. The evaluation considers a wide set of natural language documents (e.g., textbooks, forum discussions, and development emails) taken from different contexts and encompassing different coding languages. Results indicate an F-measure of IRISH between 74% and 99%; this is in line with existing approaches which, differently from IRISH, require specific expertise for the definition of regular expressions or grammars. (C) 2014 Elsevier B.V. All rights reserved.

Developers' communication, as contained in emails, issue trackers, and forums, is a precious source of information to support the development process. For example, it can be used to capture knowledge about development practice or about a software project itself. Thus, extracting the content of developers' communication can be useful to support several software engineering tasks, such as program comprehension, source code analysis, and software analytics. However, automating the extraction process is challenging, due to the unstructured nature of free text, which mixes different coding languages (eg source code, stack dumps, and log traces) with natural language parts.We conduct an extensive evaluation of Irish, an approach weproposed to extract islands of coded information from free text at token granularity, with respect to the stateof art approaches based on island parsing or island parsing combinedwith machine learners. The evaluation considers a wide set of natural language documents (eg textbooks, forum discussions, and development emails) taken fromdifferent contexts and encompassing different codinglanguages. Results indicate an F-measure of Irish between 74% and99%; this is in line with existing approaches which, differentlyfrom Irish, require specific expertise for the definition of regular expressions or grammars.