While performing a task, software developers interact with a myriad of natural language documents. Not all information in these documents is relevant to a developer's task forcing them to filter relevant information from large amounts of irrelevant information. If a developer misses some of the necessary information for her task, she will have an incomplete or incorrect basis from which to complete the task. Many approaches mine relevant text fragments from natural language artifacts. However, existing approaches mine information for pre-defined tasks and from a restricted set of artifacts. I hypothesize that it is possible to design a more generalizable approach that can identify, for a particular task, relevant text across different artifact types establishing relationships between them and facilitating how developers search and locate task-relevant information. To investigate this hypothesis, I propose to match a developer's task to text fragments in natural language artifacts according to their semantics. By semantically matching textual pieces to a developer's task we aim to more precisely identify fragments relevant to a task. To help developers in thoroughly navigating through the identified fragments I also propose to synthesize and group them. Ultimately, this research aims to help developers make more informed decisions regarding their software development task. Dr. Gail C. Murphy supervises this work.

@inproceedings{paperbot5176,
  title = {Helping developers search and locate task-relevant information in natural language documents},
  author = {Arthur Marques},
  booktitle = {FSE},
  year = {2019},
  doi = {10.1145/3338906.3341459}
}