Extracting Archival-Quality Information from Software-Related Chats

Software developers are increasingly having conversations about software development via online chat services. Many of those chat communications contain valuable information, such as code descriptions, good programming practices, and causes of common errors/exceptions. However, the nature of chat community content is transient, as opposed to the archival nature of other developer communications such as email, bug reports and Q&A forums. As a result, important information and advice are lost over time.The focus of this dissertation is Extracting Archival Information from Software-Related Chats, specifically to (1) automatically identify conversations that contain archival-quality information, (2) accurately reduce the granularity of the information reported as archival information, and (3) conduct a case study to investigate how archival quality information extracted from chats compare to related posts in Q&A forums. Archiving knowledge from developer chats could be used potentially in several applications such as: creating a new archival mechanism available to a given chat community, augmenting Q&A forums, or facilitating the mining of specific information and improving software maintenance tools.