Answer Garden: a tool for growing organizational memory
COCS '90 Proceedings of the ACM SIGOIS and IEEE CS TC-OA conference on Office information systems
LaSSIE: a knowledge-based software information system
Communications of the ACM - Special issue on software engineering
Implementing faceted classification for software reuse
Communications of the ACM - Special issue on software engineering
The computer scientist as toolsmith II
Communications of the ACM
Automatic text decomposition using text segments and text themes
Proceedings of the the seventh ACM conference on Hypertext
Answer Garden 2: merging organizational memory with collaborative help
CSCW '96 Proceedings of the 1996 ACM conference on Computer supported cooperative work
Task Oriented Software Understanding
ASE '98 Proceedings of the 13th IEEE international conference on Automated software engineering
Question Answering from Frequently Asked Question Files: Experiences with the FAQ Finder System
Question Answering from Frequently Asked Question Files: Experiences with the FAQ Finder System
Proceedings of the fourth international conference on Communities and technologies
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Developers often use electronic mailing lists when seeking assistance with a particular software application. The archives of these mailing lists provide a rich repository of problem-solving knowledge. Developers seeking a quick answer to a problem find these archives inconvenient, because they lack efficient searching mechanisms, and retain the structure of the original conversational threads which are rarely relevant to the knowledge seeker.We present a system called MCS which improves mailing list archives through a process called condensation. Condensation involves several tasks: extracting only messages of longer-term relevance, adding metadata to those messages to improve searching, and potentially editing the content of the messages when appropriate to clarify. The condensation process is performed by a human editor (assisted by a tool), rather than by an artificial intelligence (AI) system.We describe the design and implementation of MCS, and compare it to rlated systems. We also present our experiences condensing a 1428 message mailing list archive to an archive containing only 177 messages (an 88% reduction). The condensation required only 1.5 minutes of editor effort per message. The condensed archive was adopted by the users of the mailing list.