NDLI: Topic modeling of NASA space system problem reports: research in practice

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Topic modeling of NASA space system problem reports: research in practice

Content Provider	ACM Digital Library
Author	Layman, Lucas Nikora, Allen P. Menzies, Tim Meek, Joshua
Abstract	Problem reports at NASA are similar to bug reports: they capture defects found during test, post-launch operational anomalies, and document the investigation and corrective action of the issue. These artifacts are a rich source of lessons learned for NASA, but are expensive to analyze since problem reports are comprised primarily of natural language text. We apply topic modeling to a corpus of NASA problem reports to extract trends in testing and operational failures. We collected 16,669 problem reports from six NASA space flight missions and applied Latent Dirichlet Allocation topic modeling to the document corpus. We analyze the most popular topics within and across missions, and how popular topics changed over the lifetime of a mission. We find that hardware material and flight software issues are common during the integration and testing phase, while ground station software and equipment issues are more common during the operations phase. We identify a number of challenges in topic modeling for trend analysis: 1) that the process of selecting the topic modeling parameters lacks definitive guidance, 2) defining semantically-meaningful topic labels requires non-trivial effort and domain expertise, 3) topic models derived from the combined corpus of the six missions were biased toward the larger missions, and 4) topics must be semantically distinct as well as cohesive to be useful. Nonetheless, topic modeling can identify problem themes within missions and across mission lifetimes, providing useful feedback to engineers and project managers.
Starting Page	303
Ending Page	314
Page Count	12
File Format	PDF
ISBN	9781450341868
DOI	10.1145/2901739.2901760
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-05-14
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Topic modeling Lda Natural language processing Data mining Defects
Content Type	Text
Resource Type	Article

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