Davide Falessi, Lucas Layman
• D. Falessi and L. Layman, “Automated classification of NASA anomalies using natural language processing techniques,” in 2013 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW), pp. 5–6, IEEE, nov 2013
Publication year: 2013

NASA anomaly databases are rich resources of software failure data in the field. These data are often captured in natural language that is not appropriate for trending or statistical analyses. This fast abstract describes a feasibility study of applying 60 natural language processing techniques for automatically classifying anomaly data to enable trend analyses.