Ayudante: Identifying Undesired Variable Interactions
A common programming mistake is for incompatible variables to interact, e.g., storing euros in a variable that should hold dollars. This paper proposes a novel approach for identifying undesired interactions between program variables. Our approach uses two different mechanisms to identify related variables. Natural language processing (NLP) identifies variables with related names that may have related semantics. Abstract type inference (ATI) identifies variables that interact with each other. Any discrepancies between these two mechanisms may indicate a programming error. We have implemented our approach in a tool called Ayudante. We evaluated Ayudante using two open source programs: the Exim mail server and grep. Although these programs have been extensively tested and in deployment for years, Ayudante’s first report for grep revealed a programming mistake.
Mon 26 Oct Times are displayed in time zone: (GMT-04:00) Eastern Time (US & Canada) change
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Peter HoferChristian Doppler Laboratory on Monitoring and Evolution of Very-Large-Scale Software Systems, Johannes Kepler University Linz, David GnedtChristian Doppler Laboratory on Monitoring and Evolution ofVery-Large-Scale Software Systems, Johannes Kepler UniversityLinz, Hanspeter MössenböckJohannes Kepler University Linz
|14:30 - 15:00|
Peter OhmannUniversity of Wisconsin - Madison, David Bingham BrownUniversity of Wisconsin - Madison, Ben LiblitUniversity of Wisconsin–Madison, Thomas RepsUniversity of Wisconsin - Madison and Grammatech Inc.Pre-print