A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2008; you can also visit the original URL.
The file type is application/pdf
.
Active learning for automatic classification of software behavior
2004
Software engineering notes
A program's behavior is ultimately the collection of all its executions. This collection is diverse, unpredictable, and generally unbounded. Thus it is especially suited to statistical analysis and machine learning techniques. The primary focus of this paper is on the automatic classification of program behavior using execution data. Prior work on classifiers for software engineering adopts a classical batchlearning approach. In contrast, we explore an active-learning paradigm for behavior
doi:10.1145/1013886.1007539
fatcat:ai2wasvlxzbffkslip6syz2f7q