Abstract
In this paper the use of Support Vector Machines to build programs behavioral models predicting misbehaviors while executing the programs, is described. Misbehaviors can be detected more precisely if the model is built considering both the failing and passing runs. It is desirable to create a model which even after fixing the detected bugs is still applicable. To achieve this, the use of a bug seeding technique to test all different execution paths of the program in both failing and passing executions is suggested. Our experiments with a test suite, EXIF, demonstrate the applicability of our proposed approach. © Springer-Verlag Berlin Heidelberg 2008.
Cite
CITATION STYLE
Parsa, S., Arabi, S., & Vahidi-Asl, M. (2008). A learning approach to early bug prediction in deployed software. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5253 LNAI, pp. 400–404). https://doi.org/10.1007/978-3-540-85776-1_38
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