CS Faculty Candidate Colloquium
Monday **Special Location & Time** January 28 10:45 - 11:50 AM Kelley 1007 Sunghun Kim EECS Colloquium: Computer Science Faculty Candidate Postdoctoral Associate MIT Predicting Bugs by Analyzing Software History Almost all software contains undiscovered bugs, ones that have not yet been exposed by testing or by users. Wouldn't it be nice if there was a way to know the location of these bugs? This talk presents two approaches for predicting the location of bugs by analyzing software history. The bug cache contains 10% of the files in a software project. Through an analysis of the software's development history and the location of bugs, files are added and removed from the cache based on four notions of bug locality: temporal, spatial, changed-entity, and new-entity locality. After processing, files in the bug cache contain 73-95% of undiscovered bugs. To improve the localization of predicted bugs, the second prediction approach focuses on configuration management commit transactions. Using machine learning techniques (Bayes Net, Support Vector Machines), we classify commits as being likely to have a fault, or unlikely to have a fault. The best precision and recall figures for each project are typically in the mid-70's. Hence, it is possible for a configuration management system to inform a developer, post-commit, that they have just created a bug (with appx. 94% likelihood). Biography: Sunghun Kim is a postdoctoral associate at MIT and a member of the Program Analysis Group. He completed his Ph.D. in the Computer Science Department at the University of California, Santa Cruz in 2006. He was a Chief Technical Officer (CTO), and led a 25-person team at the Nara Vision Co. Ltd, a leading Internet software company in Korea for six years. His core research area is Software Engineering, focusing on software evolution, program analysis, and empirical studies.
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