세미나안내

세미나안내

“Programs that Fix Programs”

2021-05-20
  • 149
김동선 교수 (경북대학교) / 2021.05.21

[Biography]

Dongsun Kim is an assistant professor at Kyungpook National University. He has received his Ph.D. degree in Computer Science and Engineering from Sogang University, Korea. His career includes several academic and industrial experiences. He was a postdoctoral fellow at the Hong Kong University of Science and Technology from September 2010 to June 2013. He joined the University of Luxembourg as a research associate in November 2013 and continued his position until November 2018. From April 2019, he worked as a senior software test engineer position at Furiosa.ai, a fabless startup company manufacturing neural processing units.He has published several research papers and participated in several research projects relevant to AI-based software engineering. In particular, he has pioneered a new line of research on pattern-based program repair. His recent achievements have focused on automated fix pattern mining, deep code representation for mining fix patterns, program repair driven by bug reports, fault localization impact on program repair, and specific topics for program repair; for these topics, he leveraged several AI-based techniques such as autoencoders, CNNs, etc. He has led a relevant research project, “Automated Program Repair using Fix Patterns Learned from Human-written Patches”

[Abstract]

Developers have been faced with a number of bugs every day. Fixing bugs is tedious and time-consuming. Typical bug resolution tasks include the identification of bug locations, the selection of bugs to fix, and the actual removal of them. This talk leads audiences to a journey of bug hunting by introducing recent advancement of automated debugging. To locating bugs, the two-phase localisation model filters out deficient bug reports to avoid noisy input and enhance the accuracy of bug localisation. The bug prioritisation approach selects bugs to fix first, by using static information. The pattern-based program repair leverages human-written patches to automatically generate more acceptable bug patches. This talk also draws further directions.

 

ZOOM : https://zoom.us/j/8978217407?pwd=d1pOZmF1OWlseEdZRVBpV3VuSkl3dz09

ID : 897 821 7407

PW : 1nTQDY

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