Zhen Yu Ding

TL;DR: This work proposes a novel fitness function that optimizes for both functionality and semantic diversity, characterized using learned invariants over intermediate behavior, and early results show that this new approach improves semantic diversity and fitness granularity, but does not statistically significantly improve repair performance.

TL;DR: In this paper, the authors conduct an empirical study of OSS-Fuzz, analyzing 23,907 bugs found in 316 projects and examine the characteristics of fuzzer-found faults, the lifecycle of such faults, and the evolution of fuzzing campaigns over time.

TL;DR: This work evaluates the correctness and diversity of patches generated by GenProg and an invariant-based diversity-enhancing extension and finds no evidence that promoting diversity changes the correctness of patches in a positive or negative direction.

TL;DR: The discussion session at the 6th International Genetic Improvement Workshop (GI-2019 @ ICSE) as mentioned in this paper was held as part of the 41st ACM/IEEE International Confer- ence on Software Engineering on Tuesday 28th May 2019.

TL;DR: The discussion session at the 6th International Genetic Improvement Workshop (GI-2019 @ ICSE) as discussed by the authors was held as part of the 41st ACM/IEEE International Conference on Software Engineering on Tuesday 28th May 2019.