Understanding and detecting real-world performance bugs

By Guoliang Jin, Linhai Song, Xiaoming Shi, Joel Scherpelz, Shan Lu. ACM-SIGPLAN Symposium on Programming Language Design and Implementation, 2012

414 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Hardware and Architecture, Software

View Paper: https://www.semanticscholar.org/paper/6bec978f4293f64b6b56f99c87fa0b3cab90c7d4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Tien Nhut Nguyen
2. Shaohua Wang
3. Yi Li
4. Shing-Chi Cheung
5. Yingfei Xiong
6. Hongyu Zhang

In Hardware and Architecture

1. Hongyu Zhang

In Software

1. Tien Nhut Nguyen
2. Shaohua Wang
3. Yi Li
4. Shing-Chi Cheung
5. Lu Zhang
6. Son Van Nguyen
7. Qing Gao
8. Xiangqun Chen

Citations in the corpus

• Improving bug detection via context-based code representation learning and attention-based neural networks (2019, methodology): “• Mining-based bug detection [Bian et al. 2018; Cole et al. 2006; Engler et al. 2001; Jin et al. 2012; Li and Zhou 2005; Olivo et al. 2015; Toman and Grossman 2017].”
• Shaping program repair space with existing patches and similar code (2018, methodology): “Following the existing approaches [20, 46], we first developed a script to automatically analyze each commit message in the version control system and used keywords (e.”
• An empirical study on TensorFlow program bugs (2018, background): “Jin et al. [19] and Zaman et al. [42, 43] conducted studies of performance bugs.”