ASE 2024
Sun 27 October - Fri 1 November 2024 Sacramento, California, United States

This program is tentative and subject to change.

Thu 31 Oct 2024 13:30 - 13:45 at Compagno - Bug detection and prediction

In this experience paper, we design, implement, and evaluate a new static type-error detection tool for Python. To build a practical tool, we first collected and analyzed 68 real-world type errors gathered from 20 open-source projects. This empirical investigation revealed four key static-analysis features that are crucial for the effective detection of Python type errors in practice. Utilizing these insights, we present a tool called Pyinder, which can successfully detect 34 out of the 68 bugs, compared to existing type analysis tools that collectively detect only 16 bugs. We also discuss the remaining 34 bugs that Pyinder failed to detect, offering insights into future directions for Python type analysis tools. Lastly, we show that Pyinder can uncover previously unknown bugs in recent Python projects.

This program is tentative and subject to change.

Thu 31 Oct

Displayed time zone: Pacific Time (US & Canada) change

13:30 - 15:00
Bug detection and predictionResearch Papers / Journal-first Papers at Compagno
13:30
15m
Talk
Towards Effective Static Type-Error Detection for Python
Research Papers
Wonseok Oh Korea University, Hakjoo Oh Korea University
13:45
15m
Talk
Detecting Element Accessing Bugs in C++ Sequence Containers
Research Papers
zhilin li , Xutong Ma Institute of Software, Chinese Academy of Sciences, Beijing, China, Mengze Hu Institute of Software, Chinese Academy of Sciences, Jun Yan Institute of Software, Chinese Academy of Sciences
14:00
15m
Talk
Concretely Mapped Symbolic Memory Locations for Memory Error Detection
Journal-first Papers
Haoxin Tu Singapore Management University, Singapore, Lingxiao Jiang Singapore Management University, Jiaqi Hong Independent Researcher, Xuhua Ding Singapore Management University, He Jiang Dalian University of Technology
14:15
15m
Talk
NeuroJIT: Improving Just-In-Time Defect Prediction Using Neurophysiological and Empirical Perceptions of Modern Developers
Research Papers
Gichan Lee Hanyang University, Hansae Ju Hanyang University, Scott Uk-Jin Lee Hanyang University