Junming Cao

I am a research assistant at School of Computer Science in Fudan University. I received my B.Sc. degree and M.Sc. in School of Software Engineering from Shanghai Jiao Tong University in 2018 and 2021. My research interests include empirical software engineering and program analysis, with emphasis on the detection and repair of bugs in deep learning applications. I will pursue my PhD. degree in Fudan University from September 2021, advised by Prof. Bihuan Chen.

Selected Publications

ASE

BugPecker: Locating Faulty Methods with Deep Learning on Revision Graphs

Junming Cao, Shouliang Yang, Wenhui Jiang, Hushuang Zeng, Beijun Shen, and Hao Zhong

In 35th IEEE/ACM International Conference on Automated Software Engineering, ASE 2020, New Idea Track, Virtual Event, Australia

Abs PDF Code

Given a bug report of a project, the task of locating the faults of the bug report is called fault localization. To help programmers in the fault localization process, many approaches have been proposed, and have achieved promising results to locate faulty files. How- ever, it is still challenging to locate faulty methods, because many methods are short and do not have sufficient details to determine whether they are faulty. In this paper, we present BugPecker, a novel approach to locate faulty methods based on its deep learn- ing on revision graphs. Its key idea includes (1) building revision graphs and capturing the details of past fixes as much as possible, and (2) discovering relations inside our revision graphs to expand the details for methods and calculating various features to assist our ranking. We have implemented BugPecker, and evaluated it on three open source projects. The early results show that BugPecker achieves a mean average precision (MAP) of 0.263 and mean re- ciprocal rank (MRR) of 0.291, which improve the prior approaches significantly. For example, BugPecker improves the MAP values of all three projects by five times, compared with two recent ap- proaches such as DNNLoc-m and BLIA 1.5.
ASE

CocoQa: Question Answering for Coding Conventions Over Knowledge Graphs

Tianjiao Du, Junming Cao, Qinyue Wu, Wei Li, Beijun Shen, and Yuting Chen

In 34th IEEE/ACM International Conference on Automated Software Engineering, ASE 2019, Tool Demo Track, San Diego, CA, USA

Abs PDF Code Demo

Coding convention plays an important role in guaranteeing software quality. However, coding conventions are usually informally presented and inconvenient for programmers to use. In this paper, we present CocoQa, a system that answers programmer’s questions about coding conventions. CocoQa answers questions by querying a knowledge graph for coding conventions. It employs 1) a subgraph matching algorithm that parses the question into a SPARQL query, and 2) a machine comprehension algorithm that uses an end-to-end neural network to detect answers from searched paragraphs. We have implemented CocoQa, and evaluated it on a coding convention QA dataset. The results show that CocoQa can answer questions about coding conventions precisely. In particular, CocoQa can achieve a precision of 82.92% and a recall of 91.10%. Repository: https://github.com/14dtj/CocoQa/ Video: https://youtu.be/VQaXi1WydAU.