ESEIW 2022
Sun 18 - Fri 23 September 2022 Helsinki, Finland
Fri 23 Sep 2022 14:45 - 15:00 at Sonck - Session 5B - Development & Testing & Behavioral 2 Chair(s): Sheila Reinehr

Background. Issues are prevalent, and identifying the correct priority of the reported issues is crucial to reduce the maintenance effort and ensure higher software quality. There are several approaches for the automatic priority prediction, yet they do not fully utilize the related information that might influence the priority assignment. Our observation reveals that there are noticeable correlations between an issue’s priority and its category, e.g., an issue of bug category tends to be assigned with higher priority than an issue of document category. This correlation motivates us to employ multi-task learning to share the knowledge about issue’s category prediction and facilitating priority prediction.

Aims. This paper aims at providing an automatic approach for effective issue’s priority prediction, to reduce the burden of the project members and better manage the issues.

Method. We propose an issue priority prediction approach PRIMA with deep multi-task learning, which takes the issue category prediction as another task to facilitate the information sharing and learning. It consists of three main phases: 1) data preparation and augmentation phase, which allows data sharing beyond single task learning; 2) model construction phase, which designs shared layers to encode the semantics of textual descriptions, and taskspecific layers to model two tasks in parallel; it also includes the indicative attributes to better capture an issue’s inherent meaning; 3) model training phase, which enables eavesdropping by shared loss function between two tasks.

Results. Evaluations with four large-scale open-source projects show that PRIMA outperforms commonly-used and state-of-the-art baselines, with 32% -55% higher precision, and 28% - 56% higher recall. Compared with single task learning, the performance improvement reaches 18% in precision and 19% in recall. Results from our user study further prove its potential practical value.

Conclusions. The proposed approach provides a novel and effective way for issue priority prediction, and sheds light on jointly exploring other issue-management tasks.


Fri 23 Sep

Displayed time zone: Athens change

13:30 - 15:00
Session 5B - Development & Testing & Behavioral 2ESEM Technical Papers at Sonck
Chair(s): Sheila Reinehr Pontifícia Universidade Católica do Paraná (PUCPR)
13:30
15m
Full-paper
Potential Technical Debt and Its Resolution in Code Reviews: An Exploratory Study of the OpenStack and Qt Communities
ESEM Technical Papers
Liming Fu Wuhan University, Peng Liang Wuhan University, China, Zeeshan Rasheed Wuhan University, Zengyang Li Central China Normal University, Amjed Tahir Massey University, Xiaofeng Han Wuhan University, China
Link to publication DOI Pre-print
13:45
15m
Full-paper
MMF3: Neural Code Summarization Based on Multi-Modal Fine-Grained Feature Fusion
ESEM Technical Papers
Zheng Ma Shandong Normal University, Yuexiu Gao Shandong Normal University, Lei Lyu Shandong Normal University, Chen Lyu Shandong Normal University
14:00
15m
Full-paper
PG-VulNet: Detect Supply Chain Vulnerabilities in IoT Devices using Pseudo-code and Graphs
ESEM Technical Papers
Xin Liu Lanzhou University, Yixiong Wu Institute for Network Science and Cyberspace of Tsinghua University, Qingchen Yu Zhejiang University, Shangru Song Beijing Institute of Technology, Yue Liu Southeast University; Qi An Xin Group Corp., Qingguo Zhou Lanzhou University, Jianwei Zhuge Tsinghua University
14:15
15m
Full-paper
Heterogeneous Graph Neural Networks for Software Effort Estimation
ESEM Technical Papers
Hung Phan Iowa State University, Ali Jannesari Iowa State University
Pre-print
14:30
15m
Full-paper
Meetings and Mood - Related or Not? Insights from Student Software Projects
ESEM Technical Papers
Jil Klünder Leibniz Universität Hannover, Oliver Karras TIB - Leibniz Information Centre for Science and Technology
Pre-print
14:45
15m
Full-paper
A Tale of Two Tasks: Automated Issue Priority Prediction with Deep Multi-task Learning
ESEM Technical Papers
Yingling Li , Xing Che , Yuekai Huang Institute of Software, Chinese Academy of Sciences, Junjie Wang Institute of Software at Chinese Academy of Sciences, Song Wang York University, Yawen Wang Institute of Software, Chinese Academy of Sciences, Qing Wang Institute of Software at Chinese Academy of Sciences