Blogs (1) >>
ASE 2019
Sun 10 - Fri 15 November 2019 San Diego, California, United States
Tue 12 Nov 2019 16:00 - 16:20 at Cortez 2&3 - Code and Artifact Analysis Chair(s): Sarah Nadi

Emotion awareness research in SE context has been growing in recent years. Currently, researchers often rely on textual communication records to extract emotion states using natural language processing techniques. However how well these extracted emotion states reflect people’s real emotions has not been thoroughly investigated. In this paper, we report a multi-level, longitudinal empirical study with 82 individual members in 27 project teams. We collected their self-reported retrospective emotion states on a weekly basis during their year-long projects and also extract corresponding emotions from the textual communication records. We then model and compare the dynamics of these two types of emotions using multiple statistical and time series analysis methods. Our analyses yield a rich set of findings. The most important one is that the dynamics of emotions extracted using text-based algorithms often do not well reflect the dynamics of self-reported retrospective emotions. Besides, the extracted emotions match self-reported retrospective emotions better at the team level. Our results also suggest that individual personalities and team’s emotion display norms significantly impact the match/mismatch. Our results should warn the research community about the limitations and challenges of applying text-based emotion recognition tools in SE research.

Tue 12 Nov

16:00 - 17:40: Papers - Code and Artifact Analysis at Cortez 2&3
Chair(s): Sarah NadiUniversity of Alberta
ase-2019-papers16:00 - 16:20
Emotions Extracted from Text vs. True Emotions –An Empirical Evaluation in SE Context
Yi WangShenzhen University
ase-2019-Journal-First-Presentations16:20 - 16:40
Collaborative feature location in models through automatic query expansion
Francisca PérezSVIT Research GroupUniversidad San Jorge, Jaime FontSan Jorge University, Spain, Lorena ArcegaSan Jorge University, Carlos CetinaSan Jorge University, Spain
Link to publication
ase-2019-papers16:40 - 17:00
Learning from Examples to Find Fully Qualified Names of API Elements in Code Snippets
C M Khaled SaifullahDepartment of Computer Science, University of Saskatchewan, Muhammad AsaduzzamanPostdoctoral Research Fellow, Software Analysis and Intelligence Lab, Queen's University, Canada, Chanchal K. RoyUniversity of Saskatchewan
ase-2019-papers17:00 - 17:20
Inferring Program Transformations From Singular Examples via Big Code
Jiajun JiangPeking University, Luyao RenPeking University, Yingfei XiongPeking University, Lingming ZhangThe University of Texas at Dallas
Link to publication Pre-print
ase-2019-Journal-First-Presentations17:20 - 17:40
Extracting and studying the Logging-Code-Issue-Introducing changes in Java-based large-scale open source software systems
Boyuan ChenYork University, Zhen Ming (Jack) JiangYork University
Link to publication