Blogs (1) >>
ASE 2019
Sun 10 - Fri 15 November 2019 San Diego, California, United States
Tue 12 Nov 2019 16:20 - 16:40 at Cortez 2&3 - Code and Artifact Analysis Chair(s): Sarah Nadi

Collaboration with other people is a major theme in the information-seeking process. However, most existing works that address the location of features during the maintenance or evolution of software do not support collaboration, or they are focused on code as the main software artifact. Hence, collaborative feature location in models has not enjoyed much attention to date. In this work, we address this concern by proposing an approach, CoFLiM, that enables the collaboration of several domain experts in order to locate the model fragment of a target feature. CoFLiM uses the feature descriptions of the domain experts and their self-rated confidence level to automatically reformulate the relevant feature descriptions in a single query. This query guides the evolutionary algorithm of our approach that finds the model fragment of the feature being located. We evaluate CoFLiM in a real-world case study from our industrial partner. We analyze the impact of CoFLiM in terms of recall, precision, and the F-measure. Moreover, we compare the reformulation of CoFLiM with four baselines. We also perform a statistical analysis to show that the impact of the results is significant. Our results show that collaboration pays off in the location of features in models. The results also show that the self-rated confidence level can be used to locate features in models. Finally, the results show that there are no significant improvements when more than three domain experts are involved, which is relevant in those industrial contexts where the availability of domain experts is scarce.

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