TY - GEN
T1 - Categorizing and Visualizing Issue Tickets to Better Understand the Features Implemented in Existing Software Systems
AU - Ishizuka, Ryo
AU - Washizaki, Hironori
AU - Fukazawa, Yoshiaki
AU - Saito, Shinobu
AU - Ouji, Saori
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - Acquiring knowledge and context of the ongoing project is one of the most challenging issues for new project members. New project members need to comprehend the contents of features already implemented in software systems. However, most software documents (e.g., flow chart and data-model) were initially created and not updated. Herein we focus on tickets issued during the project period because they are created based on stakeholders' requirements as a project evolves. We propose a novel approach to categorize and visualize tickets to better understand the features implemented in the existing software systems. Specifically, tickets are grouped by a clustering method and the number of clusters (i.e., ticket categories) is automatically estimated. Then the ticket categories are visualized by (i) creating a heatmap of the tickets lifetimes, (ii) extracting keywords of the ticket categories, and (iii) analyzing the relationships between ticket categories. We applied our approach to an NTT software development project. Additionally, we interviewed the project members and external experts to evaluate the effectiveness of our approach. Our approach helps comprehend the features of the software system.
AB - Acquiring knowledge and context of the ongoing project is one of the most challenging issues for new project members. New project members need to comprehend the contents of features already implemented in software systems. However, most software documents (e.g., flow chart and data-model) were initially created and not updated. Herein we focus on tickets issued during the project period because they are created based on stakeholders' requirements as a project evolves. We propose a novel approach to categorize and visualize tickets to better understand the features implemented in the existing software systems. Specifically, tickets are grouped by a clustering method and the number of clusters (i.e., ticket categories) is automatically estimated. Then the ticket categories are visualized by (i) creating a heatmap of the tickets lifetimes, (ii) extracting keywords of the ticket categories, and (iii) analyzing the relationships between ticket categories. We applied our approach to an NTT software development project. Additionally, we interviewed the project members and external experts to evaluate the effectiveness of our approach. Our approach helps comprehend the features of the software system.
KW - interviewing
KW - issue ticket
KW - machine learning
KW - reverse engineering
KW - software comprehension
KW - text mining
UR - http://www.scopus.com/inward/record.url?scp=85078089373&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85078089373&partnerID=8YFLogxK
U2 - 10.1109/IWESEP49350.2019.00013
DO - 10.1109/IWESEP49350.2019.00013
M3 - Conference contribution
AN - SCOPUS:85078089373
T3 - Proceedings - 2019 10th International Workshop on Empirical Software Engineering in Practice, IWESEP 2019
SP - 25
EP - 30
BT - Proceedings - 2019 10th International Workshop on Empirical Software Engineering in Practice, IWESEP 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th International Workshop on Empirical Software Engineering in Practice, IWESEP 2019
Y2 - 13 December 2019 through 14 December 2019
ER -