TY - JOUR
T1 - Towards a knowledge base for software developers to choose suitable traceability techniques
AU - Kaiya, Haruhiko
AU - Hazeyama, Atsuo
AU - Ogata, Shinpei
AU - Okubo, Takao
AU - Yoshioka, Nobukazu
AU - Washizaki, Hironori
N1 - Publisher Copyright:
© 2019 The Author(s). Published by Elsevier B.V.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2019
Y1 - 2019
N2 - Huge amount of techniques for creating, maintaining and/or recovering traceability among software development artifacts have been proposed. It is thus not easy for a potential user of such techniques to choose a technique suitable for his/her project because too many techniques exist and projects are different from each other. In this paper, we proposed a model for characterizing a traceability technique with respect to its users. The model can become a meta-model of the knowledge base for such users. The model is designed on the basis of the contents of existing technical papers so that we can easily describe model instances on the basis of technical papers. The model is represented in a feature model, and it has four mandatory features: source, destination, consequence and process. The user can at least understand a technique is unsuitable for him/her by referring such features. If a technique estimates the traceability relationships, the instance of its feature model may contain the quality metrics of its estimation such as precision and recall. It has also several optional features: assumptions, preprocess and tool. Although we sometimes cannot obtain such optional features from technical papers, they are so helpful for a user to decide a technique suitable for him/her. On the basis of the feature model, we described model instances of several techniques in technical papers. We also examined and discussed who the suitable user for each technique is.
AB - Huge amount of techniques for creating, maintaining and/or recovering traceability among software development artifacts have been proposed. It is thus not easy for a potential user of such techniques to choose a technique suitable for his/her project because too many techniques exist and projects are different from each other. In this paper, we proposed a model for characterizing a traceability technique with respect to its users. The model can become a meta-model of the knowledge base for such users. The model is designed on the basis of the contents of existing technical papers so that we can easily describe model instances on the basis of technical papers. The model is represented in a feature model, and it has four mandatory features: source, destination, consequence and process. The user can at least understand a technique is unsuitable for him/her by referring such features. If a technique estimates the traceability relationships, the instance of its feature model may contain the quality metrics of its estimation such as precision and recall. It has also several optional features: assumptions, preprocess and tool. Although we sometimes cannot obtain such optional features from technical papers, they are so helpful for a user to decide a technique suitable for him/her. On the basis of the feature model, we described model instances of several techniques in technical papers. We also examined and discussed who the suitable user for each technique is.
KW - Feature Model
KW - Knowledge Base
KW - Software Traceability
KW - Survey
KW - User Perspective
UR - http://www.scopus.com/inward/record.url?scp=85076255683&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85076255683&partnerID=8YFLogxK
U2 - 10.1016/j.procs.2019.09.276
DO - 10.1016/j.procs.2019.09.276
M3 - Conference article
AN - SCOPUS:85076255683
SN - 1877-0509
VL - 159
SP - 1075
EP - 1084
JO - Procedia Computer Science
JF - Procedia Computer Science
T2 - 23rd International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, KES 2019
Y2 - 4 September 2019 through 6 September 2019
ER -