Visualizing Feature-Level Evolution in Product Lines: A Research Preview

被引:4
|
作者
Hinterreiter, Daniel [1 ]
Gruenbacher, Paul [1 ]
Praehofer, Herbert [2 ]
机构
[1] Johannes Kepler Univ Linz, Inst Software Syst Engn, Christian Doppler Lab MEVSS, Linz, Austria
[2] Johannes Kepler Univ Linz, Inst Syst Software, Linz, Austria
关键词
Product lines; Evolution; Visualization;
D O I
10.1007/978-3-030-44429-7_21
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
[Context and motivation] Software product lines evolve frequently to address customer requirements in different domains. This leads to a distributed engineering process with frequent updates and extensions. [Question/problem] However, such changes are typically managed and tracked at the level of source code while feature-level awareness about software evolution is commonly lacking. In this research preview paper we thus present an approach visualizing the evolution in software product lines at the level of features. [Principal ideas/results] Specifically, we extend feature models with feature evolution plots to visualize changes at a higher level. Our approach uses static code analyses and a variation control system to compute the evolution data for visualisation. As a preliminary evaluation we report selected examples of applying our approach to a cyberphysical ecosystem from the field of industrial automation. [Contribution] Integrating visualisations into state-of-the-art feature models can contribute to better integrate requirements-level and code-level perspectives during product line evolution.
引用
收藏
页码:300 / 306
页数:7
相关论文
共 50 条
  • [21] Feature-level data fusion for bimodal person recognition
    Chibelushi, CC
    Mason, JSD
    Deravi, F
    SIXTH INTERNATIONAL CONFERENCE ON IMAGE PROCESSING AND ITS APPLICATIONS, VOL 1, 1997, (443): : 399 - 403
  • [22] Learning User Perceived Clusters with Feature-Level Supervision
    Cheng, Ting-Yu
    Lin, Kuan-Hua
    Gong, Xinyang
    Liu, Kang-Jun
    Wu, Shan-Hung
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 29 (NIPS 2016), 2016, 29
  • [23] Feature-level Approach for the Evaluation of Text Classification Models
    Bracamonte, Vanessa
    Hidano, Seira
    Nakamura, Toru
    Kiyomoto, Shinsaku
    PROCEEDINGS OF THE 17TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS (IVAPP), VOL 3, 2022, : 164 - 170
  • [24] Capturing Feature-Level Irregularity in Disease Progression Modeling
    Zheng, Kaiping
    Wang, Wei
    Gao, Jinyang
    Ngiam, Kee Yuan
    Ooi, Beng Chin
    Yip, Wei Luen James
    CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, 2017, : 1579 - 1588
  • [25] Human Guided Linear Regression with Feature-Level Constraints
    Gress, Aubrey
    Davidson, Ian
    THIRTY-SECOND AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTIETH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / EIGHTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2018, : 3053 - 3060
  • [26] Feature-Level Fusion of Surface Electromyography for Activity Monitoring
    Xi, Xugang
    Tang, Minyan
    Luo, Zhizeng
    SENSORS, 2018, 18 (02):
  • [27] FLSL: Feature-level Self-supervised Learning
    Su, Qing
    Netchaev, Anton
    Li, Hai
    Ji, Shihao
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 36 (NEURIPS 2023), 2023,
  • [28] Feature-level signal processing for odor sensor arrays
    Roppel, T
    Dunman, K
    Padgett, M
    Wilson, D
    Lindblad, T
    IECON '97 - PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON INDUSTRIAL ELECTRONICS, CONTROL, AND INSTRUMENTATION, VOLS. 1-4, 1997, : 218 - 221
  • [29] An investigation into feature-level fusion of face and fingerprint biometrics
    Computer Vision Laboratory, Department of Architecture and Planning , University of Sassari, Palazzo del Pou Salit Piazza Duomo 6, Alghero
    07041, Italy
    不详
    07041, Italy
    Multibiometrics for Hum. Identif., (120-142):
  • [30] Feature-level Frankenstein: Eliminating Variations for Discriminative Recognition
    Liu, Xiaofeng
    Li, Site
    Kong, Lingsheng
    Xie, Wanqing
    Jia, Ping
    You, Jane
    Kumar, B. V. K.
    2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019), 2019, : 637 - 646