Visualization of Knowledge Distribution across Development Teams using 2.5D Semantic Software Maps

被引:5
作者
Atzberger, Daniel [1 ]
Cech, Tim [1 ]
Jobst, Adrian [1 ]
Scheibel, Willy [1 ]
Limberger, Daniel [1 ]
Trapp, Matthias [1 ]
Doellner, Juergen [1 ]
机构
[1] Univ Potsdam, Digital Engn Fac, Hasso Plattner Inst, Potsdam, Germany
来源
PROCEEDINGS OF THE 17TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS (IVAPP), VOL 3 | 2022年
关键词
Topic Modeling; Software Visualization; Source Code Mining;
D O I
10.5220/0010991100003124
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
In order to detect software risks at an early stage, various software visualization techniques have been developed for monitoring the structure, behaviour, or the underlying development process of software. One of greatest risks for any IT organization consists in an inappropriate distribution of knowledge among its developers, as a projects' success mainly depends on assigning tasks to developers with the required skills and expertise. In this work, we address this problem by proposing a novel Visual Analytics framework for mining and visualizing the expertise of developers based on their source code activities. Under the assumption that a developer's knowledge about code is represented directly through comments and the choice of identifier names, we generate a 2D layout using Latent Dirichlet Allocation together with Multidimensional Scaling on the commit history, thus displaying the semantic relatedness between developers. In order to capture a developer's expertise in a concept. we utilize Labeled LDA trained on a corpus of Open Source projects. By mapping aspects related to skills onto the visual variables of 3D glyphs, we generate a 2.5D Visualization, we call KnowhowMap. We exemplify this approach with an interactive prototype that enables users to analyze the distribution of skills and expertise in an explorative way.
引用
收藏
页码:210 / 217
页数:8
相关论文
共 34 条
  • [1] Atzberger D., 2021, P 14 INT S VIS INF C
  • [2] Atzberger D., P 16 INT JO C COMP V, P112
  • [3] Bird Christian, 2015, The art and science of analyzing software data
  • [4] Latent Dirichlet allocation
    Blei, DM
    Ng, AY
    Jordan, MI
    [J]. JOURNAL OF MACHINE LEARNING RESEARCH, 2003, 3 (4-5) : 993 - 1022
  • [5] Bohnet Johannes, 2011, P 2 WORKSH MAN TECHN, P9, DOI [10.1145/1985362.1985365, DOI 10.1145/1985362.1985365]
  • [6] A survey on the use of topic models when mining software repositories
    Chen, Tse-Hsun
    Thomas, Stephen W.
    Hassan, Ahmed E.
    [J]. EMPIRICAL SOFTWARE ENGINEERING, 2016, 21 (05) : 1843 - 1919
  • [7] Cosentino V, 2015, 2015 22ND INTERNATIONAL CONFERENCE ON SOFTWARE ANALYSIS, EVOLUTION, AND REENGINEERING (SANER), P499, DOI 10.1109/SANER.2015.7081864
  • [8] Cox M. A., 2008, Handbook of Data Visualization, P315, DOI [DOI 10.1007/978-3-540-33037-014, DOI 10.1007/978-3-540-33037-0_14, 10.1007/978-3-540-33037-0_14]
  • [9] DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO
  • [10] 2-9