Research perspective on supporting software engineering via physical 3D models

Building architects, but also civil or mechanical engineers often build from their designs physical 3D models for a better presentation, comprehension, and communication among stakeholders. Software engineers usually create visualizations of their software designs as digital objects to be presented on a screen only. 3D software visualization metaphors, such as the software city metaphor, provide a basis for exporting those on-screen software visualizations into physical models. This can be achieved by 3D-printers to transfer the advantages of real, physical, tangible architecture models from traditional engineering disciplines to software engineering. We present a new research perspective of using physical models to support software engineering. Furthermore, we describe four envisioned usage scenarios for physical models which provide a plethora of new research topics. To examine the benefits of our concept, we investigate the first usage scenario by evaluating the impact of using physical models on program comprehension in teams through a first controlled experiment. Since the usage of physical models had a diverging influence for our chosen task set, we report on the qualitative results in this paper. We observed that the physical models improved the team-based program comprehension process for specific tasks by initiating gesture-based interaction.

[1]  Romain Robbes,et al.  Software systems as cities: a controlled experiment , 2011, 2011 33rd International Conference on Software Engineering (ICSE).

[2]  Michel R. V. Chaudron,et al.  Interactive Views to Improve the Comprehension of UML Models - An Experimental Validation , 2007, 15th IEEE International Conference on Program Comprehension (ICPC '07).

[3]  Hong Yul Yang,et al.  A Solar System Metaphor for 3D Visualisation of Object Oriented Software Metrics , 2004, InVis.au.

[4]  Kellogg S. Booth,et al.  Fish tank virtual reality , 1993, INTERCHI.

[5]  Michael Balzer,et al.  Software landscapes: visualizing the structure of large software systems , 2004, VISSYM'04.

[6]  Samin Ishtiaq,et al.  "Can I Implement Your Algorithm?": A Model for Reproducible Research Software , 2014, ArXiv.

[7]  R. Lathe Phd by thesis , 1988, Nature.

[8]  Michael Gleicher,et al.  The challenges of 3D interaction: a CHI '94 workshop , 1994, SGCH.

[9]  Wilhelm Hasselbring,et al.  Research perspective on supporting software engineering via physical 3D models , 2015, IEEE Working Conference on Software Visualization.

[10]  S. Goldin-Meadow,et al.  Hearing Gesture: How Our Hands Help Us Think , 2003 .

[11]  Marcelo R. Campo,et al.  An Overview of 3D Software Visualization , 2009, IEEE Transactions on Visualization and Computer Graphics.

[12]  Claes Wohlin,et al.  Experimentation in Software Engineering , 2000, The Kluwer International Series in Software Engineering.

[13]  W. Shadish,et al.  Experimental and Quasi-Experimental Designs for Generalized Causal Inference , 2001 .

[14]  H. Levene Robust tests for equality of variances , 1961 .

[15]  Jarke J. van Wijk,et al.  Botanical visualization of huge hierarchies , 2001, IEEE Symposium on Information Visualization, 2001. INFOVIS 2001..

[16]  John C. Grundy,et al.  A 3D metaphor for software production visualization , 2003, Proceedings on Seventh International Conference on Information Visualization, 2003. IV 2003..

[17]  Colin Ware,et al.  Reevaluating stereo and motion cues for visualizing graphs in three dimensions , 2005, APGV '05.

[18]  Michael Gleicher,et al.  The challenges of 3D interaction , 1994, CHI Conference Companion.

[19]  Malcolm Munro,et al.  Virtual but visible software , 2000, 2000 IEEE Conference on Information Visualization. An International Conference on Computer Visualization and Graphics.

[20]  Jason Leigh,et al.  Visualizing object-oriented software in virtual reality , 2001, Proceedings 9th International Workshop on Program Comprehension. IWPC 2001.

[21]  Patrick Ogao,et al.  Evaluation of software visualization tools: Lessons learned , 2009, 2009 5th IEEE International Workshop on Visualizing Software for Understanding and Analysis.

[22]  Václav Rajlich,et al.  Towards standard for experiments in program comprehension , 1997, Proceedings Fifth International Workshop on Program Comprehension. IWPC'97.

[23]  Y. B. Wah,et al.  Power comparisons of Shapiro-Wilk , Kolmogorov-Smirnov , Lilliefors and Anderson-Darling tests , 2011 .

[24]  S. Shapiro,et al.  An Analysis of Variance Test for Normality (Complete Samples) , 1965 .

[25]  Michele Lanza,et al.  Visualizing Software Systems as Cities , 2007, 2007 4th IEEE International Workshop on Visualizing Software for Understanding and Analysis.

[26]  Thomas Ball,et al.  Software Visualization in the Large , 1996, Computer.

[27]  Hausi A. Müller,et al.  How do program understanding tools affect how programmers understand programs? , 1997, Proceedings of the Fourth Working Conference on Reverse Engineering.

[28]  R. Likert “Technique for the Measurement of Attitudes, A” , 2022, The SAGE Encyclopedia of Research Design.