A Collaborative Platform for Software Evolution Visualization - Leveraging Meta-model Driven Measurements with Big Data Strengths

João Carlos Caldeira

2014

Abstract

This document describes a preliminary PhD thesis proposal that will hopefully lead to a collaborative framework and platform for Software Evolution Visualization (SEV). We sustain our decision to follow this research area by evaluating recent works which have shown that there is a need for multi-metrics, multi-perspective and multi-strategy approaches to SEV as summarized by (Novais, et al., 2013). The authors identify some research niches such as missing case studies, tool comparisons and experiments with the aim of predicting defects, improve software quality and development processes. Another missing aspect relates to the presentation of real scalable visualization and dependency impact among projects. It is also recognized that there is little formal validation and collaboration in this area, most likely because the data is scarce, dispersed and not widely shared by each individual researcher. The lack of empirical studies is a real constraint to allow the community to perform benchmarking and compare methodologies and results. In other words, the SEV community has failed to provide sound evidences, through empirical validation studies, of the impact of using the technology they created. In fact SEV research deliverables provide visual insights that are expected to help understand complex software artefacts and ultimately contribute to improve their quality and the maintenance process itself. The goals of our research consist on proposing a structured approach to (i) collect data from public domain software repositories, (ii) extract complexity and quality metrics using a meta-model driven measurement approach (M2DM), (iii) store and eventually transform those metrics by adopting big data technologies for scalability sake, (iv) visualize software evolution, along the corresponding metrics, in a collaborative fashion, allowing to identify patterns and trends. The aforementioned approach is expected to scaffold exploratory activities on top of the collected data, allowing the community to do benchmarking, evaluate software engineering best practices and assess software engineering research questions by means of empirical studies (Goulão, et al., 2012).

References

  1. Abreu, F. B., 2001. Using OCL to formalize object oriented metrics definitions, Lisbon: Technical Report ES007/2001.
  2. Alam, S. & Dugerdil, P., s.d. EvoSpaces Visualization Tool: Exploring Software Architecture in 3D, Geneva, Switzerland: s.n.
  3. Bacchelli, A., Rigotti, F., Hattori, L. & Lanza, M., s.d. Manhattan- 3D City Visualizations in Eclipse, Switzerland: University of Lugano.
  4. Balzer, M., Deussen, O. & Lewerentz, C., 2005. Voronoi Treemaps for the Visualization of Software Metrics, s.l.: s.n.
  5. Beyer, D. & Hassan, A. E., 2006. Animated Visualization of Software History using Evolution Storyboards. s.l., IEEE.
  6. Beyer, D. & Hassan, A. E., 2006. Evolution Storyboards: Visualization of Software Structure Dynamics. s.l., IEEE.
  7. Breivold, H. P., Crnkovic, I. & Larsson, M., 2011. A systematic review of software architecture evolution research. Information and Software Technology.
  8. Burch, M., Diehl, S. & WeiB3gerber, P., 2005. EPOSee - A Tool For Visualizing Software Evolution, Eichstatt,Germany: s.n.
  9. Coimbra, P. J., 2013. An Eclipse Plug-in for Metamodel Driven Measurement, Lisbon: ISCTE-IUL.
  10. D'Ambros, M., Lanza, M. & Lungu, M., 2006. The Evolution Radar: Visualizing Integrated Logical Coupling Information. Shanghai, China, ACM.
  11. Erra, U., Scanniello, G. & Capece, N., 2012. Visualizing the Evolution of Software Systems using the Forest Metaphor. s.l., s.n.
  12. Gonzalez-Torres, A. et al., 2011. Maleku: an evolutionary visual software analytics tool for providing insights into software evolution. Williamsburg VA, USA, IEEE.
  13. Goulão, M., Fonte, N., Wermelinger, M. & Abreu, F. B. e., 2012. Software Evolution Prediction using Seasonal Time Analysis: A Comparative Study. s.l., s.n.
  14. Hanakawa, N., 2007. Visualization for software evolution based on logical coupling and module coupling. s.l., IEEE.
  15. Holt, R. & Pak, J. Y., 1996. GASE: Visualizing Software Evolution-in-the-Large, Toronto, Canada: s.n.
  16. Hong, Q., Kim, S., Cheung, S. & Bird, C., 2011. Understanding a Developer Social Network and its Evolution. s.l., IEEE.
  17. Langelier, G., Sahraoui, H. & Poulin, P., 2008. Exploring the Evolution of Software Quality with Animated Visualization. Montréal, Canada, IEEE.
  18. Lanza, M. et al., 2013. Manhattan: Supporting Real-Time Visual Team Activity Awareness. San Francisco, USA, IEEE.
  19. Lanza, M. & Ducasse, S., 2002. Understanding software evolution using a Combination of Software Visualization and Software Metrics, Berne, Switzerland: s.n.
  20. Lanza, M., Gall, H. & Dugerdil, P., 2009. EvoSpaces: Multi-dimensional Navigation Spaces for Software Evolution. s.l., IEEE.
  21. Liu, G., Zhang, M. & Yan, F., 2010. Large-Scale Social Network Analysis based on MapReduce. s.l., IEEE.
  22. Novais, R. L., 2013. Visualizando Evolução de Software Em Detalhes, Salvador: s.n.
  23. Novais, R. L. et al., 2011. An Interactive Differential and Temporal Approach to Visually Analyze Software Evolution, s.l.: IEEE.
  24. Novais, R. et al., 2012. On the Proactive and Interactive Visualization for Feature Evolution Comprehension: An Industrial Investigation. Zurich, Switzerland, s.n.
  25. Pérez, J., Deshayes, R., Goeminne, M. & Mens, T., 2012. SECONDA: Software Ecosystem Analysis Dashboard. s.l., IEEE.
  26. Renato Lima Novais, A. T. T. S. M. M. N. Z., 2013. Software evolution visualization: A systematic mapping study. Information and Software Technology, 31 May.
  27. Ripley, R. M., Sarma, A. & Hoek, A. v. d., s.d. A Visualization for Software Project Awareness and Evolution, Irvine, CA 92697-3425 USA: Donald Bren School of Information and Computer Sciences.
  28. Sakamoto, Y., Matsumoto, S. & Nakamura, M., 2012. Integrating Service Oriented MSR Framework and Google Chart Tools for Visualizing Software Evolution. s.l., IEEE.
  29. Shollo, A. & Pandazo, K., 2008. Improving presentations of software metrics indicators using visualization techniques, Göteborg, Sweden: s.n.
  30. Simmhan, Y. et al., 2013. Cloud-based Software Platform For Big Data Analytics In Smart Grids. s.l., IEEE.
  31. Sun, X., Gao, B., Fan, L. & An, W., 2012. s.l., IEEE.
  32. Ulges, A., 2005. Visualizing Software Evolution, Kaiserslautern: s.n.
  33. Vasa, R., 2010. Growth and Change Dynamics in Open Source Software Systems, Melbourne, Australia: s.n.
  34. Wu, J., Holt, R. C. & Hassan, A. E., 2004. Exploring Software Evolution Using Spectrographs, s.l.: s.n.
  35. Zhang, D., 2012. Software Analytics in Practice - Approaches and Experiences. Zurich, Switzerland, IEEE.
  36. Zhang, D., Dang, Y. & Han, S., 2012. Teaching and Training for Software Analytics. s.l., IEEE.
  37. Zhang, D. & Xie, T., 2012. Software Analytics in Practice:Mini Tutorial. Zurich, Switzerland, IEEE.
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Paper Citation


in Harvard Style

Carlos Caldeira J. (2014). A Collaborative Platform for Software Evolution Visualization - Leveraging Meta-model Driven Measurements with Big Data Strengths . In Doctoral Consortium - DCMODELSWARD, (MODELSWARD 2014) ISBN Not Available, pages 12-16


in Bibtex Style

@conference{dcmodelsward14,
author={João Carlos Caldeira},
title={A Collaborative Platform for Software Evolution Visualization - Leveraging Meta-model Driven Measurements with Big Data Strengths},
booktitle={Doctoral Consortium - DCMODELSWARD, (MODELSWARD 2014)},
year={2014},
pages={12-16},
publisher={SciTePress},
organization={INSTICC},
doi={},
isbn={Not Available},
}


in EndNote Style

TY - CONF
JO - Doctoral Consortium - DCMODELSWARD, (MODELSWARD 2014)
TI - A Collaborative Platform for Software Evolution Visualization - Leveraging Meta-model Driven Measurements with Big Data Strengths
SN - Not Available
AU - Carlos Caldeira J.
PY - 2014
SP - 12
EP - 16
DO -