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Issue No.04 - July-Aug. (2013 vol.30)
pp: 31-37
Tim Menzies , West Virginia University
Thomas Zimmermann , Microsoft Research
ABSTRACT
The guest editors of this special issue of IEEE Software invited submissions that reflected the benefits (and drawbacks) of software analytics, an area of explosive growth. They had so many excellent submissions that they had to split this special issue into two volumes--you'll see even more content in the September/October issue. They divided the articles on conceptual grounds, so both volumes will feature equally excellent work. The Web extra at http://youtu.be/nO6X0azR0nw is a video interview in which IEEE Software editor in chief Forrest Shull speaks with Tim Menzies about the growing importance of software analytics.
INDEX TERMS
Special issues and sections, Data analysis, Decision making, Data models, Software development, Software algorithms, Software engineering, measurement, software analytics, big data, analysis, metrics
CITATION
Tim Menzies, Thomas Zimmermann, "Software Analytics: So What?", IEEE Software, vol.30, no. 4, pp. 31-37, July-Aug. 2013, doi:10.1109/MS.2013.86
REFERENCES
1. T.H. Davenport, J.G. Harris, and R. Morison, Analytics at Work: Smarter Decisions, Better Results, Harvard Business Review Press, 2010.
2. R. Buse and T. Zimmermann, “Information Needs for Software Development Analytics,” Proc. Int'l Conf. Software Eng. (ICSE), IEEE CS, 2012; http://thomas-zimmermann.com/publications/ detailsbuse-icse-2012.
3. T. Menzies and F. Shull, “Empirical Software Engineering,” tech. briefing, Proc. Int'l Conf. Software Eng., IEEE CS, 2011, http://2011.icse-conferences.org/technical-briefings #381.
4. C. Bird et al., “Does Distributed Development Affect Software Quality? An Empirical Case Study of Windows Vista,” Proc. 31st Int'l Conf. Software Eng., IEEE CS, 2009, pp. 518–528.
5. A. Porter and R. Selby, “Empirically Guided Software Development Using Metric-Based Classification Trees,” IEEE Software, vol. 7, no. 2, 1990, pp. 46–54.
6. F. Peters et al., “Balancing Privacy and Utility in Cross-Company Defect Prediction,” to be published in IEEE Trans. Software Eng., 2013.
7. I. Witten, E. Frank, and M. Hall, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, 2011.
8. R. Duda, P. Hart, and D. Stork, Pattern Recognition, 2nd ed., Wiley-Interscience, 2000.
9. E. Kocaganeli et al., “Distributed Development Considered Harmful?,” to be published in Proc. Int'l Conf. Software Eng. (ICSE), IEEE CS, 2013.
10. H. Pareek and P. Ravikumar, “Human Boosting,” to be published in Proc. Int'l Conf. Machine Learning, 2013; http://jmlr.csail.mit.edu/proceedings/papers/ v28pareek13.pdf.
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