Failure is a four-letter word - A parody in empirical research

ACM International Conference Proceeding Series

Volumn , Issue , 2011, Pages

  Zeller, Andreas ^a,b   Zimmermann, Thomas ^a   Bird, Christian ^a  

^a SAARLAND UNIVERSITY   (Germany)

^b MICROSOFT RESEARCH   (United States)

Author keywords

Empirical Research; Parody

Indexed keywords

DATA SETS; EMPIRICAL RESEARCH; INITIAL FEEDBACK; LOGISTIC REGRESSIONS; PARODY; PRECISION AND RECALL; PREDICTIVE POWER; SAMPLE SETS; SOFTWARE FAILURE; SOFTWARE SYSTEMS; SOURCE CODES;

COBALT COMPOUNDS; COMPUTER PROGRAMMING; DEFECTS; MODELS; PREDICTIVE CONTROL SYSTEMS; SOFTWARE DESIGN;

SAFETY ENGINEERING;

EID: 80054055536     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2020390.2020395     Document Type: Conference Paper

References (14)

1. Zeller, A., Zimmermann, T., Premraj, R., Just, S., and Schröter, A. Eclipse bug data 2.0a. 2007.
2. Zimmermann, T., Premraj, R., and Zeller, A. Predicting Defects for Eclipse [Revised for Dataset Version 2.0a]. In Proceedings of the Third International Workshop on Predictor Models in Software Engineering (Minneapolis, MN, USA, 2007), ACM.
3. Benjamini, Y. and Hochberg, Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society. Series B (Methodological), 57 (1995), 289-300
4. Dowdy, S., Wearden, S., and Chilko, D. Statistics for research. John Wiley \& Sons, 2004.
5. Kan, S.H. Metrics and Models in Software Quality Engineering. Addison-Wesley, 2003.
6. Hannay, J.E., Sjoberg, D.I.K., and Dyba, T. A Systematic Review of Theory Use in Software Engineering Experiments. IEEE Transactions on Software Engineering, 33, 2 (February 2007), 87-107.
7. Zhou, M. and Mockus, A. Developer fluency: Achieving true mastery in software projects. In Proceedings of the eighteenth ACM SIGSOFT international symposium on Foundations of software engineering (2010), 137-146
8. Shadish, W., Cook, T., and Campbell, D. Experimental & Quasi-Experimental Designs for Generalized Causal Inference. Houghon Mifflin, Boston, 2002.
9. Hindle, A., Germán, D.M., and Holt, R.C. What do large commits tell us? A taxonomical study of large commits. In Mining Software Repositories (2008).
10. Menzies, T., Greenwald, J., and Frank, A. Data Mining Static Code Attributes to Learn Defect Predictors. IEEE Transactions on Software Engineering (2007).
11. Ramasubbu, N. and Balan, R.K. The impact of process choice in high maturity environments: An empirical analysis. In International Conference on Software Engineering (2009).
12. Ko, A.J., DeLine, R., and Venolia, G. Information Needs in Collocated Software Development Teams. In International Conference on Software Engineering (ICSE) (Minneapolis, MN, 2007), 344-353.
13. Huff, D. How to lie with statistics. Norton, New York, 1954.
14. Munroe, R. Significant. xkcd, http://xkcd.com/882/(Accessed May 1, 2011).