When to use data from other projects for effort estimation

Collecting the data required for quality prediction within a development team is time-consuming and expensive. An alternative to make predictions using data that crosses from other projects or even other companies. We show that with/without relevancy filtering, imported data performs the same/worse (respectively) than using local data. Therefore, we recommend the use of relevancy filtering whenever generating estimates using data from another project.

[1]  Emilia Mendes,et al.  A Comparative Study of Cost Estimation Models for Web Hypermedia Applications , 2003, Empirical Software Engineering.

[2]  Martin J. Shepperd,et al.  Software project economics: a roadmap , 2007, Future of Software Engineering (FOSE '07).

[3]  Kjetil Moløkken-Østvold,et al.  A survey on software estimation in the Norwegian industry , 2004, 10th International Symposium on Software Metrics, 2004. Proceedings..

[4]  Magne Jørgensen,et al.  A review of studies on expert estimation of software development effort , 2004, J. Syst. Softw..

[5]  Harald C. Gall,et al.  Cross-project defect prediction: a large scale experiment on data vs. domain vs. process , 2009, ESEC/SIGSOFT FSE.

[6]  Thomas J. Ostrand,et al.  \{PROMISE\} Repository of empirical software engineering data , 2007 .

[7]  Barbara A. Kitchenham,et al.  Effort estimation using analogy , 1996, Proceedings of IEEE 18th International Conference on Software Engineering.

[8]  Karen T. Lum,et al.  Selecting Best Practices for Effort Estimation , 2006, IEEE Transactions on Software Engineering.

[9]  Barbara Kitchenham,et al.  Software cost models , 1984 .

[10]  Barry W. Boehm,et al.  Software Engineering Economics , 1993, IEEE Transactions on Software Engineering.

[11]  Barry W. Boehm,et al.  The business case for automated software engineering , 2007, ASE.

[12]  Thong Ngee Goh,et al.  A study of project selection and feature weighting for analogy based software cost estimation , 2009, J. Syst. Softw..

[13]  Guilherme Horta Travassos,et al.  Cross versus Within-Company Cost Estimation Studies: A Systematic Review , 2007, IEEE Transactions on Software Engineering.

[14]  Natalia Juristo Juzgado,et al.  Using differences among replications of software engineering experiments to gain knowledge , 2009, 2009 3rd International Symposium on Empirical Software Engineering and Measurement.

[15]  Anette C. Lien,et al.  A survey on software estimation in the Norwegian industry , 2004 .

[16]  Magne Jørgensen,et al.  The Impact of Lessons-Learned Sessions on Effort Estimation and Uncertainty Assessments , 2009, IEEE Transactions on Software Engineering.

[17]  Ayse Basar Bener,et al.  On the relative value of cross-company and within-company data for defect prediction , 2009, Empirical Software Engineering.

[18]  Barry W. Boehm,et al.  Software development cost estimation approaches — A survey , 2000, Ann. Softw. Eng..

[19]  Chris F. Kemerer,et al.  An empirical validation of software cost estimation models , 1987, CACM.

[20]  Emilia Mendes,et al.  Further comparison of cross-company and within-company effort estimation models for Web applications , 2004 .

[21]  Guilherme Horta Travassos,et al.  A systematic review of cross- vs. within- company cost estimation studies , 2006 .

[22]  Martin J. Shepperd,et al.  Comparing Software Prediction Techniques Using Simulation , 2001, IEEE Trans. Software Eng..

[23]  Emilia Mendes,et al.  A replicated comparison of cross-company and within-company effort estimation models using the ISBSG database , 2005, 11th IEEE International Software Metrics Symposium (METRICS'05).

[24]  Emilia Mendes,et al.  Why comparative effort prediction studies may be invalid , 2009, PROMISE '09.