\{PROMISE\} Repository of empirical software engineering data

By G. Boetticher, Tim Menzies, Thomas J. Ostrand. 2007

761 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/f74d664f0c3451bd4c31cf973a549a6dc00897c6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Danijel Radjenovic
2. Zhimin He
3. M. Heričko
4. Shuo Wang
5. Fengdi Shu
6. Richard Torkar
7. Ye Yang
8. Ales Zivkovic

Citations in the corpus

• An Empirical Comparison of Model Validation Techniques for Defect Prediction Models (2017, methodology): “76 datasets are downloaded from the Tera-PROMISE repository [69], 12 clean NASA datasets are provided by Shepperd et al.”
• On the relative value of cross-company and within-company data for defect prediction (2009, background): “On the other hand, there are public data repositories including projects from companies such as NASA [1].”
• Software fault prediction metrics: A systematic literature review (2013, methodology): “In 2005, when the PROMISE repository [6] was released, the number of studies per year started to increase, reaching 14, 15 and 13 studies per year in the years 2008, 2009 and 2010, respectively.”
• Automated Parameter Optimization of Classification Techniques for Defect Prediction Models (2016, methodology): “This study would not have been possible without the data shared in the Tera-PROMISE repository [39], Shepperd et al.”
• An investigation on the feasibility of cross-project defect prediction (2011, methodology): “All these data sets are available at the PROMISE Data Repository (Boetticher et al. 2007).”
• Using Class Imbalance Learning for Software Defect Prediction (2013, background): “There is a rapid growth of SDP research after the PROMISE repository [27] was created in 2005.”
• Systematic literature review of machine learning based software development effort estimation models (2012, methodology): “If needed, PROMISE repository [64] is a convenient place for sharing SDEE data sets.”
• Software defect prediction using Bayesian networks (2012, methodology): “Based on these criteria we select public data sets Ant, Tomcat, Jedit, Velocity, Synapse, Poi, Lucene, Xalan, and Ivy from Promise data repository [5] (See Table 3 for the details of the datasets).”