Dataset For Software Defect Predictions

SOFTLAB: dataset were from a Turkish software company which develops embedded controllers for home appliances [1] <br>RELINK: Dataset with has 26 static code features and contains three projects (Apache HTTP Server (Apache), OpenIntents Safe (Safe), and ZXing) [2]<br>NASA: Dataset from e C-based and are extracted from a software system which is made of a set of static code features [3].<br>4. AEEEM: dataset was compiled by Marco et al. [4], and contains five open source projects with 5,371 data points. The projects include: Apache Lucene (LC), Equinox (EQ), Eclipse JDT Core (JDT), Eclipse PDE UI (PDE) and Mylyn (ML).<br><br>AEEEM software defect datasets which has a format of ARFF was collected by D’Ambros et al. [1]<br>[1] M. D’Ambros, M. Lanza, and R. Robbes, “Evaluating defect prediction approaches: A benchmark and an extensive comparison,”Empirical Softw. Engg., vol. 17, no. 4-5, pp. 531–577, Aug. 2012.<br>[3] ] B. Ghotra, S. McIntosh, and A. E. Hassan, “Revisiting the impact of classification techniques on the performance of defect prediction models,” in 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering, vol. 1. IEEE, 2015, pp. 789–800.<br>[4] M. D’Ambros, M. Lanza, and R. Robbes, “Evaluating defect prediction approaches: a benchmark and an extensive comparison,” Empirical Software Engineering, vol. 17, no. 4, pp. 531–577, 2012