## 4 New Experimental Data Set, Benchmark Data Sets and Software Measurement Data  
In this research, a new data set named SFP XP-TDD comprised of several metrics of three separate software systems was created. The aforementioned three different software were developed as distinct domains of the same automation system, consisting of 472,443 lines of code for three software in total. Analysis, coding, testing, and maintenance phases of these software systems were continuously monitored in each iteration in accordance with the spiral software development methodology of XP. In accordance with TDD practice, the basic code was written first, followed by the functional code. The functional code was revised to be successful in the tests determined with TDD. These steps were repeated until all the tests were successful. New versions were created every three weeks using the continuous integration practice of XP. The testing of software systems in the data sets were considered separately in each phase starting from the analysis phase. In accordance with collective ownership practice of XP, it was subjected to the white-box unit testing method developed by the programmers and the team leader. In the final phase of the testing process, verification and acceptance testing was performed by the client with real production data. The delivery period of the software was completed with the receipt of approval being delivered by the client. A previously developed project management software was used actively throughout the process to keep track of all these steps. After the acceptance testing, faults observed in the software were recorded in the software project management tool in the maintenance phase. There was another separate software to track the errors of the software developed in the study. This software has logged errors that occurred during the six months following the use of the master versions of the developed software. Creation of 'the Classes with Error section' took place in the data set in this manner. In this study, we have also used data sets from Eclipse and JIRA, and the Defect data set repository [48]. All bug prediction data sets were given in Table 1. By the nature of DL, it is necessary to use data with a sizable number of samples; thus, while selecting data sets, those data sets with numerous records were chosen rather than those comprising of a small number of samples. The first data set is from the Eclipse Equinox and Eclipse Core software project which were developed in the Java language system. The goal of the Equinox project is to be a first-class open service gateway initiative (OSGi) [48]. The second data set is form JIRA Defect prediction data set (Apache Active MQ) [49] which was developed using the Java language for the Java Message Service client infrastructure. In Table 4, general information about each project in the data sets are presented. The name of the last data set is SFP XP-TDD. This data set has been made publicly available for researchers to conduct their own studies and to have reproducible experimental results. The SFP XP-TDD data set consists of 1,713 classes. There are approximately 300 code lines for each class in the data set. On average, 3.7% of each class consists of comments. The Table 4 below displays details about the metrics in the data set. The definition and description of these metrics are presented in Table 4.  
In this study, we have also used datasets from Eclipse and JIRA. Eclipse and JIRA data sets are among the most-used datasets in the literature regarding software error estimation. Defect dataset repository [48] and all bug prediction datasets are given in Table 5. By the nature of DL, it is necessary to use data with a sizable number of samples; as a result, datasets with numerous records were chosen above those comprising a small number of samples while selecting the dataset. First dataset is from Eclipse Equinox and Eclipse Core software project which were developed in the Java language system.