This analysis is performed to test the hypotheses. The univariate regression analysis aims to determine if each individual stability metric is significantly correlated with PRDD. PRDD varies from 0.54 defects/KLOC to 4.56 defects/KLOC with an average of 1.78 defects/KLOC. In addition, Table 1 provides descriptive statistics for measures of PRDD from the releases of Apache Ant. Measures of the TBR metric vary from 37 days to 272 days with an average of ~122 days between releases. Deletions of classes and relationships were relatively low compared to additions and modifications. In general, class relationships were more stable than classes over the releases of Apache Ant. Table 1 provides descriptive statistics for the measures of the 17 stability metrics that were collected from the releases of Apache Ant. The results of the empirical study are reported and analyzed in this section. The number of defects is then divided by the release’s KLOC that was measured with the JStyle metrics tool. The dependant variable PRDD of a release was measured by searching the Apache Ant’s bug database for a list of post-release defects. The TBR metric was simply measured between any two successive releases using their release dates. A prototype metrics tool that has been developed as part of this research was used to calculate the relationship-based stability metrics from each release to the next by comparing relationships between releases. Comment and blank lines were excluded in class comparison.
#Examdiff pro file size limitation pro
The ExamDiff Pro file comparison tool was used to calculate the class-based stability metrics from each release to the next by comparing classes between releases. The independent variables were measured as follows.
hypotheses are that there are significant correlations between measures of each of the 17 stability metrics from release i to release i+1 (following release) and the PRDD of release i+1. Unlike its fellow members, cmp and comm, it tells us which lines in one file have is to be changed to make the two files identical. Difference between cmp and diff commands with exampleĭiff stands for difference.This command is used to display the differences in the files by comparing the files line by line.
#Examdiff pro file size limitation full
The benefits of this script is that it allow user full control of their file diffs. This is especially useful with csv/tsv/psv files: compare products file for changes to import compare log files compare database export of large organization employee/users NOTE. line by line diff of two large (sorted) text files. If they are not, sort them first.įor the updated data in order to perform a line by line comparison you can use awk and not grep : awk 'NR=FNR # ifs="$IFS" IFS=$' ' set -f ix=0 left=0 right=1 while IFS= read -r line do pair="$line" ((ix++)) if ((ix%2=0)) then # Change \x20 to \x02 to simplify parsing diff's output, #+ then change \x02 back to \x20 for the final output. comm -2 -3 file1 file2 > file3 The input files must be sorted.
NAME comm - compare two sorted files line by line SYNOPSIS comm FILE1 FILE2 -1 suppress lines unique to FILE1 -2 suppress lines unique to FILE2 -3 suppress lines that appear in both files So. file_1_text, file_2_text, fromfile='file1.txt', tofile='file2.txt', lineterm=''): print(line) chevron_right. With open('file1.txt') as file_1: file_1_text = file_1.readlines () with open('file2.txt') as file_2: file_2_text = file_2.readlines () for line in difflib.unified_diff (.
0 kommentar(er)
