Second, the evaluation of the attribute agreement should be applied and the detailed results of the audit should provide a number of information that will help to understand how evaluation can be the best way to be organized. The reasons why the agreements (coherences) were weak might be: I`ll put all the results and default evaluation on Minitab and do the analysis of the attributes contract. Then I saw that the agreements were about 60% in “Within the Evaluators” and “Appraise vs. Standard”. Some Kappa values were less than 0.6. The result was pretty bad. The review should help determine which specific individuals and codes are the main causes of the problems, and the evaluation of the attribute agreement should help determine the relative contribution of repeatability and reproducibility issues to these specific codes (and individuals). In addition, many bug tracking systems have problems with precision readings that indicate where a defect has occurred, because the location where the defect is detected is recorded and not where the defect appeared. Where the error is found, it does not help much to identify the causes, which is why the accuracy of the site assignment should also be an element of the test. Once it is established that the bug tracking system is a system for measuring attributes, the next step is to examine the concepts of accuracy and accuracy that relate to the situation. First, it helps to understand that accuracy and precision are terms borrowed from the world of continuous (or variable) gags. For example, it is desirable that the speedometer in a car can carefully read the right speed over a range of speeds (z.B. 25 mph, 40 mph, 55 mph and 70 mph), regardless of the drive.
The absence of distortion over a range of values over time can generally be described as accuracy (Bias can be considered wrong on average). The ability of different people to interpret and reconcile the same value of salary multiple times is called accuracy (and accuracy problems may be due to a payment problem, not necessarily to the people who use it). Analytically, this technique is a wonderful idea. But in practice, the technique can be difficult to execute judiciously. First, there is always the question of sample size. For attribute data, relatively large samples are required to be able to calculate percentages with relatively low confidence intervals. If an expert looks at 50 different error scenarios – twice – and the match rate is 96 percent (48 votes vs. 50), the 95 percent confidence interval ranges from 86.29% to 99.51 percent. It is a fairly large margin of error, especially in terms of the challenge of choosing the scenarios, checking them in depth, making sure the value of the master is assigned, and then convincing the examiner to do the job – twice. If the number of scenarios is increased to 100, the 95 per cent confidence interval for a 96 per cent match rate will be reduced to a range of 90.1 to 98.9 per cent (Figure 2). If the test is planned and designed effectively, it can reveal enough information about the causes of the accuracy problems to justify a decision not to use attribute analysis at all. In cases where the trial does not provide sufficient information, the analysis of the attribute agreement allows for a more detailed review to inform the introduction of training changes and error correction in the measurement system.
Attribute analysis can be an excellent tool for detecting the causes of inaccuracies in a bug tracking system, but it must be used with great care, reflection and minimal complexity, should it ever be used.