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. First, the analyst should determine that there is indeed attribute data. One can assume that the assignment of a code – that is, the division of a code into a category – is a decision that characterizes the error with an attribute. Either a category is correctly assigned to an error, or it is not. Similarly, the appropriate source location is either attributed to the defect or not. These are “yes” or “no” and “correct allocation” or “wrong allocation” answers. This part is pretty simple. The reasons why the agreements (consistencys) were weak might be: in addition to the question of sample size, logistics can ensure that listeners do not remember the initial attribute they attributed to a scenario when they see it for the second time, but also a challenge. Of course, this can be avoided a bit by increasing the sample size and, better yet, waiting a while before giving the scenarios to the evaluators a second time (perhaps one to two weeks). Randomization of transitions from one audit to another can also be helpful. In addition, evaluators tend to work differently when they know they are being examined, so that the fact that they know it is a test also distorts the results.
Hiding this in one way or another can help, but it`s almost impossible to achieve, despite the fact that it borders on the inthesis. And in addition to being at best marginally effective, these solutions increase an already demanding study with complexity and time. Like any measurement system, the accuracy and accuracy of the database must be understood before the information is used (or at least during use) to make decisions. At first glance, it appears that the apparent starting point begins with an analysis of the attribute (or attribute-Gage-R-R). That may not be a very good idea. Unlike a continuous measurement value, which cannot be accurate (on average), any lack of precision in an attribute measurement system inevitably leads to accuracy problems. If the error coder is not clear or undecided on how to encode a defect, different codes are assigned to several defects of the same type, making the database imprecise. In fact, the vagueness of an attribute measurement system is an important factor in inaccuracies. An attribute analysis was developed to simultaneously assess the effects of repeatability and reproducibility on accuracy.
It allows the analyst to review the responses of several reviewers if they look at multiple scenarios multiple times. It establishes statistics that assess the ability of evaluators to agree with themselves (repeatability), with each other (reproducibility) and with a master or correct value (overall accuracy) known for each characteristic – over and over again. Repeatability and reproducibility are components of accuracy in an analysis of the attribute measurement system, and it is advisable to first determine if there is a precision problem. This means that before designing an attribute contract analysis and selecting the appropriate scenarios, an analyst should urgently consider monitoring the database to determine if past events have been properly coded.