Attribute Agreement Analysis Process
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. 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. Despite these difficulties, performing an attribute analysis on bug tracking systems is not a waste of time. In fact, it is (or may be) an extremely informative, valuable and necessary exercise. The analysis of attributes should only be applied with caution and with a certain focus. 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. 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.