First, the analyst should establish that there is indeed attribute data. It can be assumed that assigning a code – that is, classifying a code into a category – is a decision that characterizes the error by an attribute. Either a category is correctly assigned to a defect or it is not. Similarly, the defect is either attributed to the right source or not. These are “yes” or “no” and “good assignment” or “wrong assignment” answers. This part is quite simple. Attribute agreement analysis can be a great tool for detecting sources of inaccuracies in a bug tracking system, but it should be used with great care, consideration, and minimal complexity, if used at all. The best way to do this is to audit the database and then use the results of that audit to perform a focused and optimized analysis of repeatability and reproducibility. Since implementing an attribute analysis can be time-saving, expensive, and usually uncomfortable for all parties involved (the analysis is simple compared to execution), it`s best to take a moment to really understand what needs to be done and why. Once it is established that the bug measurement system is an attribute measurement system, the next step is to look at the notions of accuracy and precision in relation to the situation. First, it helps to understand that precision and precision are terms borrowed from the world of continuous measuring instruments (or variables).
For example, it is desirable for the tachometer in a car to read the right speed over a speed range (for example.B. 25 mph, 40 mph, 55 mph and 70 mph), no matter who reads it. The absence of distortions over a range of values over time can generally be described as precision (on average, the distortion can be considered erroneous). The ability of different people to interpret and tune the same value of Ness multiple times is referred to as accuracy (and accuracy problems may stem from a problem with Ness, not necessarily from the people who use it). An attribute agreement analysis allows the impact of repeatability and reproducibility on accuracy to be assessed simultaneously. It allows the analyst to study the responses of multiple auditors, while examining multiple scenarios. It compiles statistics that assess the ability of evaluators to agree with themselves (repeatability), with each other (reproducibility) and with a well-known control or accuracy value (overall precision) for each characteristic – again and again. In this example, a repeatability assessment is used to illustrate the idea, and it also applies to reproducibility. .