At this stage, the evaluation of the attribute agreement should be applied and the detailed results of the review should provide a good set of information in order to understand how best to organize the evaluation. The accuracy of a measurement system is analyzed by subdividing it into two essential components: repeatability (the ability of a particular evaluator to assign the same value or attribute several times under the same conditions) and reproducibility (the ability of several evaluators to agree among themselves for a number of circumstances). In the case of an attribute measurement system, repeatability or reproducibility problems inevitably cause accuracy problems. In addition, if one knows the overall accuracy, repeatability and reproducibility, distortions can be detected even in situations where decisions are systematically wrong. 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. 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. Unlike a continuous measuring device that can be accurate (on average) but not accurate, any lack of accuracy in an attribute measurement system necessarily leads to accuracy problems. If the error encoder is unclear or undecided about how to code an error, multiple errors of the same type are assigned to different codes, making the database inaccurate. In fact, for an attribute measurement system, imprecision is an important contribution to imprecision. 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). The audit should help to identify the specific people and codes that are the main sources of problems and the evaluation of the attribute agreement should help to determine the relative contribution of reproducibility and reproducibility problems for those specific codes (and individuals). In addition, many bug databases have problems with precision records that indicate where an error was created because the place where the error is detected is recorded and not where the error was created. When the error is detected, there is not much to identify the causes, therefore the accuracy of the site assignment should also be an element of the audit. 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 achieve 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. . .
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