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. 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. However, a bug tracking system is not an ongoing payment. The assigned values are correct or not; There is no (or should not) grey area. If codes, locations and degrees of gravity are defined effectively, there is only one attribute for each of these categories for a particular error. Yes, for example.
B Repeatability is the main problem, evaluators are disoriented or undecided by certain criteria. When it comes to reproducibility, evaluators have strong opinions on certain conditions, but these opinions differ. If the problems are highlighted by several assessors, the problems are naturally systemic or procedural. If the problems only concern a few assessors, then the problems might simply require a little personal attention. In both cases, training or work aids could be tailored to either specific individuals or all evaluators, depending on the number of evaluators who were guilty of imprecise attribution of attributes. The accuracy of a measurement system is analyzed by segmenting into two main elements: 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 assessors to agree on a set of circumstances). In the case of an attribute measurement system, repeatability or reproducibility problems necessarily pose precision problems. In addition, if global accuracy, repeatability and reproducibility are known, distortions can also be detected in situations where decisions are always wrong.
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. The best way to do this is to first monitor the database and then use the results of that audit to perform a targeted and optimized analysis of repeatability and reproducibility. 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.