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). 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. 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. 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. If the number of scenarios is increased to 100, the 95 per cent confidence interval for a 96 per cent match rate will be reduced to a range of 90.1 to 98.9 per cent (Figure 2). 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. In this example, a repeatability assessment is used to illustrate the idea, and it also applies to reproducibility. The fact is that many samples are needed to detect differences in an analysis of the attribute, and if the number of samples is doubled from 50 to 100, the test does not become much more sensitive. Of course, the difference that needs to be identified depends on the situation and the level of risk that the analyst is willing to bear when making the decision, but the reality is that in