| Quality Metrics - Ryan Cook | |||
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Definition of quality
Before we can measure quality, we need to understand and define what quality is exactly. Quality is commonly interpreted as something that can be judged but not measured and as indefinable because quality means many different things to many different people. Such a vague understanding of the concept quality obviously could not be helpful to professionals attempting to quantify it. Therefore, the workable definition of quality was created as the conformance of a product to the customers’ requirements. Measurement theory Scientific advancement is made through the observation of data resulting in new theories and affirmation or refutation of these theories based upon new data, thus making measurement of that data crucial to scientific progress. The first component of a measurement is it scale, the four levels of scales are nominal scale, ordinal scale, interval scale, and ratio scale. Nominal scale classifies or categorizes the attribute being measured. An example of this would be the classifications male and female. The nominal scale doesn’t allow for comparisons to be made or mathematical operations to be preformed. So the expression male > female or male < female are invalid. As well, it is impossible to subtract a female from a male. The key requirement of the nominal scale is that each data item can fit into only one category. Ordinal scale is vary similar to the nominal scale except categories can be ordered and comparisons can be made. An example of this would be customer satisfaction surveys which often require an answer of completely dissatisfied, somewhat dissatisfied, neutral, somewhat satisfied, or completely satisfied. In such a case, there is a natural order to the scale unlike nominal scales. However like nominal scales mathematical operations still can’t be performed. Interval scale indicates exact differences between measurement points unlike the previous two scales. Only the mathematical operations add and subtract can be applied to an interval scale because the zero point on the scale has been arbitrarily decided upon. For example the temperature 20 degrees is hotter than 10 degrees but 20 degrees may not be twice as hot as 10 degrees. The key requirement of the interval scale is that units be clearly defined. Ratio scale is the highest level of measurement much the same as interval scales except zero is a non-arbitrary point allowing mathematical operations such as division. Examples of ratio scale measurements are weight and length. The next important component is the quality of measurements taken. The two facets of measurement quality are reliability and validity. First reliability, which simply means how repeatable the results are and low reliability indicates that many random errors are occurring during measurement. Second, the concept of validity, which means the metric measures what it intended to measure. Errors in validity, called systematic errors, are usually experienced because not all factors were taken into account when the metric was designed. The next component of measurement is causality. The cause and effect relationship has three requirements:
CASE 1 Z causes changes in X Z causes changes in Y X is perceived to causes changes in Y or vice versa and Z is overlooked CASE 2 X causes changes in Z Z causes changes in Y X is perceived to causes changes in Y and Z is overlooked Many metrics Most metrics in software quality assurance fall under one of two categories, product metrics and process metrics. Product metrics are for describing characteristics of product such as it’s size, complexity, features, and performance. Several common product metrics are mean time to failure, defect density, customer problem, and customer satisfaction metrics.
Process metrics are strictly for evaluating & improving the effectiveness of development and maintenance processes. Some common process metrics are defect arrival pattern, backlog management index, fix quality, and fix response time metrics.
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