How are the defects classified?

All defects are not the same type. Some defects may result in system crash. Some defects may be very minor. So, the defects need to be classified based on its impact on the functionality of the software. There are various ways in which we can classify.

Severity Wise

Major: A defect, which will cause an observable product failure or departure from requirements.
Minor: A defect that will not cause a failure in execution of the product.
Fatal: A defect that will cause the system to crash or close abruptly or effect other applications.

While recording the defects in a defect log sheet, the severity of the defects also needs to be noted. IF a critical defect is present in the code, then the software is not ready for delivery. Only a few major defects are allowed. Similarly, a threshold needs to be kept on the number of minor defects. As the threshold depends on the application, the project manager/test team member has to use the judgement in fixing the thresholds on major and minor defects. After analyzing the test report, the project manager needs to decide whether the software is ready for delivery or not.

Stages and Goals of Data Mining

Data Mining is an analytic process designed to explore data (usually large amounts of data - typically business or market related) in search of consistent patterns and/or systematic relationships between variables, and then to validate the findings by applying the detected patterns to new subsets of data. The process of data mining consists of three stages:
1. Initial exploration :
This stage usually starts with data preparation which may involve cleaning data, data transformations, selecting subsets of records and - in case of data sets with large numbers of variables ("fields") - performing some preliminary feature selection operations to bring the number of variables to a manageable range. Then, depending on the nature of the analytic problem, this first stage of the process of data mining may involve anywhere between a simple choice of straightforward predictors for a regression model, to elaborate exploratory analyzes using a wide variety of graphical and statistical methods.
2. Model building and validation :
This stage involves considering various models and choosing the best one based on their predictive performance. There are a variety of techniques developed to achieve that goal - many of which are based on so-called "competitive evaluation of models," that is, applying different models to the same data set and then comparing their performance to choose the best. These techniques - which are often considered the core of predictive data mining - include: Bagging (Voting, Averaging), Boosting, Stacking (Stacked Generalizations), and Meta-Learning.
3. Deployment :
The final stage involves using the model selected as best in the previous stage and applying it to new data in order to generate predictions or estimates of the expected outcome.

GOALS OF DATA MINING :
- Prediction : Data mining can show how certain attributes within the data will behave in the future. In such applications, business logic is used coupled with data mining.
- Identification : Data patterns can be used to identify the existence of an item, an event, or an activity. For example, intruders trying to break a system may be identified by the programs executed, files accessed, and CPU time per session.
- Classification : Data mining can partition the data so that different classes or categories can be identified based on combination of parameters.
- Optimization : One eventual goal of data mining may be to optimize the use of limited resources such as time, space, money, or materials and to maximize output variables such as sales or profits under a given set of constraints.