Introduction to Data Mining Techniques to Automate Software Testing Project:
The data mining models in tested software can be utilized for recovering missing and incomplete specifications, designing a minimal set of regression tests, and evaluating the correctness of software outputs when testing The study of feasibility of the proposed approach a novel data mining algorithm called Info-Fuzzy Network (IFN) to execution data of a general-purpose code for solving partial differential equations.
Data mining models of software testing can be utilized for recovering incomplete specification, desinig a regression test and evaluating the software outputs when testing new, potentially flawed releases of the system.
A successful test of software should make a problem while testing software. The tests that do not gives any faults are useless. While testing a large system, the test of the entire application (system testing) is usually preceded by the stages of unit testing and integration testing.
Different task involved in Data mining are been divided into four types:
The Association rule in learning database, Searches for relationships between variables.
Clustering in Database, Task of discovering groups and structures in the data.
Classification, Hear the information are scanned and distinguished into predefined classes
Regression helps to find a function which helps in modeling a data in database with the least amount of error and also identify any of the visible patterns and trends in the database.
This paper also discuss about some of the topics like, Uses of Data mining in various field of computer science. Info-Fuzzy Network Structure, Info-fuzzy network has an tree-like structure, where the same input attribute is used across all nodes of a given layer (level).input-output analyses with info-Fuzzy Networks, The IFN algorithm is trained on inputs provided by RTG and outputs obtained from a legacy system by means of the Test Bed module. As indicated above, a separate IFN model is built for each output variable
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