Introduction to Data Mining Technique Helps To Reduce Test Suite Seminar Topic:

This paper discussed about data mining technique which helps to reduce test suite and thereby facilitating the knowledge extraction from test cases. Both functional and non-functional requirements from SRS document transformed into state charts. These state charts will helpful to generate different test cases automatically for a single planning problem. Test cases generated from state charts are used for manual software testing or automated testing at system level.


There is a need to have an effective test automation methods or tools to address many issues in testing like Test ware creation and its inconsistent maintenance. Data mining is a powerful technique which helps to create patterns from an existing database. Data mining techniques helps to manage the project by maintaining project related valuable data and produce higher quality software systems. The three steps involved in applying data mining technique to the current project is generation of classification rules, create the UML state machine, then generate test cases from it and applying data mining techniques to the generated test cases to reduce the test suite size.

The WEKA classifier system generates classification rules and those rules will get apply to both functional and non-functional requirements. State machines which can specify the current state\behavior of the system and these states machines can be created based on non-functional requirements.

  In a software testing world, state models are used to derive test cases from state machines. The main target of model-based testing is to automate the generation of test cases process from test models. Predicate selection, Predicate transformation and Test data generation are the three steps involved in generating the test cases from state machines.


The process of covering patterns from data is termed as Data mining. Verification and validation of patterns on data samples is one of the important part in Data mining process. For mining different kinds of data, there are many methods available including clustering, characterization etc. 

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