Using Data Mining Technique to Reduce Test Suite

Topics: Data mining, State diagram, Unified Modeling Language Pages: 8 (2616 words) Published: September 6, 2011
computer science
Seema Phogat, Research Scholar
Dr. Sumeet Gill, Research Supervisor
Singhania University

Using Data mining technique to reduce test suite
Writing requirements is a two-way process. In this paper we use to classify Functional Requirements (FR) and Non Functional Requirements (NFR) statements from Software Requirements Specification (SRS) documents. This is systematically transformed into state charts considering all relevant information. The current paper outlines how test cases can be automatically generated from these state charts. The application of the states yields the different test cases as solutions to a planning problem. The test cases can be used for automated or manual software testing on system level. And also the paper presents a method for reduction of test suite by using mining methods thereby facilitating the mining and knowledge extraction from test cases. Keywords: SRS, FR, NFR, State model, Test case, Test suite, Minining

The systematic production of high-quality software, which meets its specification, is still a major problem. Although formal specification methods have been around for a long time, only a few safety-critical domains justify the enormous effort of their application. The state of the practice, which relies on testing to force the quality into the product at the end of the development process, is also unsatisfactory. The need for effective test automation adds to this problem, because the creation and maintenance of the test ware is a source of inconsistency itself and is becoming a task of comparable complexity as the construction of the code. Data mining algorithms can be applied at different levels of abstraction and help the user discover more meaningful patterns. Data mining will create patterns from the existing database. Using well-established data mining techniques, practitioners and researchers can explore the potential of this valuable data in order to manage their project and to produce higher quality software systems that are delivered on time and within budget our approach is as follows i. Generation of classification rules.

ii. Generate test cases from the UML state machine.
iii .Finally data mining techniques are applied on the generated test cases in order to further reduce the test suite size. 2. Generation of Classification Rules
In the current paper, we provide the Software Requirements Specification to the classifier system. For classifying we use Weka. The Weka classifier is initially trained with a training set. Later it is provided with the SRS. It classifies SRS in to functional and non functional requirements by generating a classify cation rules. The classification rules are applied to the SRS to get FR and NFR. From NFR we derive the state machine. State machines specify the behaviour of a system/subsystem. 3. Generation of Test Cases

This section briefly describes a transformation from State diagrams in to Test cases. State machines and state diagrams have a long history in computer science. Recent versions of UML include an expressive state diagrams concept. Especially the abstraction mechanisms in the UML state machine formalism, i.e. nesting of states and stubs, allow us to map all the important elements of our use case documents to State machines. From State Machines to Test Cases Using state models to derive test cases has been common practice in the software testing world for some time .The final goal of model-based testing is to automate the test case generation from test models as much as possible. Our approach generates a set of valid test sequences, where the preconditions of all transitions are established either by previous actions or by properties of the test data. The scope of our method is the generation of test sequences supplemented by...

References: 10. Vasilache S., and Tanaka J., "Synthesis of State Machines from Multiple Interrelated Scenarios Using Dependency Diagrams," Journal of Systemic, Cybernetics and Informatics, Vol.3, No.3, 2006.
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