| Título: | Improving Software Reliability by Optimizing the Test Cases – A Novel approach |
| Autores: |
S Raju; Sri Venkateswara College of Engineering G V Uma; Professor |
| Fecha: | 2010-07-05 |
| Publicador: | International Journal of Computer Science Letters |
| Fuente: |
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| Tipo: | |
| Tema: |
Computer Science and Engineering Software Testing, Test Case, Control Flow Graph, Neural Network Software Engineering |
| Descripción: | Software testing is a significant activity in the software development life cycle. Software Testing ensures that the program meet its specification. It is a methodology for verifying the correctness of software system. But it is very expensive and time consuming. Effectiveness of the software testing is directly proportional to the effective generation of test cases (TC). An efficient technique is required to reduce the cost of testing. The proposed technique must include an optimization module to optimize the number of test cases generated. A novel idea proposed in this paper deals with the generation of TCs and considers an optimization technique using artificial neural network (ANN). The proposed work consists of the modules (1) to transform source code or software-under-test (SUT) into Control Flow Graph (CFG), (2)generates paths specification from flow graph representation, (3) generates TCs from path specifications, and (4) Test Case Optimizer (TCO) - construction of neural network model to train the test cases. The system generates test cases automatically and improves the efficiency of the software testing by reducing the redundant test cases. The core part of the system is to identify the redundant test cases that reveal same bugs or faults again and again. |
| Idioma: | Inglés |