scholarly journals Enhancing path-oriented test data generation using adaptive random testing techniques

Author(s):  
Esmaeel Nikravan ◽  
Farid Feyzi ◽  
Saeed Parsa
Author(s):  
Duc-Anh Nguyen ◽  
Tran Nguyen Huong ◽  
Hieu Vo Dinh ◽  
Pham Ngoc Hung

This paper improves the breadth-first search strategy in directed automated random testing (DART) to generate a fewer number of test data while gaining higher branch coverage, namely Static DART or SDART for short. In addition, the paper extends the test data compilation mechanism in DART, which currently only supports the projects written in C, to generate test data for C++ projects. The main idea of SDART is when it is less likely to increase code coverage with the current path selection strategies, the static test data generation will be applied with the expectation that more branches are covered earlier. Furthermore, in order to extend the test data compilation of DART for C++ context, the paper suggests a general test driver technique for C++ which supports various types of parameters including basic types, arrays, pointers, and derived types. Currently, an experimental tool has been implemented based on the proposal in order to demonstrate its efficacy in practice. The results have shown that SDART achieves higher branch coverage with a fewer number of test data in comparison with that of DART in practice.


1970 ◽  
Vol 2 (1) ◽  
Author(s):  
S. A. Hameed, A. M. A. Al-Abbasi

The actual test data generation is one of the difficult and expensive parts of applying software-testing techniques. Many of the current test data generators suffer from the reduction of user’s confidence in generated test data and testing process. This is because of focusing on developer and database administrator viewpoints regardless of users concerns and focusing on data type and structure regardless of meaning. This paper proposes a model of an intelligent generator for semi-actual test data with the aim of increasing users confidence in software testing. The model uses samples of real data as a resource data and a set of efficient generation techniques based on statistical methods such as permutations, combination, sampling, and statistical distributions. The selection of the suitable structure and generation technique is based on one of the intelligent soft computing techniques such as fuzzy logic, neural network, heuristic, or genetic algorithm. The generated test data is validated according to the data specifications then tested by one of the normality testing techniques to be close to the real world or environment of the testing processes. This model offers the ability of simulating real environments.Key Words: Software Testing, Test Data Generation, Semi-Actual Data, Intelligent Generator, Simulation.


2009 ◽  
Vol 29 (6) ◽  
pp. 1722-1724
Author(s):  
Xiao-cheng HUANG ◽  
Xi-wu WANG ◽  
Dong-sheng CHANG ◽  
Gang HE

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