Name
Abstract | ||
|---|---|---|
Software testing is an important discipline, and consumes significant amount of effort. A proper strategy is required to design and generate test cases systematically and effectively. In this paper automated software test case generation with Radial Basis Function Neural Network (RBFNN) has been proposed and empirically validated with the help of a case study and compared with other techniques of soft computing. Experimental results show that RBFNN is one of the best technique for automated test case generation. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1145/2020976.2020992 | ACM SIGSOFT Software Engineering Notes |
Keywords | Field | DocType |
best technique,paper automated software test,radial basis function neural,consumes significant amount,test cases systematically,software testing,automated test case generation,case study,case generation,test oracle,feed forward,soft computing,neural network,backpropagation,artificial neural network | Data mining,Radial basis function network,Radial basis function neural,Computer science,Oracle,Time delay neural network,Software,Test case,Artificial intelligence,Soft computing,Artificial neural network,Machine learning | Journal |
Volume | Issue | Citations |
36 | 5 | 7 |
PageRank | References | Authors |
0.44 | 13 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Om Prakash Sangwan | 1 | 58 | 4.69 |
| Pradeep Kumar Bhatia | 2 | 72 | 6.00 |
| Yogesh Singh | 3 | 267 | 13.87 |
| BhatiaPradeep Kumar | 4 | 15 | 1.18 |