Name
Abstract | ||
|---|---|---|
This paper describes a method for extracting functional requirements from tests, where tests take the form of vectors of inputs (supplied to the system) and outputs (produced by the system in response to inputs). The approach uses data-mining techniques to infer invariants from the test data, and an automated-verification technology to determine which of these proposed invariants are indeed invariant and may thus be seen as requirements. Experimental results from a pilot study involving an automotive-electronics application show that using tests that fully cover the structure of the software yield more complete invariants than structurally-agnostic black-box tests. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-16612-9_1 | RV |
Keywords | Field | DocType |
pilot study,automotive-electronics application show,software yield,test data,automatic requirement extraction,proposed invariants,complete invariants,automated-verification technology,functional requirement,structurally-agnostic black-box test,test case,black box testing,data mining | Test suite,Data mining,Functional requirement,Simulation,Computer science,Theoretical computer science,Association rule learning,Software,Test case,Invariant (mathematics),Test data | Conference |
Volume | ISSN | ISBN |
6418 | 0302-9743 | 3-642-16611-3 |
Citations | PageRank | References |
12 | 0.57 | 17 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chris Ackermann | 1 | 107 | 4.60 |
| Rance Cleaveland | 2 | 2266 | 254.39 |
| Samuel Huang | 3 | 42 | 3.93 |
| Arnab Ray | 4 | 69 | 7.60 |
| Charles Shelton | 5 | 31 | 4.78 |
| elizabeth latronico | 6 | 52 | 4.09 |