Premium
Employing accumulated knowledge to refine test descriptions
Author(s) -
Wild Christian,
Zeil Steven,
Feng Gao,
Chen Ji
Publication year - 1992
Publication title -
software testing, verification and reliability
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.216
H-Index - 49
eISSN - 1099-1689
pISSN - 0960-0833
DOI - 10.1002/stvr.4370020203
Subject(s) - computer science , knowledge base , domain knowledge , test (biology) , domain (mathematical analysis) , test case , object (grammar) , data mining , semantics (computer science) , class (philosophy) , test data , artificial intelligence , programming language , machine learning , mathematics , regression analysis , mathematical analysis , paleontology , biology
Most testing methods generate test descriptions which define the desired characteristics of the input data in a test case. This paper describes the use of accumulated knowledge about a problem domain to refine these test descriptions, with the goal of increasing the probability that the input data generated from the refined test descriptions will reveal faults in a software system. A knowledge base is introduced to hold information about object semantics and object class/subclass relationships. Knowledge accumulates with experience in a particular domain and can be focused on those objects and relationships in that domain which experience has shown to be error‐prone. This paper also defines a knowledge‐driven functional testing (KDFT) method which derives test descriptions from a formal specification and refines these descriptions using that knowledge base. A case study of the KDFT method using data from a previous study of the launch intercept control problem is described. These preliminary results indicate that knowledge‐based refinement of test descriptions can dramatically improve their ability to detect certain classes of faults.