Mining Test Oracles for Test Inputs Generated from Java Bytecode
Weifeng Xu, Tao Ding, Hanlin Wang, Dianxiang Xu · 2013
Search-based test generation can automatically produce a large volume of test inputs. However, it is difficult to define the test oracle for each of the test inputs. This paper presents a mining approach to building a decision tree model according to the test inputs generated from Java bytecode. It converts Java bytecode into the Jimple representation, extracts predicates from the control flow graph of the Jimple code, and uses these predicates as attributes for organizing training data to build a decision tree. Our case studies show that the mining approach generated accurate behavioral models and that test oracles derived from these models were able to kill 94.67% of the mutants with injected faults.