Automatic Test Data Generation Using Data Flow Information

Automatic Test Data Generation for Data Flow Testing Using a Genetic Algorithm

One of the major difficulties in software testing is the automatic generation of test data that satisfy a given adequacy criterion. This paper presents an automatic test data generation technique that uses a genetic algorithm (GA), which is guided by the data flow dependencies in the program, to search for test data to cover its def-use associations. The GA conducts its search by constructing n...

Towards Efficient Data-flow Test Data Generation Using KLEE

Dataflow coverage, one of the white-box testing criteria, focuses on the relations between variable definitions and their uses. Several empirical studies have proved data-flow testing is more effective than control-flow testing. However, data-flow testing still cannot find its adoption in practice, due to the lack of effective tool support. To this end, we propose a guided symbolic execution ap...

Towards Efficient Data-flow Test Data Generation

Data-flow testing (DFT) checks the correctness of variable definitions by observing their corresponding uses. It has been empirically proved to be more effective than control-flow testing in fault detection, however, its complexities still overwhelm the testers in practice. To tackle this problem, we introduce a hybrid testing framework: (1) The core of our framework is symbolic execution, enha...

Automatic Test Data Generation using Genetic Algorithm using Sequence Diagram

The most striking feature of SDLC is software testing. It is very labour-intensive and expensive process in software development and handling as well as maintenance of software. The main objective of this paper is to extend the testing technique. Testing is to show the incorrectness and is considered to succeed when an error is detected [Myers79]. Today’s automatic testing has replaced manual t...

The automatic generation of software test data using genetic algorithms