A tool for data flow testing using evolutionary approaches (ETODF)

Abstract

Software testing is one of the most important phases of software development lifecycle. Software testing can be categorized into two major types; white box testing and black box testing. Data flow testing is a white box testing technique that uses both flow of control and flow of data through the program for testing. Evolutionary testing selects and generates test data by applying optimizing search techniques. This paper discusses the architecture and implementation of an automated tool for data flow testing by applying genetic algorithm for the automatic generation of test paths for data flow testing based on selected criteria for data flow testing. Our tool generates random initial population of test paths and then based on the selected data flow testing criteria new paths are generated by applying a genetic algorithm. A fitness function in tool evaluates each chromosome (path) based on the selected data flow testing criteria and computes its fitness. We have applied one point crossover and mutation operators for the generation of new paths based on fitness value. The proposed research tool called ETODF is continuation of our previous research work [6] on data flow testing using evolutionary approaches. The tool ETODF (evolutionary testing of data flow) has been implemented in Java. In experiments with this tool, our implemented tool has much better results as compared to random testing.