Paulo Marcos Siqueira Bueno

AUTOMATIC TEST DATA GENERATION FOR PROGRAM PATHS USING GENETIC ALGORITHMS

A new technique and tool are presented for test data generation for path testing. They are based on the dynamic technique and on a Genetic Algorithm, which evolves a population of input data towards reaching and solving the predicates along the program paths. We improve the performance of test data generation by using past input data to compose the initial population for the search. An experiment was done to assess the performance of the techniques compared to that of random data generation.

Diversity oriented test data generation using metaheuristic search techniques

We present a new test data generation technique which uses the concept of diversity of test sets as a basis for the diversity oriented test data generation - DOTG. Using DOTG we translate into an automatic test data generation technique the intuitive belief that increasing the variety, or diversity, of the test data used to test a program can lead to an improvement on the completeness, or quality, of the testing performed. We define the input domain perspective for diversity (DOTG-ID), which considers the distances among the test data in the program input domain to compute a diversity value for test sets. We describe metaheuristics which can be used to automate the generation of test sets for the DOTG-ID testing technique: simulated annealing; a genetic algorithm; and a proposed metaheuristic named simulated repulsion. The effectiveness of DOTG-ID was evaluated by using a Monte Carlo simulation, and also by applying the technique to test simple programs and measuring the data-flow coverage and mutation scores achieved. The standard random testing technique was used as a baseline for these evaluations. Results provide an understanding of the potential gains in terms of testing effectiveness of DOTG-ID over random testing and also reveal testing factors which can make DOTG-ID less effective.

Improving random test sets using the diversity oriented test data generation

We present a measure that characterizes the diversity of a test set from the perspective of the input domain of the program under test. By using a metaheuristic algorithm, randomly generated test sets (RTS) are evolved towards Diversity Oriented Test Sets (DOTS), which thoroughly cover the input domain. DOTS are evaluated using a Monte Carlo simulation to assess how testing factors influence their effectiveness and also by the values of data flow coverage and mutation scores attained on simple programs. Results provide understanding on possible gains of using DOTS and on circumstances where RTS can be more effective.

Automatic test data generation using particle systems

The simulated repulsion algorithm, which is based on particle systems, is used for the automatic generation of diversity oriented test sets (DOTS). These test sets are generated by taking randomly generated test sets and iteratively improving their diversity (the level of variability among values for the test data) towards DOTS. The results of a simulation performed to evaluate characteristics of DOTS indicate improvement, with respect to fault detection, of these test sets over the standard random test sets.