Created by W.Langdon from gp-bibliography.bib Revision:1.7410
The main goal of this thesis is, therefore, to address different problems from the Software Testing field and devise ways of solving (or approximate a solution for) such problems using tools and results coming from the Information Theory and Artificial Intelligence fields. Specifically, this thesis addresses the Failed Error Propagation (FEP) problem, the test case generation problem, the Integration Testing of Software Product Lines (SPLs) problem, and the selection of hard-to-kill mutants for Mutation Testing problem. These four problems are addressed from different perspectives, looking for the best method to try to solve each of them.
This way, for the test case generation problem we propose both an evolutionary method based on a Grammar-Guided Genetic Programming Algorithm and an Information Theory-based measure (initially developed to choose between test cases) to guide such algorithm, with the goal of generating test cases with high fault finding capability. This is one of those cases where both fields join forces to obtain really good solutions. Additionally, we develop a Grammar Guided Genetic Programming Algorithm to generate test cases guided by coverage metrics, with the goal of increasing the coverability of the produced test cases.
For the Failed Error Propagation problem our work focuses on the use of Information Theory based measures to address it. Specifically, we focus on a previously proposed information theoretic measure called Squeeziness that measures the likelihood of FEP in a System Under Test (SUT), and we adapt it to work in a black-box scenario, in a non-deterministic one, and even to work with notions of entropy different from the original Shannon's entropy. Additionally, we develop a tool to automatically compute this last version. It is inside this tool where another case of these two fields helping each other can be found: we implement an Artificial Neural Network to automatically estimate the best notion of entropy to use for the given SUT.
In another line of work, our research to address the selection of hard-to-kill-mutants problem delves in the idea of using swarm intelligence to solve a complex problem. Specifically, with the goal of reducing the amount of useful mutants, we develop a swarm intelligence algorithm, inspired in the Particle Swarm Optimisation one, to decide which mutants are the harder-to-kill ones. Finally, in order to solve the Integration Testing of SPLs problem we use an Ant Colony Optimisation algorithm to select features either with a low testing cost or with a high probability of being requested. The goal is to simplify the testing processes through the reduction of the number of feature combinations needed to test an SPL.
The outcomes of all these proposals are relevant, improve the state-of-the-art and set new precedents for future work. Moreover, they open newlines of work for further development of the proposals and for improving the obtained solutions. Thus, this thesis makes its humble contribution to the aforementioned fields, for the enjoyment of whoever find it interesting.",
Supervisor: Manuel Nunez Garcia",
Genetic Programming entries for Alfredo Ibias