Evolutionary Test Case Generation via Many Objective Optimization and Stochastic Grammars

Kifetew, Fitsum Meshesha (2015) Evolutionary Test Case Generation via Many Objective Optimization and Stochastic Grammars. PhD thesis, University of Trento.

[img]PDF - Doctoral Thesis
Restricted to Repository staff only until 9999.
Available under License Creative Commons Attribution Non-commercial No Derivatives.

849Kb

Abstract

In search based test case generation, most of the research works focus on the single-objective formulation of the test case generation problem. However, there are a wide variety of multi- and many-objective optimization strategies that could offer advantages currently not investigated when addressing the problem of test case generation. Furthermore, existing techniques and available tools mainly handle test generation for programs with primitive inputs, such as numeric or string input. The techniques and tools applicable to such types of programs often do not effectively scale up to large sizes and complex inputs. In this thesis work, at the unit level, branch coverage is reformulated as a many-objective optimization problem, as opposed to the state of the art single-objective formulation, and a novel algorithm is proposed for the generation of branch adequate test cases. At the system level, this thesis proposes a test generation approach that combines stochastic grammars with genetic programming for the generation of branch adequate test cases. Furthermore, the combination of stochastic grammars and genetic programming is also investigated in the context of field failure reproduction for programs with highly structured input.

Item Type:Doctoral Thesis (PhD)
Doctoral School:Information and Communication Technology
PhD Cycle:27
Subjects:Area 01 - Scienze matematiche e informatiche > INF/01 INFORMATICA
Funders:Fondazione Bruno Kessler
Repository Staff approval on:30 Nov 2015 11:44

Repository Staff Only: item control page