2 research outputs found
A Systematic Review of the Application and Empirical Investigation of Search-Based Test Case Generation
Otsingupõhine tarkvara testimine kasutab metaheuristilisi algoritme, et automatiseerida testide genereerimist. Selle töö eesmärgiks on osaliselt taasluua 2010. aastal kirjutatud Ali et al. artikkel, et uurida, kuidas on aastatel 2008-2015 kasutatud metaheuristilisi algoritme testide loomiseks. See töö analüüsib, kuidas on antud artiklid koostatud ning kuidas neis on algoritmide maksumust ja efektiivsust hinnatud. Kogutud tulemusi võrreldakse Ali et al. tulemustega.Search based software testing uses metaheuristic algorithms to automate the generation of test cases. This thesis partially replicates a literature study published in 2010 by Ali et al. to determine how studies published in 2008-2015 use metaheuristic algorithms to automate the generation of test cases. The thesis analyses how these studies were conducted and how the cost-effectiveness is assessed in these papers. The trends detected in the new publications are compared to those presented in Ali et al
Instance Space Analysis of Search-Based Software Testing
Search-based software testing (SBST) is now a mature area, with numerous
techniques developed to tackle the challenging task of software testing. SBST
techniques have shown promising results and have been successfully applied in
the industry to automatically generate test cases for large and complex
software systems. Their effectiveness, however, is problem-dependent. In this
paper, we revisit the problem of objective performance evaluation of SBST
techniques considering recent methodological advances -- in the form of
Instance Space Analysis (ISA) -- enabling the strengths and weaknesses of SBST
techniques to be visualized and assessed across the broadest possible space of
problem instances (software classes) from common benchmark datasets. We
identify features of SBST problems that explain why a particular instance is
hard for an SBST technique, reveal areas of hard and easy problems in the
instance space of existing benchmark datasets, and identify the strengths and
weaknesses of state-of-the-art SBST techniques. In addition, we examine the
diversity and quality of common benchmark datasets used in experimental
evaluations