Investigation and Automation of Cost Reduction Techniques for Mutation Testing

• Grant number: 16/21251-0
• Support Opportunities: Scholarships abroad - Research
• Effective date (Start): October 01, 2017
• Effective date (End): September 30, 2018
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: Fabiano Cutigi Ferrari
• Grantee: Fabiano Cutigi Ferrari
• Host Investigator: Andrew Jefferson Offutt
• Host Institution: Centro de Ciências Exatas e de Tecnologia (CCET). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil
• Research place: George Mason University, United States

Abstract

Context: The Mutant Analysis test selection criterion, also known as mutation testing, is an important option for the evaluation of both software systems and test sets that are used to validate them. However, mutation testing imposes high application cost. Due to this, cost reduction techniques are on focus, aiming at reducing significantly the cost of this criterion, without compromising the quality of derived test sets.Problem Definition: Results yielded by such cost reduction techniques are valid only for restricted groups of programs, as highlighted in related research. The question that persists regards the usefulness of such results to be applied to untested programs.Goal: The general aim of this proposal is the definition of a strategy for the application of cost reduction techniques for mutation testing that reuses empirical knowledge gathered from previous studies.Method: Initially, we will characterise the state-of-the-art with respect to cost reduction techniques for mutation testing. We will rank the best techniques in order to define a strategy for their application. The intended strategy will tank into account the similarity between tested programs and programs to which cost reduction techniques will be applied. Results will be assessed with respect to the quality of the newly created tests.

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

PIZZOLETO, ALESSANDRO VIOLA; FERRARI, FABIANO CUTIGI; OFFUTT, JEFF; FERNANDES, LEO; RIBEIRO, MARCIO. A systematic literature review of techniques and metrics to reduce the cost of mutation testing. JOURNAL OF SYSTEMS AND SOFTWARE, v. 157, NOV 2019. (16/21251-0)

PINHEIRO, PEDRO; VIANA, JOSE CARLOS; RIBEIRO, MARCIO; FERNANDES, LEO; FERRARI, FABIANO; GHEYI, ROHIT; FONSECA, BALDOINO. Mutating code annotations: An empirical evaluation on Java and C# programs. SCIENCE OF COMPUTER PROGRAMMING, v. 191, JUN 1 2020. (16/21251-0)