Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Improving MC/DC and Fault Detection Strength Using Combinatorial Testing

Published

Author(s)

D. Richard Kuhn, Raghu N. Kacker

Abstract

Software, in many different fields and tasks, has played a critical role and even replaced humans to improve efficiency and safety. However, catastrophic consequences can be caused by implementation bugs and design defects. MC/DC (Modified Condition Decision Coverage), required by the Federal Aviation Administration on Level A (the most safety critical system), has been shown to be effective in detecting software bugs. However, generating tests to achieve high MC/DC can be very expensive and time consuming. Recently, many studies showed that combinatorial testing (CT) could generate high-quality test cases in a cost-effective way. Can CT generate test cases for high MC/DC? In this paper, we conduct an empirical study on two real-life programs to evaluate the efficiency and effectiveness of using combinatorial testing to improve MC/DC coverage achievement, as well as the fault detection strength.
Proceedings Title
Proceedings of IEEE International Conference on Quality, Reliability, and Security
Conference Dates
July 25-29, 2017
Conference Location
Prague, CZ
Conference Title
IEEE International Conference on Quality, Reliability, and Security

Keywords

Combinatorial Testing, MC/DC Coverage, Fault Detection

Citation

Kuhn, D. and Kacker, R. (2019), Improving MC/DC and Fault Detection Strength Using Combinatorial Testing, Proceedings of IEEE International Conference on Quality, Reliability, and Security, Prague, CZ, [online], https://doi.org/10.1109/QRS-C.2017.131, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=923729 (Accessed December 11, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created July 24, 2019, Updated October 12, 2021