A similarity metric for the inputs of OO programs and its application in adaptive random testing

Chen, Jinfu; Kuo, Fei-Ching; Chen, Tsong Yueh; Towey, Dave; Su, Chenfei; Huang, Rubing

Abstract

Random testing (RT) has been identified as one of the most popular testing techniques, due to its simplicity and ease of automation. Adaptive random testing (ART) has been proposed as an enhancement to RT, improving its fault-detection effectiveness by evenly spreading random test inputs across the input domain. To achieve the even spreading, ART makes use of distance measurements between consecutive inputs. However, due to the nature of object-oriented software (OOS), its distance measurement can be particularly challenging: Each input may involve multiple classes, and interaction of objects through method invocations. Two previous studies have reported on how to test OOS at a single-class level using ART. In this study, we propose a new similarity metric to enable multiclass level testing using ART. When generating test inputs (for multiple classes, a series of objects, and a sequence of method invocations), we use the similarity metric to calculate the distance between two series of objects, and between two sequences of method invocations. We integrate this metric with ART and apply it to a set of open-source OO programs, with the empirical results showing that our approach outperforms other RT and ART approaches in OOS testing.

Citation

Chen, J., Kuo, F.-C., Chen, T. Y., Towey, D., Su, C., & Huang, R. (2017). A similarity metric for the inputs of OO programs and its application in adaptive random testing. IEEE Transactions on Reliability, 66(2), 373-402. https://doi.org/10.1109/tr.2016.2628759

• Journal Article Type: Article
• Acceptance Date: Nov 8, 2016
• Online Publication Date: Dec 19, 2016
• Publication Date: Jun 30, 2017
• Deposit Date: Nov 3, 2017
• Journal: IEEE Transactions on Reliability
• Print ISSN: 0018-9529
• Electronic ISSN: 1558-1721
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 66
• Issue: 2
• Pages: 373-402
• DOI: https://doi.org/10.1109/tr.2016.2628759
• Keywords: Adaptive random testing (ART); Method invocation; Object distance; Object-oriented software (OOS) testing; Test input distance
• Public URL: https://nottingham-repository.worktribe.com/output/870173
• Publisher URL: https://doi.org/10.1109/tr.2016.2628759
• Additional Information: c2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
• Contract Date: Nov 1, 2017