An empirical study of real-world variability bugs detected by variability-oblivious tools

Author:

Mordahl Austin1,Oh Jeho2,Koc Ugur3,Wei Shiyi1,Gazzillo Paul4

Affiliation:

1. University of Texas at Dallas, USA

2. University of Texas at Austin, USA

3. University of Maryland, USA

4. University of Central Florida, USA

Funder

NSF

Publisher

ACM

Cited by 14 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Evolutionary Feature Dependencies: Analyzing Feature Co-Changes in C Systems;2023 IEEE 23rd International Working Conference on Source Code Analysis and Manipulation (SCAM);2023-10-02

2. Automatic Testing and Benchmarking for Configurable Static Analysis Tools;Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis;2023-07-12

3. Detecting feature influences to quality attributes in large and partially measured spaces using smart sampling and dynamic learning;Knowledge-Based Systems;2023-06

4. Automatically Detecting Variability Bugs Through Hybrid Control and Data Flow Analysis;2023 IEEE Security and Privacy Workshops (SPW);2023-05

5. Bringing Together Configuration Research: Towards a Common Ground;Proceedings of the 2022 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software;2022-11-29

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