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Dynamic Unit State Data-Driven False Alarm Filtering for Regression Unit Testing
Youngseok Choi, Ahcheong Lee, Hyoju Nam, Insub Lee, Namhoom Jung, Kyutae Cho, Moonzoo Kim
http://doi.org/10.5626/JOK.2024.51.11.996
Regression testing focuses on testing changed parts of software to quickly find errors caused by changes. Unit testing individually tests each unit (i.e., a small component of software) to identify a bug quickly. We propose a new regression testing technique using unit testing with a dynamic unit state-based false alarm reduction model. Experimental results showed that when the proposed technique was applied to 10 C programs, acc@10 performance increased by 40%p compared to the state-of-the-art technique foridentifying a buggy function. For 7 programs, target regression bugs were ranked within the top 20% of the bugs reported by the proposed technique.
Categories and Patterns of Java Program Unit Test Code Bugs
http://doi.org/10.5626/JOK.2019.46.4.341
Since unit testing is widely used in many software projects, the threat of unit test bugs(i.e., bugs in the test case code) is becoming a more important issue of software quality assurance. Test code bugs are critical threats because they may invalidate the quality assurance process, which consequently hurts quality of products and performance of the project. This paper presents a set of test bug categories and a set of bug patterns extracted from real-world cases. Unlike the existing work on test code bugs, this paper suggests a classification method to systematically categorize different features of test code bugs (i.e., structures, operations, and requirements). In addition, this paper defines eight new bug patterns in unit test code, based on previous bug reports from well-known open-source projects. Each pattern is formally specified as source code patterns so that it can be used for to construct a static bug pattern checker.
Automated Unit-test Generation for Detecting Vulnerabilities of Android Kernel Modules
In this study, we propose an automated unit test generation technique for detecting vulnerabilities of Android kernel modules. The technique automatically generates unit test drivers/stubs and unit test inputs for each function of Android kernel modules by utilizing dynamic symbolic execution. To reduce false alarms caused by function pointers and missing pre-conditions of automated unit test generation technique, we develop false alarm reduction techniques that match function pointers by utilizing static analysis and generate pre-conditions by utilizing def-use analysis. We showed that the proposed technique could detect all existing vulnerabilities in the three modules of Android kernel 3.4. Also, the false alarm reduction techniques removed 44.9% of false alarms on average.
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