Vectorizing Program Ingredients for Better JVM Testing

Gao，Tianchang1; Chen，Junjie1; Zhao，Yingquan1; Zhang，Yuqun2; Zhang，Lingming3

10.1145/3597926.3598075

2023

32nd ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA)

PROCEEDINGS OF THE 32ND ACM SIGSOFT INTERNATIONAL SYMPOSIUM ON SOFTWARE TESTING AND ANALYSIS, ISSTA 2023

JUL 17-21, 2023

null,Seattle,WA

1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES

ASSOC COMPUTING MACHINERY

JVM testing is one of the most widely-used methodologies for guaranteeing the quality of JVMs. Among various JVM testing techniques, synthesis-based JVM testing, which constructs a test program by synthesizing various code snippets (also called program ingredients), has been demonstrated state-of-the-art. The existing synthesis-based JVM testing work puts more efforts in ensuring the validity of synthesized test programs, but ignores the influence of huge ingredient space, which largely limits the ingredient exploration efficiency as well as JVM testing performance. In this work, we propose Vectorized JVM Testing (called VECT) to further promote the performance of synthesis-based JVM testing. Its key insight is to reduce the huge ingredient space by clustering semantically similar ingredients via vectorizing ingredients using state-of-the-art code representation. To make VECT complete and more effective, based on vectorized ingredients, VECT further designs a feedback-driven ingredient selection strategy and an enhanced test oracle. We conducted an extensive study to evaluate VECT on three popular JVMs (i.e., HotSpot, OpenJ9, and Bisheng JDK) involving five OpenJDK versions. The results demonstrate VECT detects 115.03%similar to 776.92% more unique inconsistencies than the state-of-the-art JVM testing technique during the same testing time. In particular, VECT detects 26 previously unknown bugs for them, 15 of which have already been confirmed/fixed by developers.

Java Virtual Machine Program Synthesis JVM Testing Test Oracle

其他

英语

EI ; CPCI-S

National Natural Science Foundation of China["62232001","62002256"]

Computer Science

Computer Science, Software Engineering ; Computer Science, Theory & Methods

WOS:001122661400043

Web of Science

1.College of Intelligence and Computing,Tianjin University,China
2.Southern University of Science and Technology,China
3.University of Illinois,Urbana-Champaign,United States

Gao，Tianchang,Chen，Junjie,Zhao，Yingquan,et al. Vectorizing Program Ingredients for Better JVM Testing[C]. 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES:ASSOC COMPUTING MACHINERY,2023.