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We present the design and implementation of GVM, the first system for executing Java bytecode entirely on GPUs. GVM is ideal for applications that execute a large number of short-living tasks, which share a significant fraction of their codebase and have similar execution time. GVM uses novel algorithms, scheduling, and data layout techniques to adapt to the massively parallel programming and execution model of GPUs. We apply GVM to generate and execute tests for Java projects. First, we implement a sequence-based test generation on top of GVM and design novel algorithms to avoid redundant test sequences. Second, we use GVM to execute randomly generated test cases. We evaluate GVM by comparing it with two existing Java bytecode interpreters (Oracle JVM and Java Pathfinder), as well as with the Oracle JVM with just-in-time (JIT) compiler, which has been engineered and optimized for over twenty years. Our evaluation shows that sequence-based test generation on GVM outperforms both Java Pathfinder and Oracle JVM interpreter. Additionally, our results show that GVM performs as well as running our parallel sequence-based test generation algorithm using JVM with JIT with many CPU threads. Furthermore, our evaluation on several classes from open-source projects shows that executing randomly generated tests on GVM outperforms sequential execution on JVM interpreter and JVM with JIT.
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Ensuring the Observability of Structural Test Obligations
Test adequacy criteria are widely used to guide test creation. However, many of these criteria are sensitive to statement structure or the choice of test oracle. This is because such criteria ensure that execution reaches the element of interest, but impose no constraints on the execution path after this point. We are not guaranteed to observe a failure just because a fault is triggered. To address this issue, we have proposed the concept of observability—an extension to coverage criteria based on Boolean expressions that combines the obligations of a host criterion with an additional path condition that increases the likelihood that a fault encountered will propagate to a monitored variable. Our study, conducted over five industrial systems and an additional forty open-source systems, has revealed that adding observability tends to improve efficacy over satisfaction of the traditional criteria, with average improvements of 125.98% in mutation detection with the common output-only test oracle and per-model improvements of up to 1760.52%. Ultimately, there is merit to our hypothesis—observability reduces sensitivity to the choice of oracle and to the program structure.
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- Award ID(s):
- 1657299
- PAR ID:
- 10082000
- Date Published:
- Journal Name:
- IEEE Transactions on Software Engineering
- ISSN:
- 0098-5589
- Page Range / eLocation ID:
- 1 to 1
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/TSE.2018.2869146
