Data races are a common problem in concurrent and multi‐threaded programming. Experience shows that the classical notion of a data race is not powerful enough to capture certain types of inconsistencies occurring in practice. This paper investigates data races on a higher abstraction layer. This enables detection of inconsistent uses of shared variables, even if no classical race condition occurs. For example, a data structure representing a coordinate pair may have to be treated atomically. By lifting the meaning of a data race to a higher level, such problems can now be covered. The paper defines the concepts ‘view’ and ‘view consistency’ to give a notation for this novel kind of property. It describes what kinds of errors can be detected with this new definition, and where its limitations are. It also gives a formal guideline for using data structures in a multi‐threaded environment. © US Government copyright

QC 20160103

QC 20160103

QC 20160103

No abstract

We propose Guided Random Testing (GRT), which uses static and dynamic analysis to include information on program types, data, and dependencies in various stages of automated test generation. Static analysis extracts knowledge from the system under test. Test coverage is further improved through state fuzzing and continuous coverage analysis. We evaluated GRT on 32 real-world projects and found that GRT outperforms major peer techniques in terms of code coverage (by 13 %) and mutation score (by 9 %). On the four studied benchmarks of Defects4J, which contain 224 real faults, GRT also shows better fault detection capability than peer techniques, finding 147 faults (66 %). Furthermore, in an in-depth evaluation on the latest versions of ten popular real-world projects, GRT successfully detects over 20 unknown defects that were confirmed by developers.

Memory deduplication shares same-content memory pages and reduces the consumption of physical memory. It is effective on environments that run many virtual machines with the same operating system. Memory deduplication, however, is vulnerable to memory disclosure attacks, which reveal the existence of an application or file on another virtual machine. Such an attack takes advantage of a difference in write access times on deduplicated memory pages that are recreated by Copy-On-Write. In our experience on KSM (kernel samepage merging) with the KVM virtual machine, the attack could detect the existence of sshd and apache2 on Linux, and IE6 and Firefox on WindowsXP. It also could detect a downloaded file on the Firefox browser. We describe the attack mechanism in this paper, and also mention countermeasures against this attack.

JNuke is a framework for verification and model checking of Java programs. It is a novel combination of run-time verification, explicit-state model checking, and counter-example exploration. Efficiency is crucial in dynamic verification. Therefore JNuke has been written from scratch in C, improving performance and memory usage by an order of magnitude compared to competing approaches and tools.