Biblio

Race conditions are difficult to detect because they usually occur only under specific execution interleavings. Numerous program analysis and testing techniques have been proposed to detect race conditions between threads on single applications. However, most of these techniques neglect races that occur at the process level due to complex system event interactions. This article presents a framework, SIMEXPLORER, that allows engineers to effectively test for process-level race conditions. SIMEXPLORER first uses dynamic analysis techniques to observe system execution, identify program locations of interest, and report faults related to oracles. Next, it uses virtualization to achieve the fine-grained controllability needed to exercise event interleavings that are likely to expose races. We evaluated the effectiveness of SIMEXPLORER on 24 real-world applications containing both known and unknown process-level race conditions. Our results show that SIMEXPLORER is effective at detecting these race conditions, while incurring an overhead that is acceptable given its effectiveness improvements.

When multiple apps on an Android platform interact, faults and security vulnerabilities can occur. Software engineers need to be able to analyze interacting apps to detect such problems. Current approaches for performing such analyses, however, do not scale to the numbers of apps that may need to be considered, and thus, are impractical for application to realworld scenarios. In this paper, we introduce JITANA, a program analysis framework designed to analyze multiple Android apps simultaneously. By using a classloader-based approach instead of a compiler-based approach such as SOOT, JITANA is able to simultaneously analyze large numbers of interacting apps, perform on-demand analysis of large libraries, and effectively analyze dynamically generated code. Empirical studies of JITANA show that it is substantially more efficient than a state-of-theart approach, and that it can effectively and efficiently analyze complex apps including Facebook, Pokemon Go, and Pandora ´ that the state-of-the-art approach cannot handle.

Non-volatile memory express (NVMe) based SSDs and the NUMA platform are widely adopted in servers to achieve faster storage speed and more powerful processing capability. As of now, very little research has been conducted to investigate the performance and energy efficiency of the stateof-the-art NUMA architecture integrated with NVMe SSDs, an emerging technology used to host parallel I/O threads. As this technology continues to be widely developed and adopted, we need to understand the runtime behaviors of such systems in order to design software runtime systems that deliver optimal performance while consuming only the necessary amount of energy. This paper characterizes the runtime behaviors of a Linuxbased NUMA system employing multiple NVMe SSDs. Our comprehensive performance and energy-efficiency study using massive numbers of parallel I/O threads shows that the penalty due to CPU contention is much smaller than that due to remote access of NVMe SSDs. Based on this insight, we develop a dynamic “lesser evil” algorithm called ESN, to minimize the impact of these two types of penalties. ESN is an energyefficient profiling-based I/O thread scheduler for managing I/O threads accessing NVMe SSDs on NUMA systems. Our empirical evaluation shows that ESN can achieve optimal I/O throughput and latency while consuming up to 50% less energy and using fewer CPUs.

Though immutability has been long-proposed as a way to prevent bugs in software, little is known about how to make immutability support in programming languages effective for software engineers. We designed a new formalism that extends Java to support transitive class immutability, the form of immutability for which there is the strongest empirical support, and implemented that formalism in a tool called Glacier. We applied Glacier successfully to two real-world systems. We also compared Glacier to Java’s final in a user study of twenty participants. We found that even after being given instructions on how to express immutability with final, participants who used final were unable to express immutability correctly, whereas almost all participants who used Glacier succeeded. We also asked participants to make specific changes to immutable classes and found that participants who used final all incorrectly mutated immutable state, whereas almost all of the participants who used Glacier succeeded. Glacier represents a promising approach to enforcing immutability in Java and provides a model for enforcement in other languages.

Concurrent programs are prone to various classes of difficult-to-detect faults, of which data races are particularly prevalent. Prior work has attempted to increase the cost-effectiveness of approaches for testing for data races by employing race detection techniques, but to date, no work has considered cost-effective approaches for re-testing for races as programs evolve. In this paper we present SimRT, an automated regression testing framework for use in detecting races introduced by code modifications. SimRT employs a regression test selection technique, focused on sets of program elements related to race detection, to reduce the number of test cases that must be run on a changed program to detect races that occur due to code modifications, and it employs a test case prioritization technique to improve the rate at which such races are detected. Our empirical study of SimRT reveals that it is more efficient and effective for revealing races than other approaches, and that its constituent test selection and prioritization components each contribute to its performance.