Effective testing for concurrency bugs

In the current multi-core era, concurrency bugs are a serious threat to software reliability. As hardware becomes more parallel, concurrent programming will become increasingly pervasive. However, correct concurrent programming is known to be extremely challenging for developers and can easily lead to the introduction of concurrency bugs. This dissertation addresses this challenge by proposing novel techniques to help developers expose and detect concurrency bugs. We conducted a bug study to better understand the external and internal effects of real-world concurrency bugs. Our study revealed that a significant fraction of concurrency bugs qualify as semantic or latent bugs, which are two particularly challenging classes of concurrency bugs. Based on the insights from the study, we propose a concurrency bug detector, PIKE that analyzes the behavior of program executions to infer whether concurrency bugs have been triggered during a concurrent execution. In addition, we present the design of a testing tool, SKI, that allows developers to test operating system kernels for concurrency bugs in a practical manner. SKI bridges the gap between user-mode testing and kernel-mode testing by enabling the systematic exploration of the kernel thread interleaving space. Our evaluation shows that both PIKE and SKI are effective at finding concurrency bugs.Im gegenwärtigen Multicore-Zeitalter sind Fehler aufgrund von Nebenläufigkeit eine ernsthafte Bedrohung der Zuverlässigkeit von Software. Mit der wachsenden Parallelisierung von Hardware wird nebenläufiges Programmieren nach und nach allgegenwärtig. Diese Art von Programmieren ist jedoch als äußerst schwierig bekannt und kann leicht zu Programmierfehlern führen. Die vorliegende Dissertation nimmt sich dieser Herausforderung an indem sie neuartige Techniken vorschlägt, die Entwicklern beim Aufdecken von Nebenläufigkeitsfehlern helfen. Wir führen eine Studie von Fehlern durch, um die externen und internen Effekte von in der Praxis vorkommenden Nebenläufigkeitsfehlern besser zu verstehen. Diese ergibt, dass ein bedeutender Anteil von solchen Fehlern als semantisch bzw. latent zu charakterisieren ist -- zwei besonders herausfordernde Klassen von Nebenläufigkeitsfehlern. Basierend auf den Erkenntnissen der Studie entwickeln wir einen Detektor (PIKE), der Programmausführungen daraufhin analysiert, ob Nebenläufigkeitsfehler aufgetreten sind. Weiterhin präsentieren wir das Design eines Testtools (SKI), das es Entwicklern ermöglicht, Betriebssystemkerne praktikabel auf Nebenläufigkeitsfehler zu überprüfen. SKI füllt die Lücke zwischen Testen im Benutzermodus und Testen im Kernelmodus, indem es die systematische Erkundung der Kernel-Thread-Verschachtelungen erlaubt. Unsere Auswertung zeigt, dass sowohl PIKE als auch SKI effektiv Nebenläufigkeitsfehler finden

μAkka: Mutation Testing for Actor Concurrency in Akka Using Real-World Bugs

Actor concurrency is becoming increasingly important in the real-world and mission-critical software. This requires these applications to be free from actor bugs, that occur in the real world, and have tests that are effective in finding these bugs. Mutation testing is a well-established technique that transforms an application to induce its likely bugs and evaluate the effectiveness of its tests in finding these bugs. Mutation testing is available for a broad spectrum of applications and their bugs, ranging from web to mobile to machine learning, and is used at scale in companies like Google and Facebook. However, there still is no mutation testing for actor concurrency that uses real-world actor bugs. In this paper, we propose Akka, a framework for mutation testing of Akka actor concurrency using real actor bugs. Akka is a popular industrial-strength implementation of actor concurrency. To design, implement, and evaluate Akka, we take the following major steps: (1) manually analyze a recent set of 186 real Akka bugs from Stack Overflow and GitHub to understand their causes; (2) design a set of 32 mutation operators, with 138 source code changes in Akka API, to emulate these causes and induce their bugs; (3) implement these operators in an Eclipse plugin for Java Akka; (4) use the plugin to generate 11.7k mutants of 10 real GitHub applications, with 446.4k lines of code and 7.9k tests; (5) run these tests on these mutants to measure the quality of mutants and effectiveness of tests; (6) use PIT to generate 26.2k mutants to compare Akka and PIT mutant quality and test effectiveness. PIT is a popular mutation testing tool with traditional operators; (7) manually analyze the bug coverage and overlap of Akka, PIT, and actor operators in a previous work; and (8) discuss a few implications of our findings. Among others, we find that Akka mutants are higher quality, cover more bugs, and tests are less effective in detecting them

Finding and Tolerating Concurrency Bugs.

Shared-memory multi-threaded programming is inherently more difficult than single-threaded programming. The main source of complexity is that, the threads of an application can interleave in so many different ways. To ensure correctness, a programmer has to test all possible thread interleavings, which, however, is impractical. Many rare thread interleavings remain untested in production systems, and they are the major cause for a majority of concurrency bugs. Given that untested interleavings are the major cause of a majority of the concurrency bugs, this dissertation explores two possible ways to tackle concurrency bugs in this dissertation. One is to expose untested interleavings during testing to find concurrency bugs. The other is to avoid untested interleavings during production runs to tolerate concurrency bugs. The key is an efficient and effective way to encode and remember tested interleavings. This dissertation first discusses two hypotheses about concurrency bugs: the small scope hypothesis and the value independent hypothesis. Based on these two hypotheses, this dissertation defines a set of interleaving patterns, called interleaving idioms, which are used to encode tested interleavings. The empirical analysis shows that the idiom based interleaving encoding scheme is able to represent most of the concurrency bugs that are used in the study. Then, this dissertation discusses an open source testing tool called Maple. It memoizes tested interleavings and actively seeks to expose untested interleavings. The results show that Maple is able to expose concurrency bugs and expose interleavings faster than other conventional testing techniques. Finally, this dissertation discusses two parallel runtime system designs which seek to avoid untested interleavings during production runs to tolerate concurrency bugs. Avoiding untested interleavings significantly improve correctness because most of the concurrency bugs are caused by untested interleavings. Also, the performance overhead for disallowing untested interleavings is low as commonly occuring interleavings should have been tested in a well-tested program.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/99765/1/jieyu_1.pd

Effective fault localization techniques for concurrent software

Multicore and Internet cloud systems have been widely adopted in recent years and have resulted in the increased development of concurrent programs. However, concurrency bugs are still difficult to test and debug for at least two reasons. Concurrent programs have large interleaving space, and concurrency bugs involve complex interactions among multiple threads. Existing testing solutions for concurrency bugs have focused on exposing concurrency bugs in the large interleaving space, but they often do not provide debugging information for developers to understand the bugs. To address the problem, this thesis proposes techniques that help developers in debugging concurrency bugs, particularly for locating the root causes and for understanding them, and presents a set of empirical user studies that evaluates the techniques. First, this thesis introduces a dynamic fault-localization technique, called Falcon, that locates single-variable concurrency bugs as memory-access patterns. Falcon uses dynamic pattern detection and statistical fault localization to report a ranked list of memory-access patterns for root causes of concurrency bugs. The overall Falcon approach is effective: in an empirical evaluation, we show that Falcon ranks program fragments corresponding to the root-cause of the concurrency bug as "most suspicious" almost always. In principle, such a ranking can save a developer's time by allowing him or her to quickly hone in on the problematic code, rather than having to sort through many reports. Others have shown that single- and multi-variable bugs cover a high fraction of all concurrency bugs that have been documented in a variety of major open-source packages; thus, being able to detect both is important. Because Falcon is limited to detecting single-variable bugs, we extend the Falcon technique to handle both single-variable and multi-variable bugs, using a unified technique, called Unicorn. Unicorn uses online memory monitoring and offline memory pattern combination to handle multi-variable concurrency bugs. The overall Unicorn approach is effective in ranking memory-access patterns for single- and multi-variable concurrency bugs. To further assist developers in understanding concurrency bugs, this thesis presents a fault-explanation technique, called Griffin, that provides more context of the root cause than Unicorn. Griffin reconstructs the root cause of the concurrency bugs by grouping suspicious memory accesses, finding suspicious method locations, and presenting calling stacks along with the buggy interleavings. By providing additional context, the overall Griffin approach can provide more information at a higher-level to the developer, allowing him or her to more readily diagnose complex bugs that may cross file or module boundaries. Finally, this thesis presents a set of empirical user studies that investigates the effectiveness of the presented techniques. In particular, the studies compare the effectiveness between a state-of-the-art debugging technique and our debugging techniques, Unicorn and Griffin. Among our findings, the user study shows that while the techniques are indistinguishable when the fault is relatively simple, Griffin is most effective for more complex faults. This observation further suggests that there may be a need for a spectrum of tools or interfaces that depend on the complexity of the underlying fault or even the background of the user.Ph.D

A Study of Concurrency Bugs and Advanced Development Support for Actor-based Programs

The actor model is an attractive foundation for developing concurrent applications because actors are isolated concurrent entities that communicate through asynchronous messages and do not share state. Thereby, they avoid concurrency bugs such as data races, but are not immune to concurrency bugs in general. This study taxonomizes concurrency bugs in actor-based programs reported in literature. Furthermore, it analyzes the bugs to identify the patterns causing them as well as their observable behavior. Based on this taxonomy, we further analyze the literature and find that current approaches to static analysis and testing focus on communication deadlocks and message protocol violations. However, they do not provide solutions to identify livelocks and behavioral deadlocks. The insights obtained in this study can be used to improve debugging support for actor-based programs with new debugging techniques to identify the root cause of complex concurrency bugs.Comment: - Submitted for review - Removed section 6 "Research Roadmap for Debuggers", its content was summarized in the Future Work section - Added references for section 1, section 3, section 4.3 and section 5.1 - Updated citation

Exposing errors related to weak memory in GPU applications

© 2016 ACM.We present the systematic design of a testing environment that uses stressing and fuzzing to reveal errors in GPU applications that arise due to weak memory effects. We evaluate our approach on seven GPUS spanning three NVIDIA architectures, across ten CUDA applications that use fine-grained concurrency. Our results show that applications that rarely or never exhibit errors related to weak memory when executed natively can readily exhibit these errors when executed in our testing environment. Our testing environment also provides a means to help identify the root causes of such errors, and automatically suggests how to insert fences that harden an application against weak memory bugs. To understand the cost of GPU fences, we benchmark applications with fences provided by the hardening strategy as well as a more conservative, sound fencing strategy