CONTEXT-AWARE DEBUGGING FOR CONCURRENT PROGRAMS

Concurrency faults are difficult to reproduce and localize because they usually occur under specific inputs and thread interleavings. Most existing fault localization techniques focus on sequential programs but fail to identify faulty memory access patterns across threads, which are usually the root causes of concurrency faults. Moreover, existing techniques for sequential programs cannot be adapted to identify faulty paths in concurrent programs. While concurrency fault localization techniques have been proposed to analyze passing and failing executions obtained from running a set of test cases to identify faulty access patterns, they primarily focus on using statistical analysis. We present a novel approach to fault localization using feature selection techniques from machine learning. Our insight is that the concurrency access patterns obtained from a large volume of coverage data generally constitute high dimensional data sets, yet existing statistical analysis techniques for fault localization are usually applied to low dimensional data sets. Each additional failing or passing run can provide more diverse information, which can help localize faulty concurrency access patterns in code. The patterns with maximum feature diversity information can point to the most suspicious pattern. We then apply data mining technique and identify the interleaving patterns that are occurred most frequently and provide the possible faulty paths. We also evaluate the effectiveness of fault localization using test suites generated from different test adequacy criteria. We have evaluated Cadeco on 10 real-world multi-threaded Java applications. Results indicate that Cadeco outperforms state-of-the-art approaches for localizing concurrency faults

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

ORDER VIOLATION IN MULTITHREADED APPLICATIONS AND ITS DETECTION IN STATIC CODE ANALYSIS PROCESS

The subject presented in the paper concerns resource conflicts, which are the cause of order violation in multithreaded applications. The work focuses on developing conditions that can be implemented as a tool for allowing to detect these conflicts in the process of static code analysis. The research is based on known errors reported to developers of large applications such as Mozilla Firefox browser and MySQL relational database system. These errors could have been avoided by appropriate monitoring of the source code

Large-Scale Analysis of Framework-Specific Exceptions in Android Apps

Mobile apps have become ubiquitous. For app developers, it is a key priority to ensure their apps' correctness and reliability. However, many apps still suffer from occasional to frequent crashes, weakening their competitive edge. Large-scale, deep analyses of the characteristics of real-world app crashes can provide useful insights to guide developers, or help improve testing and analysis tools. However, such studies do not exist -- this paper fills this gap. Over a four-month long effort, we have collected 16,245 unique exception traces from 2,486 open-source Android apps, and observed that framework-specific exceptions account for the majority of these crashes. We then extensively investigated the 8,243 framework-specific exceptions (which took six person-months): (1) identifying their characteristics (e.g., manifestation locations, common fault categories), (2) evaluating their manifestation via state-of-the-art bug detection techniques, and (3) reviewing their fixes. Besides the insights they provide, these findings motivate and enable follow-up research on mobile apps, such as bug detection, fault localization and patch generation. In addition, to demonstrate the utility of our findings, we have optimized Stoat, a dynamic testing tool, and implemented ExLocator, an exception localization tool, for Android apps. Stoat is able to quickly uncover three previously-unknown, confirmed/fixed crashes in Gmail and Google+; ExLocator is capable of precisely locating the root causes of identified exceptions in real-world apps. Our substantial dataset is made publicly available to share with and benefit the community.Comment: ICSE'18: the 40th International Conference on Software Engineerin

OSCAR. A Noise Injection Framework for Testing Concurrent Software

“Moore’s Law” is a well-known observable phenomenon in computer science that describes a visible yearly pattern in processor’s die increase. Even though it has held true for the last 57 years, thermal limitations on how much a processor’s core frequencies can be increased, have led to physical limitations to their performance scaling. The industry has since then shifted towards multicore architectures, which offer much better and scalable performance, while in turn forcing programmers to adopt the concurrent programming paradigm when designing new software, if they wish to make use of this added performance. The use of this paradigm comes with the unfortunate downside of the sudden appearance of a plethora of additional errors in their programs, stemming directly from their (poor) use of concurrency techniques. Furthermore, these concurrent programs themselves are notoriously hard to design and to verify their correctness, with researchers continuously developing new, more effective and effi- cient methods of doing so. Noise injection, the theme of this dissertation, is one such method. It relies on the “probe effect” — the observable shift in the behaviour of concurrent programs upon the introduction of noise into their routines. The abandonment of ConTest, a popular proprietary and closed-source noise injection framework, for testing concurrent software written using the Java programming language, has left a void in the availability of noise injection frameworks for this programming language. To mitigate this void, this dissertation proposes OSCAR — a novel open-source noise injection framework for the Java programming language, relying on static bytecode instrumentation for injecting noise. OSCAR will provide a free and well-documented noise injection tool for research, pedagogical and industry usage. Additionally, we propose a novel taxonomy for categorizing new and existing noise injection heuristics, together with a new method for generating and analysing concurrent software traces, based on string comparison metrics. After noising programs from the IBM Concurrent Benchmark with different heuristics, we observed that OSCAR is highly effective in increasing the coverage of the interleaving space, and that the different heuristics provide diverse trade-offs on the cost and benefit (time/coverage) of the noise injection process.Resumo A “Lei de Moore” é um fenómeno, bem conhecido na área das ciências da computação, que descreve um padrão evidente no aumento anual da densidade de transístores num processador. Mesmo mantendo-se válido nos últimos 57 anos, o aumento do desempenho dos processadores continua garrotado pelas limitações térmicas inerentes `a subida da sua frequência de funciona- mento. Desde então, a industria transitou para arquiteturas multi núcleo, com significativamente melhor e mais escalável desempenho, mas obrigando os programadores a adotar o paradigma de programação concorrente ao desenhar os seus novos programas, para poderem aproveitar o desempenho adicional que advém do seu uso. O uso deste paradigma, no entanto, traz consigo, por consequência, a introdução de uma panóplia de novos erros nos programas, decorrentes diretamente da utilização (inadequada) de técnicas de programação concorrente. Adicionalmente, estes programas concorrentes são conhecidos por serem consideravelmente mais difíceis de desenhar e de validar, quanto ao seu correto funcionamento, incentivando investi- gadores ao desenvolvimento de novos métodos mais eficientes e eficazes de o fazerem. A injeção de ruído, o tema principal desta dissertação, é um destes métodos. Esta baseia-se no “efeito sonda” (do inglês “probe effect”) — caracterizado por uma mudança de comportamento observável em programas concorrentes, ao terem ruído introduzido nas suas rotinas. Com o abandono do Con- Test, uma framework popular, proprietária e de código fechado, de análise dinâmica de programas concorrentes através de injecção de ruído, escritos com recurso `a linguagem de programação Java, viu-se surgir um vazio na oferta de framework de injeção de ruído, para esta mesma linguagem. Para mitigar este vazio, esta dissertação propõe o OSCAR — uma nova framework de injeção de ruído, de código-aberto, para a linguagem de programação Java, que utiliza manipulação estática de bytecode para realizar a introdução de ruído. O OSCAR pretende oferecer uma ferramenta livre e bem documentada de injeção de ruído para fins de investigação, pedagógicos ou até para a indústria. Adicionalmente, a dissertação propõe uma nova taxonomia para categorizar os dife- rentes tipos de heurísticas de injecção de ruídos novos e existentes, juntamente com um método para gerar e analisar traces de programas concorrentes, com base em métricas de comparação de strings. Após inserir ruído em programas do IBM Concurrent Benchmark, com diversas heurísticas, ob- servámos que o OSCAR consegue aumentar significativamente a dimensão da cobertura do espaço de estados de programas concorrentes. Adicionalmente, verificou-se que diferentes heurísticas produzem um leque variado de prós e contras, especialmente em termos de eficácia versus eficiência

Spectrum-Based Fault Localization for Diagnosing Concurrency Faults

Due to copyright restrictions, the access to the full text of this article is only available via subscription.Concurrency faults are activated by specific thread interleavings at runtime. Traditional fault localization techniques and static analysis fall short to diagnose these faults efficiently. Existing dynamic fault-localization techniques focus on pinpointing data-access patterns that are subject to concurrency faults. In this paper, we propose a spectrum-based fault localization technique for localizing faulty code blocks instead. We systematically instrument the program to create versions that run in particular combinations of thread interleavings. We run tests on all these versions and utilize spectrum-based fault localization to correlate detected errors with concurrently executing code blocks. We have implemented a tool and applied our approach on several industrial case studies. Case studies show that our approach can effectively and efficiently localize concurrency faults