Team, Visitors, External Collaborators
Overall Objectives
Research Program
Application Domains
Highlights of the Year
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
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Section: New Results

Software engineering for infrastructure software

Data races are often hard to detect in device drivers, due to the non-determinism of concurrent execution. With colleagues from Tsinghua University, we have addressed this issue using dynamic analysis. According to our study of Linux driver patches that fix data races, more than 38% of patches involve a pattern that we call inconsistent lock protection. Specifically, if a variable is accessed within two concurrently executed functions, the sets of locks held aroundeach access are disjoint, at least one of the locksets is non-empty, and at least one of the involved accesses is a write, then a datarace may occur. In a paper published at SANER 2019 [17], we present a runtime analysis approach, named DILP, to detect data races caused by inconsistent lock protection in device drivers. By monitoring driver execution, DILP collects the information about runtime variable accesses and executed functions. Then after driver execution, DILP analyzes the collected information to detect and report data races caused by inconsistent lock protection. We evaluate DILP on 12 device drivers in Linux 4.16.9, and find 25 real data races.

For waiting, the Linux kernel offers both sleep-able and non-sleep operations. However, only non-sleep operations can be used in atomic context. Detecting the possibility of execution in atomic context requires a complete inter-procedural flow analysis, often involving function pointers. Developers may thus conservatively use non-sleep operations even outside of atomic context, which may damage system performance, as such operations unproductively monopolize the CPU. Until now, no systematic approach has been proposed to detect such conservative non-sleep (CNS) defects. In a paper published at ASPLOS 2019 [14] with colleagues from Tsinghua University, we propose a practical static approach, named DCNS, to automatically detect conservative non-sleep defects in the Linux kernel. DCNS uses a summary-based analysis to effectively identify the code in atomic context and a novel file-connection-based alias analysis to correctly identify the set of functions referenced by a function pointer. We evaluate DCNS on Linux 4.16, and in total find 1629 defects. We manually check 943 defects whose call paths are not so difficult to follow, and find that 890 are real. We have randomly selected 300 of the real defects and sent them to kernel developers, and 251 have been confirmed.

In Linux device drivers, use-after-free (UAF) bugs can cause system crashes and serious security problems. We have addressed this issue in work with colleagues at Tsinghua University. According to our study of Linux kernel commits, 42% of the driver commits fixing use-after-free bugs involve driver concurrency. We refer to these use-after-free bugs as concurrency use-after-free bugs. Due to the non-determinism of concurrent execution, concurrency use-after-free bugs are often more difficult to reproduce and detect than sequential use-after-free bugs. In a paper published at USENIX ATC 2019 [13], we propose a practical static analysis approach named DCUAF, to effectively detect concurrency use-after-free bugs in Linux device drivers. DCUAF combines a local analysis analyzing the source code of each driver with a global analysis statistically analyzing the local results of all drivers, forming a local-global analysis, to extract the pairs of driver interface functions that may be concurrently executed. Then, with these pairs, DCUAF performs a summary-based lockset analysis to detect concurrency use-after-free bugs. We have evaluated DCUAF on the driver code of Linux 4.19, and found 640 real concurrency use-after-free bugs. We have randomly selected 130 of the real bugs and reported them to Linux kernel developers, and 95 have been confirmed.

Linux kernel stable versions serve the needs of users who value stability of the kernel over new features. The quality of such stableversions depends on the initiative of kernel developers and maintainers to propagate bug fixing patches to the stable versions. Thus, it is desirable to consider to what extent this process can be automated. A previous approach relies on words from commit messages and a small set of manually constructed code features. This approach, however, shows only moderate accuracy. In a tool paper published ICSE 2019 [11], in the context of the ANR-NRF ITrans project with colleagues from Singapore Management University, paper, we investigate whether deep learning can provide a more accurate solution. We propose PatchNet, a hierarchical deep learning-based approach capable of automatically extracting features from commit messages and commit code and usingthem to identify stable patches. PatchNet contains a deep hierarchical structure that mirrors the hierarchical and sequential structure of commit code, making it distinctive from the existing deep learning models on source code. Experiments on 82,403 recent Linux patches confirm the superiority of PatchNet against various state-of-the-art baselines, including the one recently-adopted by Linux kernel maintainers.

Developing software often requires code changes that are widespread and applied to multiple locations. Previously, the Whisper team has addressed this problem with the tool Coccinelle. In a recent experience paper, published at ECOOP 2019 [21], in the context of the ANR-NRF ITrans project with colleagues from Singapore Management University, we have considered the benefits of extending Coccinelle to Java code. There are tools for Java that allow developers to specify patterns for program matching and source-to-source transformation. However, to our knowledge, none allows for transforming code based on its control-flow context. We prototype Coccinelle4J, an extension to Coccinelle, which is a program transformation tool designed for widespread changes in C code, in order to work on Java source code. We adapt Coccinelle to be able to apply scripts written in the Semantic Patch Language (SmPL), a language provided by Coccinelle, to Java source files. As a case study, we demonstrate the utility of Coccinelle4J with the task of API migration. We show 6 semantic patches to migrate from deprecated Android API methods on several open source Android projects. We describe how SmPL can be used to express several API migrations and justify several of our design decisions. This paper was accompanied by a tool demo.

A challenge in designing cooperative distributed systems is to develop feasible and cost-effective mechanisms to foster cooperation among selfish nodes, i.e., nodes that strategically deviate from the intended specification to increase their individual utility. Finding a satisfactory solution to this challenge may be complicated by the intrinsic characteristics of each system, as well as by the particular objectives set by the system designer. In a previous work we addressed this challenge by proposing RACOON, a general and semi-automatic framework for designing selfishness-resilient cooperative systems. RACOON relies on classical game theory and a custom built simulator to predict the impact of a fixed set of selfish behaviours on the designer's objectives. In a paper published in IEEE Transactions on Dependable and Secure Computing [12], we present RACOON++, which extends the previous framework with a declarative model for defining the utility function and the static behaviour of selfish nodes, along with a new model for reasoning on the dynamic interactions of nodes, based on evolutionary game theory. We illustrate the benefits of using RACOON++ by designing three cooperative systems: a peer-to-peer live streaming system, a load balancing protocol, and an anonymous communication system. Extensive experimental results using the state-of-the-art PeerSim simulator verify that the systems designed using RACOON++ achieve both selfishness-resilience and high performance.