An electric fence for kernel buffers

One of the aspects of programming that learners often struggle with is the syntax of programming languages: remembering the right commands to use and combining those into a working program. Prior research demonstrated that students submit source code with syntax errors in 73% of cases and even the best students do so in 50% of cases. An analysis of 37 million compilations by 250.000 students found that the most common error was a syntax error, which occurred in almost 800.000 compilations. It was also found that Java and Perl are not easier to understand than a programming language with randomly generated keywords, stressing the difficulties that novices face in understanding syntax. This paper presents Hedy: a new way of teaching the syntax of a programming language to novices, inspired by educational methods by which punctuation is taught to children. Hedy starts as a simple programming language without any syntactic elements such as brackets, colons or indentation. The rules slowly and gradually change until the novices are programming in Python. Hedy is evaluated on 9714 programs.

Digital signal processors (DSP) are microprocessor- like processing elements that are specifically designed for signal and image processing algorithms. DSPs share many of the same failure mechanisms as microprocessors, including component crashes, program crashes and silent data corruption (SDC). The root of these problems is often single- event upsets (SEUs) in caches or control circuitry. In testing we have found DSPs are very sensitive to SDC, where computationally incorrect output data is produced but program execution is not affected. SDC can be problematic as it is hard to detect and could lead to the corruption of on-orbit data. We found that SDC could be reduced through the use of triple modular redundancy. In this paper we present data on how software on DSPs is susceptible to radiation-induced upsets and software techniques for reducing SDC.

The workstation file system for the Cedar programming environment was modified to improve its robustness and performance. Previously, the file system used hardware-provided labels on disk blocks to increase robustness against hardware and software errors. The new system does not require hardware disk labels, yet is more robust than the old system. Recovery is rapid after a crash. The performance of operations on file system metadata, e.g., file creation or open, is greatly improved.The new file system has two features that make it atypical. The system uses a log, as do most database systems, to recover metadata about the file system. To gain performance, it uses group commit, a concept derived from high performance database systems. The design of the system used a simple, yet detailed and accurate, analytical model to choose between several design alternatives in order to provide good disk performance.

An important threat to reliable storage of data is silent data corruption. In order to develop suitable protection mechanisms against data corruption, it is essential to understand its characteristics. In this article, we present the first large-scale study of data corruption. We analyze corruption instances recorded in production storage systems containing a total of 1.53 million disk drives, over a period of 41 months. We study three classes of corruption: checksum mismatches, identity discrepancies, and parity inconsistencies. We focus on checksum mismatches since they occur the most. We find more than 400,000 instances of checksum mismatches over the 41-month period. We find many interesting trends among these instances, including: (i) nearline disks (and their adapters) develop checksum mismatches an order of magnitude more often than enterprise-class disk drives, (ii) checksum mismatches within the same disk are not independent events and they show high spatial and temporal locality, and (iii) checksum mismatches across different disks in the same storage system are not independent. We use our observations to derive lessons for corruption-proof system design.

This article describes the dotplot data visualization technique and its potential for contributing to the identification of design patterns. Pattern languages have been used in architectural design and urban planning to codify related rules-of-thumb for constructing vernacular buildings and towns. When applied to software design, pattern languages promote reuse while allowing novice designers to learn from the insights of experts. Dotplots have been used in biology to study similarity in genetic sequences. When applied to software, dotplots identify patterns that range in abstraction from the syntax of programming languages to the organizational uniformity of large, multicomponent systems. Dotplots are useful for design by successive abstraction—replacing duplicated code with macros, subroutines, or classes. Dotplots reveal a pervasive design pattern for simplifying algorithms by increasing the complexity of initialization. Dotplots also reveal patterns of wordiness in languages—one example Inspired a design pattern for a new programming language. In addition, dotplots of data associated with programs Identify dynamic usage patterns—one example identifies a design pattern used in the construction of a UNIX(tm) file system. © 1996 John Wiley & Sons, Inc.

Dynamic syntax: A concept for the definition of the syntax of programming languages

Future high-performance computing (HPC) systems with ever-increasing resource capacity (such as compute cores, memory and storage) may significantly increase the risks on reliability. Silent data corruptions (SDCs) or silent errors are among the major sources that corrupt HPC execution results. Unlike fail-stop errors, SDCs can be harmful and dangerous in that they cannot be detected by hardware. To remedy this, we propose an online MAchine-learning-based silent data CORruption Detection framework (abbreviated as MACORD) for detecting SDCs in HPC applications. In our study, we comprehensively investigate the prediction ability of a multitude of machine-learning algorithms and enable the detector to automatically select the best-fit algorithms at runtime to adapt to the data dynamics. Because it takes only spatial features (i.e., neighboring data values for each data point in the current time step) into the training data, our learning framework exhibits low memory overhead (less than 1%). Experiments based on real-world scientific applications/benchmarks show that our framework can elevate the detection sensitivity (i.e., recall) up to 99%. Meanwhile the false positive rate is limited to 0.1% in most cases, which is one order of magnitude improvement compared with the latest state-of-the-art spatial technique.

Radiation-induced soft errors degrade the reliability of aerospace-based computing. Silent data corruption (SDC) is the most dangerous and insidious type of soft error result. To detect SDC, program invariant assertions are used to harden programs. However, there exist redundant assertions in hardened programs, which impairs the detection efficiency. Benign errors are another type of soft error result. An assertion may detect benign errors, incurring unnecessary recovery overhead. The detection degree of an assertion represents the detection capability, and an assertion with a high detection degree can detect severe errors. To improve the detection efficiency and detection degree while reducing the benign detection ratio, F_Radish is proposed in the present work to screen redundant assertions in a novel way. At a program point, the detection degree and benign detection ratio are considered to evaluate the importance of the assertions in the program point. As a result, only the most important assertion remains in the program point. Moreover, the redundancy degree is considered to screen redundant assertions for neighbouring program points. Experimental results show that in comparison with the Radish approach, the detection efficiency of F_Radish is about two times greater. Moreover, F_Radish reduces the benign detection ratio and improves the detection degree. It can avoid more unnecessary recovery overheads and detect more serious SDC than can Radish.

Critical systems that integrate software components (e.g., from third-parties) need to address the risk of residual software defects in these components. Software fault injection is an experimental solution to gauge such risk. Many error models have been proposed for emulating faulty components, such as by injecting error codes and exceptions, or by corrupting data with bit-flips, boundary values, and random values. Even if these error models have been able to find breaches in fragile systems, it is unclear whether these errors are in fact representative of software faults. To pursue this open question, we propose a methodology to analyze how software faults in C/C++ software components turn into errors at components' interfaces (interface error propagation), and present an experimental analysis on what, where, and when to inject interface errors. The results point out that the traditional error models, as used so far, do not accurately emulate software faults, but that richer interface errors need to be injected, by: injecting both fail-stop behaviors and data corruptions; targeting larger amounts of corrupted data structures; emulating silent data corruptions not signaled by the component; combining bit-flips, boundary values, and data perturbations.

Embedded systems' hardware can be impacted by soft errors, which can cause data flow errors in the systems' software. In this paper, we present a novel software-based approach to counter data flow errors, called Selective Duplication and Selective Comparison (SDSC). First, we validated our SDSC technique by implementing it for six case studies and submitting it to a fault injection campaign. Next, we measured its execution time overhead. To put the measured results into perspective, we compared them to those of two established techniques, called Critical Block Duplication (CBD) and near Zero silent Data Corruption (nZDC). The results show that our SDSC technique has a higher error detection ratio with a lower silent data corruption compared to both the CBD and nZDC techniques. This does, however, come with a slightly higher execution time overhead.

The C programming language is at least as well known for its absence of spatial memory safety guarantees (i.e., lack of bounds checking) as it is for its high performance. C's unchecked pointer arithmetic and array indexing allow simple programming mistakes to lead to erroneous executions, silent data corruption, and security vulnerabilities. Many prior proposals have tackled enforcing spatial safety in C programs by checking pointer and array accesses. However, existing software-only proposals have significant drawbacks that may prevent wide adoption, including: unacceptably high run-time overheads, lack of completeness, incompatible pointer representations, or need for non-trivial changes to existing C source code and compiler infrastructure.

This article shows how to use model checking to find serious errors in file systems. Model checking is a formal verification technique tuned for finding corner-case errors by comprehensively exploring the state spaces defined by a system. File systems have two dynamics that make them attractive for such an approach. First, their errors are some of the most serious, since they can destroy persistent data and lead to unrecoverable corruption. Second, traditional testing needs an impractical, exponential number of test cases to check that the system will recover if it crashes at any point during execution. Model checking employs a variety of state-reducing techniques that allow it to explore such vast state spaces efficiently.We built a system, FiSC, for model checking file systems. We applied it to four widely-used, heavily-tested file systems: ext3, JFS, ReiserFS and XFS. We found serious bugs in all of them, 33 in total. Most have led to patches within a day of diagnosis. For each file system, FiSC found demonstrable events leading to the unrecoverable destruction of metadata and entire directories, including the file system root directory “/”.

Recent non-volatile memory technologies such as 3D XPoint and NVDIMMs have enabled persistent memory (PM) systems that can manipulate persistent data directly in memory. This advancement of memory technology has spurred the development of a new set of crash-consistent software (CCS) for PM - applications that can recover persistent data from memory in a consistent state in the event of a crash (e.g., power failure). CCS developed for persistent memory ranges from kernel modules to user-space libraries and custom applications. However, ensuring crash consistency in CCS is difficult and error-prone. Programmers typically employ low-level hardware primitives or transactional libraries to enforce ordering and durability guarantees that are required for ensuring crash consistency. Unfortunately, hardware can reorder instructions at runtime, making it difficult for the programmers to test whether the implementation enforces the correct ordering and durability guarantees. We believe that there is an urgent need for developing a testing framework that helps programmers identify crash consistency bugs in their CCS. We find that prior testing tools lack generality, i.e., they work only for one specific CCS or memory persistency model and/or introduce significant performance overhead. To overcome these drawbacks, we propose PMTest (available at https://pmtest.persistentmemory.org), a crash consistency testing framework that is both flexible and fast. PMTest provides flexibility by providing two basic assertion-like software checkers to test two fundamental characteristics of all CCS: the ordering and durability guarantee. These checkers can also serve as the building blocks of other application-specific, high-level checkers. PMTest enables fast testing by deducing the persist order without exhausting all possible orders. In the evaluation with eight programs, PMTest not only identified 45 synthetic crash consistency bugs, but also detected 3 new bugs in a file system (PMFS) and in applications developed using a transactional library (PMDK), while on average being 7.1× faster than the state-of-the-art tool.

The job of the constraint programmer is to use mathematical constraints to model real world constraints and objects. In this book, Kim Marriott and Peter Stuckey provide the first comprehensive introduction to the discipline of constraint programming and, in particular, constraint logic programming. The book covers the necessary background material from artificial intelligence, logic programming, operations research, and mathematical programming. Topics discussed range from constraint-solving techniques to programming methodologies for constraint programming languages. Because there is not yet a universally used syntax for constraint logic programming languages, the authors present the programs in a way that is independent of any existing programming language. Practical exercises cover how to use the book with a number of existing constraint languages.

Code clones are widespread in software since programmers always reuse code to reduce programming effort. As programming languages are continuing to evolve and morph, code clones also widely exist across different languages for platform compatibility and adoption. Although code clones can improve development efficiency, they are prone to introducing bugs. Existing code clone detection technologies, however, mainly focused on single programming language or syntactical features of code. The syntax of different programming language are diverse because of syntax sugar, and many cloning pairs are semantic related instead of syntactic similar, such as Type 4 clones. To bridge the gap between syntax and semantic, and detect clone-related bugs more accurately, we explore an IR (Intermediate Representation) based method to represent code semantic representation information of multiple language code. We utilize graph neural network to learn code semantic representation. Through the semantic representation, we can detect more cross-language clone related bugs across multiple language.