Behavioral Consistency of C and Verilog Programs Using Bounded Model Checking

Clarke, Edmund M.; Kroening, Daniel; Yorav, Karen

doi:10.21236/ada461052

Cited by 65 publications

(10 citation statements)

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“…This logico-deductive approach has tremendous appeal in programming languages research, as it holds the promise of proving facts and properties of the program. Many elegant and powerful abstractions, definitions, algorithms, and proof techniques have been developed, which have led to important practical tools for program verification, bug finding, and refactoring [24,42,45]. It should be emphasized that these are theory-first approaches.…”

Section: Introductionmentioning

confidence: 99%

A Survey of Machine Learning for Big Code and Naturalness

et al. 2018

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Research at the intersection of machine learning, programming languages, and software engineering has recently taken important steps in proposing learnable probabilistic models of source code that exploit code's abundance of patterns. In this article, we survey this work. We contrast programming languages against natural languages and discuss how these similarities and differences drive the design of probabilistic models. We present a taxonomy based on the underlying design principles of each model and use it to navigate the literature. Then, we review how researchers have adapted these models to application areas and discuss crosscutting and application-specific challenges and opportunities.1 It may be worth pointing out that deep learning and probabilistic modeling are not mutually exclusive. Indeed, many of the currently most effective methods for language modeling, for example, are based on deep learning. of probabilistic source code models (Section 5). Finally, we mention a few overlapping research areas (Section 7), and we discuss challenges and interesting future directions (Section 6).Related Reviews and other Resources. There have been short reviews summarizing the progress and the vision of the research area, from both software engineering [52] and programming languages perspectives [28,195]. However, none of these articles can be considered extensive literature reviews, which is the purpose of this work. Ernst [57] discusses promising areas of applying natural language processing to software development, including error messages, variable names, code comments, and user questions. Some resources, datasets and code can be found at http://learnbigcode.github.io/. An online version of the work reviewed here -which we will keep up-to-date by accepting external contributions -can be found at https://ml4code.github.io. THE NATURALNESS HYPOTHESISMany aspects of code, such as names, formatting, the lexical order of methods, etc. have no impact on program semantics. This is precisely why we abstract them in most program analyses. But then, why should statistical properties of code matter at all? To explain this, we recently suggested a hypothesis, called the naturalness hypothesis. The inspiration for the naturalness hypothesis can be traced back to the "literate programming" concept of D. Knuth, which draws from the insight that programming is a form of human communication: "Let us change our traditional attitude to the construction of programs: Instead of imagining that our main task is to instruct a computer what to do, let us concentrate rather on explaining to human beings what we want a computer to do... " [105] The naturalness hypothesis, then, holds thatThe naturalness hypothesis. Software is a form of human communication; software corpora have similar statistical properties to natural language corpora; and these properties can be exploited to build better software engineering tools.The exploitation of the statistics of human communication is a mature and effective technology, with numerous applications ...

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Section: Introductionmentioning

confidence: 99%

A Survey of Machine Learning for Big Code and Naturalness

et al. 2018

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“…One type of verification tools can directly verify C programs. For example, CBMC [21,22] and its extensions such as ESBMC [23] verify C programs by using bounded model checking. They support the standard ANSI-C programming language with several extensions including some dialects for embedded systems.…”

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confidence: 99%

Model checking aircraft controller software: a case study

Chen

Huang

et al. 2013

Softw Pract Exp

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SummaryThis paper documents an application of model checking to formally verify an interrupt‐driven Slats and Flaps Control Unit software programmed in C, one component of a certain type of Chinese aircraft. Our objective was to identify errors rather than to prove correctness. We focused on the correctness of the algorithms used in the buffer operations, which are very common and important in aircraft software. In the verification, a total of four flawed code fragments was identified, including a minor efficiency issue. According to the programming team, this is regarded as a very successful result, and this project is the first successful attempt to apply model checking to the practice of verifying onboard aircraft software in China. Thanks to its completeness and reality, this case study can also serve as a complete and valuable real‐world example for teaching and learning model checking. Copyright © 2013 John Wiley & Sons, Ltd.

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“…The absence of memory holes can be verified by asserting that the active bits are false at program termination. We provide the full details in [6].…”

Section: Dynamic Memory Allocationmentioning

confidence: 99%

“…Due to space limitations, we are not able to present this here. The interested reader may refer to [6].…”

Section: Introductionmentioning

confidence: 99%

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