DNA strand displacement (DSD) is an experimental technology based on DNA molecules, which has a wide range of prospects for application in the fields of molecular biology, nanotechnology and biomedicine. In this paper, classical conditioned reflexes and operant conditioning based on DSD are researched and applied to path planning of unmanned aerial vehicles (UAVs). Firstly, classical conditioned reflexes and four kinds of operant conditioning are studied. Secondly, the process of using the integral system principle to train dogs is modeled based on DSD. Thirdly, the combination of classical conditioned reflexes and operant conditioning is investigated. Finally, the application of conditioned reflexes in path planning of UAVs is proposed. All experiments in this paper are validated using Visual DSD software. The combination of DSD technology and conditioned reflexes provides a research idea for intelligent IoT systems.

Translating software between programming languages is a challenging task, for which automated techniques have been elusive and hard to scale up to larger programs. A key difficulty in cross-language translation is that one has to re-express the intended behavior of the source program into idiomatic constructs of a different target language. This task needs abstracting away from the source language-specific details, while keeping the overall functionality the same. In this work, we propose a novel and systematic approach for making such translation amenable to automation based on a framework we call program skeletons. A program skeleton retains the high- level structure of the source program by abstracting away and effectively summarizing lower-level concrete code fragments, which can be mechanically translated to the target programming language. A skeleton, by design, permits many different ways of filling in the concrete implementation for fragments, which can work in conjunction with existing data-driven code synthesizers. Most importantly, skeletons can conceptually enable sound decomposition, i.e., if each individual fragment is correctly translated, taken together with the mechanically translated skeleton, the final translated program is deemed to be correct as a whole. We present a prototype system called Skel embodying the idea of skeleton-based translation from Python to JavaScript. Our results show promising scalability compared to prior works. For 9 real-world Python programs, some with more than about 1 k lines of code, 95% of their code fragments can be automatically translated, while about 5% require manual effort. All the final translations are correct with respect to whole-program test suites.

Automated code translation between programming languages can greatly reduce the human effort needed in learning new languages or in migrating code. Recent neural machine translation models, such as Codex, have been shown to be effective on many code generation tasks including translation. However, code produced by neural translators often has semantic mistakes. These mistakes are difficult to eliminate from the neural translator itself because the translator is a black box, which is difficult to interpret or control compared to rule-based transpilers. We propose the first automated approach to pinpoint semantic mistakes in code obtained after neural code translation. Our techniques are implemented in a prototype tool called TransMap which translates Python to JavaScript, both of which are popular scripting languages. On our created micro-benchmarks of Python programs with 648 semantic mistakes in total, TransMap accurately pinpoints the correct location for a fix for 87.96%, often highlighting 1-2 lines for the user to inspect per mistake. We report on our experience in translating 5 Python libraries with up to 1k lines of code with TransMap. Our preliminary user study suggests that TransMap can reduce the time for fixing semantic mistakes by around 70% compared to using a standard IDE with debuggers.

Abstract The C standard libraries are basic function libraries standardized by the C language. Programmers usually refer to their API documentation provided by third-party websites. Unfortunately, these documents are not necessarily complete or accurate, especially for constraint sentences of API usage, which are called Security Specifications (SSs). SS issues can prevent programmers from following obligatory constraints, which results in API misuse vulnerabilities. Previous work studying SS issues could only find certain types of inaccurate SSs through checking the compliance between API usage and existing SSs. Therefore, we propose a novel approach SSeeker for quickly discovering missing and inaccurate SSs through the inconsistency of semantically similar SSs. More specifically, SSeeker first completes broken sentences and discovers SSs from them by judging their constraint sentiment. Then SSeeker puts semantically similar SSs from different sources into a group, which can be used to discover missing or inaccurate SSs. With the help of SSeeker, we investigated 4 popular online third-party C standard library documents, studied their conformity with the C99 standard, analyzed their APIs and SSs, and discovered 92 prototype issues, 15 web page issues, and 96 SS issues.

To use library APIs, a developer is supposed to follow guidance and respect some constraints, which we call integration assumptions (IAs). Violations of these assumptions can have serious consequences, introducing security-critical flaws such as use-after-free, NULL-dereference, and authentication errors. Analyzing a program for compliance with IAs involves significant effort and needs to be automated. A promising direction is to automatically recover IAs from a library document using Natural Language Processing (NLP) and then verify their consistency with the ways APIs are used in a program through code analysis. However, a practical solution along this line needs to overcome several key challenges, particularly the discovery of IAs from loosely formatted documents and interpretation of their informal descriptions to identify complicated constraints (e.g., data-/control-flow relations between different APIs).