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Code generation with Roslyn
\"Learn how Roslyn's new code generation capability will let you write software that is more concise, runs faster, and is easier to maintain. You will learn from real-world business applications to create better software by letting the computer write its own code based on your business logic already defined in lookup tables. Code Generation with Rosyln is the first book to cover this new capability. You will learn how these techniques can be used to simplify systems integration so that if one system already defines business logic through lookup tables, you can integrate a new system and share business logic by allowing the new system to write its own business logic based on already existing table-based business logic. One of the many benefits you will discover is that Roslyn uses an innovative approach to compiler design, opening up the inner workings of the compiler process. You will learn how to see the syntax tree that Roslyn is building as it compiles your code. Additionally, you will learn to feed it your own syntax tree that you create on the fly.\"-- Provided by publisher
Book
Offline text-independent writer identification using a codebook with structural features
Understanding handwritten documents is a vital and challenging problem that attracts many researchers in the fields of forensic and authentication science. This paper presents an offline system for text-independent writer identification of handwritten documents. The system extracts a handwritten connected component contour, which in turn is divided into segments of specific length. The system utilizes the concept of a bag of features in the writer recognition domain and considers handwritten contour segments to extract two conceptually simple and effective structural features. These features are the contour point curve angle and the CONtour point CONcavity/CONvexity. The system uses the proposed features to train a k-means clustering algorithm to construct a codebook of size K . The method then uses occurrence histograms of the extracted features in the codebook to create a final feature vector for each handwritten document. The effectiveness of the proposed features is evaluated in the writer identification domain using two widely used classification methods: the nearest neighbor and the support vector machine techniques. The proposed writer identification is evaluated on two large and public datasets from different language domains, the Arabic KHATT and English IAM datasets. The experimental results show that the proposed system outperforms state-of-the-art methods on the IAM dataset and provides competitive results on the KHATT dataset with respect to the identification rate.
Journal Article
Enhancing robustness of AI offensive code generators via data augmentation
Since manually writing software exploits for
offensive security
is time-consuming and requires expert knowledge, AI-base code generators are an attractive solution to enhance security analysts’ productivity by automatically crafting exploits for security testing. However, the variability in the natural language and technical skills used to describe offensive code poses unique challenges to their robustness and applicability. In this work, we present a method to add perturbations to the code descriptions to create new inputs in natural language (NL) from well-intentioned developers that diverge from the original ones due to the use of new words or because they miss part of them. The goal is to analyze how and to what extent perturbations affect the performance of AI code generators in the context of offensive code. First, we show that perturbed descriptions preserve the semantics of the original, non-perturbed ones. Then, we use the method to assess the robustness of three state-of-the-art code generators against the newly perturbed inputs, showing that the performance of these AI-based solutions is highly affected by perturbations in the NL descriptions. To enhance their robustness, we use the method to perform data augmentation, i.e., to increase the variability and diversity of the NL descriptions in the training data, proving its effectiveness against both perturbed and non-perturbed code descriptions.
Journal Article
DeviceTalk: A No-Code Low-Code IoT Device Code Generation
The deployment of a client–server-based distributed intelligent system involves application development in both the network domain and the device domain. In the network domain, an application server (typically in the cloud) is deployed to execute the network applications. In the device domain, several Internet of Things (IoT) devices may be configured as, for example, wireless sensor networks (WSNs), and interact with each other through the application server. Developing the network and the device applications are tedious tasks that are the major costs for building a distributed intelligent system. To resolve this issue, a low-code or no-code (LCNC) approach has been purposed to automate code generation. As traditional LCNC solutions are highly generic, they tend to generate excess code and instructions, which will lack efficiency in terms of storage and processing. Fortunately, optimization of automated code generation can be achieved for IoT by taking advantage of the IoT characteristics. An IoT-based distributed intelligent system consists of the device domain (IoT devices) and the network domain (IoT server). The software of an IoT device in the device domain consists of the Device Application (DA) and the Sensor Application (SA). Most IoT LCNC approaches provide code generation in the network domain. Very few approaches automatically generate the DA code. To our knowledge, no approach supports the SA code generation. In this paper, we propose DeviceTalk, an LCNC environment for the DA and the SA code development. DeviceTalk automatically generates the code for IoT devices to speed up the software development in the device domain for a distributed intelligent system. We propose the DeviceTalk architecture, design and implementation of the code generation mechanism for the IoT devices. Then, we show how a developer can use the DeviceTalk Graphical User Interface (GUI) to exercise LCNC development of the device software.
Journal Article
Pseudocode Generation from Source Code Using the BART Model
In the software development process, more than one developer may work on developing the same program and bugs in the program may be fixed by a different developer; therefore, understanding the source code is an important issue. Pseudocode plays an important role in solving this problem, as it helps the developer to understand the source code. Recently, transformer-based pre-trained models achieved remarkable results in machine translation, which is similar to pseudocode generation. In this paper, we propose a novel automatic pseudocode generation from the source code based on a pre-trained Bidirectional and Auto-Regressive Transformer (BART) model. We fine-tuned two pre-trained BART models (i.e., large and base) using a dataset containing source code and its equivalent pseudocode. In addition, two benchmark datasets (i.e., Django and SPoC) were used to evaluate the proposed model. The proposed model based on the BART large model outperforms other state-of-the-art models in terms of BLEU measurement by 15% and 27% for Django and SPoC datasets, respectively.
Journal Article
On Quasi-Cyclic Codes of Index 3
Quasi-cyclic codes of index 3 over finite fields are studied. We give a classification of such codes. Their duals with respect to the Euclidean and Hermitian inner products are investigated. We give a characterization of self-orthogonal and dual-containing codes. A quasi-cyclic code of index 3 is generated by at most three elements. We describe conditions when such a code (or its dual) is generated by one element.
Journal Article
Application of domain-specific modeling in kinetography and bipedal humanoid robot control
The article presents a new approach in the development of software for bipedal humanoid robot controllers, based on the construction and application of graphic domain-specific languages (DSLs). The notations used to describe dance movements and gestures are typical examples of DSLs. With certain extensions, related to the description of foot topology, sensors and actuators, such DSLs are applicable for modeling dance movements that would be performed by a robot. The existing software development methodologies in robotics have a purely mechanistic approach to understanding and implementing robotic tasks. Such an approach in humanoid robotics complicates the understanding of the problem, as well as the specification and implementation of solutions. Our approach, which uses DSLs, adopts complex movements and gestures performed by the feet of dancers using professional dancers, people with above-average motor skills, as reference. We believe that the developed software can also be successfully applied to assistive robots that would help people with special needs whose mobility is significantly lower than average.
Journal Article
Implementation of B2a Code Generator of Beidou-3 Satellite Based on FPGA
In the satellite signal acquisition and tracking, the locally reproducible ranging code plays an important role. For the newly launched Beidou-3 satellite in China, a method for implementing the B2a signal ranging code generator in FPGA is proposed. Taking the B2a ranging code of Beidou-3 satellite as the research object, the design principle of the B2a ranging code generator is explained in detail, which includes the analysis of the key modules of the Weil code and the Gold code generator. The FPGA is modularly designed to be scalable The B2a ranging code generator module, through the reserved register initialization interface, can realize the ranging code of different PRN satellites, solving the complex problem of generating arbitrary satellite number ranging codes, for the design of Beidou No. 3 navigation receiver Certain reference significance.
Journal Article