Code Structure Research Articles

Generative Models for Source Code: Fine-Tuning Techniques for Structured Pattern Learning

This study addresses the problem of how to automatically generate source code that is not only functional, but also well-structured, readable, and maintainable. Existing generative models for source code often produce functional code, but they lack consistency in structure and adherence to coding standards, essential for integration into existing application development projects and long-term software maintenance. By training the model on specific code structures, including a dataset with Italian annotations, the proposed methodology ensures that the generated code is compliant with both the functional requirements and the pre-defined coding standards. The methodology proposed in this study applies transfer learning techniques on the DeepSeek Coder model, to refine pre-trained models to generate code that integrates additional structuring constraints. By training the model on specific code structures, including a dataset with Italian comments, the proposed methodology ensures that the generated code meets both functional requirements and coding structure. Experimental results, evaluated using the perplexity metric, demonstrate the effectiveness of the proposed approach, which impacts the goals of reducing errors, and ultimately improves software development quality.

Strengthening Resilience and Sustainability for Post-Disaster Recovery: A Comparative Law and Economics Analysis on Smart Mixes Between Mechanisms

This article advocates smart mixes of mechanisms of post-disaster recovery in terms of boosting resilience and sustainability through liability rules, government intervention, insurance, and their combinations. Liability rules can provide compensation to victims and, in theory, also reduce disaster risks, while they have their limits in practice. The government widely intervenes in disaster compensation in many countries, but it faces challenges in how it can intervene fairly and efficiently, whether through informal channels, such as ad hoc charity, or structured approaches, such as compensation funds. As governments may struggle to provide efficient and effective compensation ex post, insurance may offer proactive solutions with models of first-party catastrophe insurance and third-party liability insurance ex ante. Where market failure, liability failure, and government failure may all arise, a smart mix of mechanisms is often preferable from a law and economic perspective, rather than relying solely on one framework. However, upon examining empirical evidence regarding the effectiveness of these instruments in China, the UK, France, Germany, and Turkey, it becomes apparent that a mix of mechanisms is not always applied in an effective manner. Models of mandatory comprehensive disaster insurance, and Public–Private Partnership (PPP), which both receive government intervention as a last resort to the market and liability failures, can reach the goal of more effective compensation for disaster victims, risk prevention and resilience when faced with disaster recovery, and should therefore be substantially implemented beyond the current levels. To be clear, the proposed mix of solutions mainly focuses on legal and economic dimensions which are rather limited, ignoring, for example, building codes for structures and physical interventions.

CETASim: A numerical tool for beam collective effect study in storage rings

We developed a 6D multi-particle tracking program CETASim in C++ to simulate intensity-dependent effects in electron storage rings. The program can simulate the beam collective effects due to short-range/long-range wakefields for single/coupled-bunch instability studies. It also features the simulation of interactions among charged ions and the trains of electron bunches, including both fast ion and ion trapping effects. The bunch-by-bunch feedback is also included so that the user can simulate the damping of the unstable motion when its growth rate is faster than the radiation damping rate. The particle dynamics is based on the transfer maps from sector to sector, including the nonlinear effects of amplitude-dependent tune shift, high-order chromaticity, and second-order momentum compaction factor. Users can also introduce a skew quadrupole useful for emittance sharing and exchange studies. This paper describes the code structure, the physics models, and the algorithms used in CETASim. We also present the results of its application to the PETRA-IV storage ring.

Magnetic dissipation in short gamma-ray-burst jets

Aims. Short gamma-ray bursts originate when relativistic jets emerge from the remnants of binary neutron star (BNS) mergers, as observed in the first multi-messenger event GW170817–GRB 170817A, which coincided with a gravitational wave signal. Both the jet and the remnant are believed to be magnetized, and the presence of magnetic fields is known to influence the jet propagation across the surrounding post-merger environment. In the magnetic interplay between the jet and the environment itself, effects due to a finite plasma conductivity may be important, especially in the first phases of jet propagation. We aim to investigate these effects, from jet launching to its final breakout from the post-merger environment. Methods. We used the PLUTO numerical code to perform 2D axisymmetric and full 3D resistive relativistic magnetohydrodynamic (MHD) simulations, employing spherical coordinates with spatial radial stretching. We considered and compared different models for physical resistivity, which must be small but still dominating over the intrinsic numerical dissipation (which yields unwanted smearing of structures in any ideal MHD code). Stiff terms in the current density are treated with IMplicit-EXplicit Runge Kutta algorithms for time-stepping. A Synge-like gas (Taub equation of state) is also considered. All simulations were performed using an axisymmetric analytical model for both the jet propagation environment and the jet injection; we leave the case of jet propagation in a realistic environment (i.e., imported from an actual BNS merger simulation) to a future study. Results. As expected, no qualitative differences are detected due to the effect of a finite conductivity, but significant quantitative differences in the jet structure and induced turbulence are clearly seen in 2D axisymmetric simulations, and we also compare different resistivity models. We see the formation of regions with a resistive electric field parallel to the magnetic field, and nonthermal particle acceleration may be enhanced there. The level of dissipated Ohmic power is also dependent on the various recipes for resistivity. Most of the differences arise before the breakout from the inner environment, whereas once the jet enters the external weakly magnetized environment region, these differences are preserved during further propagation despite the lower grid refinement. Finally, we show and discuss the 3D evolution of the jet within the same environment in order to highlight the emergence of nonaxisymmetric features.

Qudit stabilizer codes, CFTs, and topological surfaces

We study general maps from the space of rational conformal field theories (CFTs) with a fixed chiral algebra and associated Chern-Simons (CS) theories to the space of qudit stabilizer codes with a fixed generalized Pauli group. We consider certain natural constraints on such a map and show that the map can be described as a graph homomorphism from an orbifold graph, which captures the orbifold structure of CFTs, to a code graph, which captures the structure of self-dual stabilizer codes. By studying explicit examples, we show that this graph homomorphism cannot always be a graph embedding. However, we construct a physically motivated map from universal orbifold subgraphs of CFTs to operators in a generalized Pauli group. We show that this map results in a self-dual stabilizer code if and only if the surface operators in the bulk CS theories corresponding to the CFTs in question are self-dual. For CFTs admitting a stabilizer code description, we show that the full Abelianized generalized Pauli group can be obtained from twisted sectors of certain 0-form symmetries of the CFT. Finally, we connect our construction with SymTFTs, and we argue that many equivalences between codes that arise in our setup correspond to equivalence classes of bulk topological surfaces under fusion with invertible surfaces. Published by the American Physical Society 2024

Enhancing Erasure Resilience in Reed-Solomon Codes: Theory and Applications in Data Storage Systems

Reed-Solomon (RS) codes are widely utilized in data storage and communication systems due to their robust error detection and correction capabilities. However, traditional RS codes encounter challenges in addressing the increasing data corruption rates demanded by modern storage systems. This paper aims to overcome these limitations by modifying and extending the structure of traditional RS codes, particularly enhancing their recovery capabilities in the face of significant data loss. We begin by reviewing the theoretical foundations of RS codes and existing extension methods and discussing emerging technologies integrated into RS codes. We then present detailed methodologies for RS code encoding, erasure recovery mechanisms, soft decision decoding, and interleaving coding techniques, followed by a series of experiments designed to test the proposed methods. The experimental results indicate that soft-decision decoding outperforms traditional hard-decision decoding under high signal-to-noise ratio conditions, albeit with increased computational complexity as the list of candidate codewords grows. Interleaved Reed-Solomon (IRS) coding offers improved performance under low signal-to-noise ratio conditions but may introduce additional system complexity due to the interleaving and deinterleaving processes. We conclude with a summary of the research findings, a discussion of the study’s limitations, and suggestions for future research directions.

Code Recommendation for Schema Evolution of Mimic Storage Systems

Schema evolution of mimic storage systems is a time-consuming and error-prone task due to the redundant development of heterogeneous executors. The ORM-based proxy requires an entire class to represent the structure of a data table. There lacks domain-specific code recommendation techniques to boost storage development. To address this issue, we design a novel type of code context, i.e. schema context, that combines features of code text, syntax and structure. Regarding the requirements of class-level granularity, we focus on behavior and attribute in code syntax, and use element position and structural metrics to mine the hidden relationships. Based on schema context and an existing inference mode, we propose SchemaRec to recommend ORM-related class for the database executors once one of them has been changed. We conduct experiments with 110 open-source projects, and the results show that SchemaRec obtains more accurate results than Lucene, DeepCS, QobCS and SEA in terms of Top-1, Top-10 and MRR accuracy due to the better ability of context representation. We also find that code syntax is the most important information because it involves behavior and attribute information of ORM-related classes.

Comparative Analysis of Hydrodynamic Loads on Bridge Piers: Assessing Standards through Numerical Modeling

Currently, there is an alarming difference in the estimated actions on piers resulting from flash-floods and tsunamis as prescribed by international Structural Design Codes. Previous studies have explored the interaction between surge waves and piers from a fluid-dynamics perspective, but there is no assessment about the relevance of the methods used in Structural Design to determine the hydrodynamic pressure resultants. To determine the degree of conservatism embedded in design, results from the different approaches are compared against validated numerical simulations. Three-dimensional numerical analyses were conducted using the OpenFOAM platform, employing the Large Eddy Simulation turbulence modeling approach to simulate the dam-break wave impacting the pier. The findings revealed that the maximum force was not necessarily caused by the stream’s first strike and could be sustained over time. The hydrodynamic pressure center consistently occurred at two-thirds of the stream depth, significantly impacting the overturning moments experienced by the pier. Identified inconsistencies between Codes underscore the need for revision of non-conservative approaches in predicting the design forces.

Realizing linear-polarization-decoupled quasi-perfect absorption through mirror-symmetric QR-code metasurfaces.

Metamaterial absorbers (MAs) with irregular structures facilitate the attainment of unique absorption properties, leveraging the extensive geometric freedom. However, a challenge arises from the fact that polarization-dependent spectra do not coincide with cross-polarization. In this study, we introduce a MA featuring a mirror-symmetric quick response (QR)-code structure to achieve linear-polarization-decoupled absorption characteristics. We employ a direct binary search algorithm to reverse design the MA structure by specifying absorptivity for eigen-polarization states. Moreover, the absorptivity for arbitrary polarizations can be predicted through linear superposition of the two eigen-polarization states, opening up avenues for investigating polarization-controlled metasurfaces and metamaterials.

Utilizing AspectJ for Defense Against Evasive Malware Attacks in Android System

Introduction: Mobile devices have become an integral part of our digital lives, facilitating various tasks and storing a treasure trove of sensitive information. However, as more people utilize mobile devices, sophisticated cyber threats are emerging to elude traditional security measures. Method: The use of evasion techniques by malicious actors presents a significant challenge to mobile security, necessitating creative solutions. In this work, we investigate the potential critical role that the aspect-oriented programming paradigm AspectJ can play in strengthening mobile security against evasion attempts. Evasion techniques cover a wide range of tactics, including runtime manipulation, code obfuscation, and unauthorized data access. Results: These tactics usually aim to bypass detection and avoid security measures. In order to address the aforementioned issues, this paper uses AspectJ to give developers a flexible and dynamic way to add aspects to their coding structures so they can monitor, intercept, and respond to evasive actions. It illustrates how AspectJ can improve mobile security and counteract the long-lasting risks that evasion techniques present in a dynamic threat landscape. Conclusion: Consequently, this work proposes a novel defense mechanism incorporating AspectJ into a significant paradigm of security against evasion with 91.33% accuracy and demonstrates the successful detection of evasive attacks.

Decoding didactic discourse strategies in digital posters

The research paper explores the critical role of didactic discourse in educational settings, with a specific focus on digital environments. It elucidates the fundamental elements of digital didactic discourse, encompassing clear communication, structured content organization, engaging features, and accessibility considerations. The paper also delineates the advantages of utilizing digital posters over traditional formats, highlighting their interactivity, dynamic content, and ability to reach a broader audience. A comprehensive analysis of a digital poster on parts of speech serves as a case study to showcase the efficacy of structured content, practical examples, color coding, progression, contextualization, hyperlinks, accessibility features, clarity, scalability, and overall effectiveness in creating an engaging and informative educational resource. The findings underscore the significance of harnessing digital technologies in didactic discourse to elevate learning experiences, foster better comprehension among learners, and cater to diverse learning styles and needs. This research contributes to the ongoing discourse on integrating technology into education to optimize pedagogical approaches and enhance educational outcomes.

MultiRegionFoam: A Unified Multiphysics Framework for Multi-Region Coupled Continuum-Physical Problems

Abstract This paper presents a unified framework, called multiRegionFoam, for solving multiphysics problems of the multi-region coupling type within OpenFOAM (FOAM-extend). It is intended to supersede the existing solver with the same name. The design of the new framework is modular, allowing users to assemble a multiphysics problem region-by-region and coupling conditions interface-by-interface. The present approach allows users to choose between deploying either monolithic or partitioned interface coupling for each individual transport equation. The formulation of boundary conditions is generalised in the sense that their implementation is based on the mathematical jump/transmission conditions in the most general form for tensors of any rank. The present contribution focuses on the underlying mathematical model for interface coupled multiphysics problems, as well as on the software design and resulting code structure that enable a flexible and modular approach. Finally, deployment for different multi-region coupling cases is demonstrated, including conjugate heat, multiphase flows and fuel-cells. Source code: multiRegionFoam v1.1 [1], repository https://bitbucket.org/hmarschall/multiregionfoam/. Article highlights A novel multiphysics framework, called multiRegionFoam, has been developed for solving multi-region coupled problems in OpenFOAM. The design of the framework allows for a modular multiphysics setup with freedom of choice on the coupling strategy (partitioned vs. monolithic). Extension of the general transport equation by interface conditions enables a unified coupling approach.

DLAP: A Deep Learning Augmented Large Language Model Prompting framework for software vulnerability detection

Software vulnerability detection is generally supported by automated static analysis tools, which have recently been reinforced by deep learning (DL) models. However, despite the superior performance of DL-based approaches over rule-based ones in research, applying DL approaches to software vulnerability detection in practice remains a challenge. This is due to the complex structure of source code, the black-box nature of DL, and the extensive domain knowledge required to understand and validate the black-box results for addressing tasks after detection. Conventional DL models are trained by specific projects and, hence, excel in identifying vulnerabilities in these projects but not in others. These models with poor performance in vulnerability detection would impact the downstream tasks such as location and repair. More importantly, these models do not provide explanations for developers to comprehend detection results. In contrast, Large Language Models (LLMs) with prompting techniques achieve stable performance across projects and provide explanations for results. However, using existing prompting techniques, the detection performance of LLMs is relatively low and cannot be used for real-world vulnerability detections. This paper contributes DLAP, a Deep Learning Augmented LLMs Prompting framework that combines the best of both DL models and LLMs to achieve exceptional vulnerability detection performance. Experimental evaluation results confirm that DLAP outperforms state-of-the-art prompting frameworks, including role-based prompts, auxiliary information prompts, chain-of-thought prompts, and in-context learning prompts, as well as fine-turning on multiple metrics.

A Suite of Foundation Models Captures the Contextual Interplay Between Codons.

In the canonical genetic code, many amino acids are assigned more than one codon. Work by us and others has shown that the choice of these synonymous codon is not random, and carries regulatory and functional consequences. Existing protein foundation models ignore this context-dependent role of coding sequence in shaping the protein landscape of the cell. To address this gap, we introduce cdsFM, a suite of codon-resolution large language models, including both EnCodon and DeCodon models, with up to 1B parameters. Pre-trained on 60 million protein-coding sequences from more than 5,000 species, our models effectively learn the relationship between codons and amino acids, recapitualing the overall structure of the genetic code. In addition to outperforming state-of-the-art genomic foundation models in a variety of zero-shot and few-shot learning tasks, the larger pre-trained models were superior in predicting the choice of synonymous codons. To systematically assess the impact of synonymous codon choices on protein expression and our models' ability to capture these effects, we generated a large dataset measuring overall and surface expression levels of three proteins as a function of changes in their synonymous codons. We showed that our EnCodon models could be readily fine-tuned to predict the contextual consequences of synonymous codon choices. Armed with this knowledge, we applied EnCodon to existing clinical datasets of synonymous variants, and we identified a large number of synonymous codons that are likely pathogenic, several of which we experimentally confirmed in a cell-based model. Together, our findings establish the cdsFM suite as a powerful tool for decoding the complex functional grammar underlying the choice of synonymous codons.

FSECAM: A contextual thematic approach for linking feature to multi-level software architectural components

Linking software features to code components is commonly performed during software development and maintenance, including to implement a feature, document code, design test cases, trace requirements, track changes, and support inspection of safety–critical software by government and other third parties. However, manually mapping features to code is error-prone and time consuming, even for developers familiar with a system. To overcome these challenges several studies proposed automated techniques to reduce human intervention when linking features to code components. Nonetheless, three challenges remain: (i) accuracy, (ii) cost, and (iii) explainability. Linking of irrelevant code snippets causes an extra burden of analyses. If the approach lacks explainability, then a tool is less useful for many crucial systems such as safety–critical software. Moreover, heavyweight techniques such as those that require generating execution traces of every scenario or require training deep-learning models are costly and limit small companies from integrating them into their development process.We propose a contextual thematic approach that extracts the most relevant theme properties of the feature/requirement to address the aforementioned challenges. Our experiments with two proprietary projects reveal significant enhancement of performance (precision and F1 scores are more than 50% in ideal cases) in linking features to three abstractions of code components, i.e., modules, classes, and methods. Our approach is also capable of linking commits to issues in a promising way. Contextual theme extraction enhances the subjective explainability which has not yet been solved with existing approaches. Moreover, we extract several critical characteristics of the feature documents and code structures that are important to consider in both manual and automated techniques. Finally, we present the FSECAM tool for linking features to code components, which can be immediately deployed within the development process and used without much effort and cost in linking code components and commits.Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board.

Semiclassical Nonadiabatic Molecular Dynamics Using Linearized Pair-Density Functional Theory.

Nonadiabatic molecular dynamics is an effective method for modeling nonradiative decay in electronically excited molecules. Its accuracy depends strongly on the quality of the potential energy surfaces, and its affordability for long direct-dynamic simulations with adequate ensemble averaging depends strongly on the cost of the required electronic structure calculations. Linearized pair-density functional theory (L-PDFT) is a recently developed post-self-consistent-field multireference method that can model potential energy surfaces with an accuracy similar to expensive multireference perturbation theories but at a computational cost similar to the underlying multiconfiguration self-consistent field method. Here, we integrate the SHARC dynamics and PySCF electronic structure code to utilize L-PDFT for electronically nonadiabatic calculations and use the combined programs to study the photoisomerization reaction of cis-azomethane. We show that L-PDFT is able to successfully simulate the photoisomerization without crashes, and it yields results similar to the more expensive extended multistate complete active space second-order perturbation theory. This shows that L-PDFT can model internal conversion, and it demonstrates its promise for broader photodynamics applications.

Quantum Codes as an Application of Constacyclic Codes

The main focus of this paper is to analyze the algebraic structure of constacyclic codes over the ring R=Fp+w1Fp+w2Fp+w22Fp+w1w2Fp+w1w22Fp, where w12−α2=0, w1w2=w2w1, w23−β2w2=0, and α,β∈Fp∖{0}, for a prime p. We begin by introducing a Gray map defined over R, which is associated with an invertible matrix. We demonstrate its advantages over the canonical Gray map through some examples. Finally, we create new and improved quantum codes from constacyclic codes over R using Calderbank–Shore–Steane (CSS) construction.

A new plastic global analysis approach for the fire design of steel and stainless steel structures

The new generation of structural codes for stainless steel allow carrying out global plastic analyses on certain types of stainless steel structures with plastic cross-sections at room temperature if the joints conforming the structure are classified as full-strength joints, a capability that has been long recognised for carbon steel structures. However, the requirements for the fire design of carbon and stainless steel structures in current and upcoming European design standards are still primarily based on the resistance of individual members, disregarding the redistribution of internal forces and strain hardening effects. Recent studies have proven that carbon and stainless steel frames with plastic cross-sections and full-strength joints are capable of redistributing internal forces at elevated temperatures, and failed describing global plastic collapse mechanisms under fire situation, suggesting that system effects are also relevant at elevated temperatures and could be taken into account for more efficient designs. On this basis, this paper develops an extensive numerical study focused on carbon and stainless steel frames under fire situation, and proposes a new methodology based on plastic global analysis to estimate the resistance of such structures under fire situation accounting for their redistribution capacity, improving the accuracy of the predicted fire time resistances significantly for the two materials.

Automating Comment Generation for Smart Contract from Bytecode

Recently, smart contracts have played a vital role in automatic financial and business transactions. To help end users without programming background to better understand the logic of smart contracts, previous studies have proposed models for automatically translating smart contract source code into their corresponding code summaries. However, in practice, only 13% of smart contracts deployed on the Ethereum blockchain are associated with source code. The practical usage of these existing tools is significantly restricted. Considering that bytecode is always necessary when deploying smart contracts, in this paper, we first introduce the task of automatically generating smart contract code summaries from bytecode. We propose a novel approach, named S mart BT ( Smart contract B ytecode T ranslator) for automatically translating smart contract bytecode into fine-grained natural language description directly. Two key challenges are posed for this task: structural code logic hidden in bytecode and the huge semantic gap between bytecode and natural language descriptions. To address the first challenge, we transform bytecode into CFG (Control-Flow Graph) to learn code structural and logic details. Regarding the second challenge, we introduce an information retrieval component to fetch similar comments for filling the semantic gap. Then the structural input and semantic input are used to build an attentional sequence-to-sequence neural network model. The copy mechanism is employed to copy rare words directly from similar comments and the coverage mechanism is employed to eliminate repetitive outputs. The automatic evaluation results show that SmartBT outperforms a set of baselines by a large margin, and the human evaluation results show the effectiveness and potential of SmartBT in producing meaningful and accurate comments for smart contract code from bytecode directly.

Optimised GRS coded cooperation: sliding relay selection design and improved joint decoding

ABSTRACT In this paper, the novel optimised generalised Reed-Solomon coded cooperative (OGRSCC) scheme is developed with a flexible selection of the code length and information dimension. In the OGRSCC scheme, two GRS codes with different dimensions are deployed at the source and relay, respectively, where the information symbols of the relay are dependent on those of the source. At the relay, different symbol selections may contribute to distinct joint codes at the destination. As a result, to achieve a maximum free distance (FD) and the optimised weight distribution of the final code, the global search method is first proposed to obtain the optimal selection pattern. However, as the code grows longer, its complexity rises dramatically. To make the selection more practical, the low-complexity sliding search method is then investigated to obtain the local optimal symbol selection pattern. Monte-Carlo simulated results indicate the OGRSCC scheme with two selection methods demonstrates almost identical performance. Furthermore, based on this special selection method and the unique algebraic structure of GRS code, the improved joint decoding algorithm is first proposed to boost the overall system performance. In addition, the theoretical upper bound is analysed, verifying the effectiveness of the proposed OGRSCC scheme. Numerical simulations reveal that the proposed OGRSCC scheme outperforms its corresponding non-cooperative scheme by a gain of over 3.2 dB and its existing comparable schemes by a margin of approximately more than 4 dB at high signal-to-noise ratio (SNR), respectively.