Multiple Programming Languages Research Articles

T5APR: Empowering automated program repair across languages through checkpoint ensemble

Automated program repair (APR) using deep learning techniques has become an important area of research in recent years, aiming to automatically generate bug-fixing patches that can improve software reliability and maintainability. However, most existing methods either target a single language or require high computational resources to train multilingual models. In this paper, we propose T5APR, a novel neural program repair approach that provides a unified solution for bug fixing across multiple programming languages. T5APR leverages CodeT5, a powerful pre-trained text-to-text transformer model, and adopts a checkpoint ensemble strategy to improve patch recommendation. We conduct comprehensive evaluations on six well-known benchmarks in four programming languages (Java, Python, C, JavaScript), demonstrating T5APR’s competitiveness against state-of-the-art techniques. T5APR correctly fixes 1,985 bugs, including 1,442 bugs that none of the compared techniques has fixed. We further support the effectiveness of our approach by conducting detailed analyses, such as comparing the correct patch ranking among different techniques. The findings of this study demonstrate the potential of T5APR for use in real-world applications and highlight the importance of multilingual approaches in the field of APR.

Shaoxia: a web-based interactive analysis platform for single cell RNA sequencing data

BackgroundIn recent years, Single-cell RNA sequencing (scRNA-seq) is increasingly accessible to researchers of many fields. However, interpreting its data demands proficiency in multiple programming languages and bioinformatic skills, which limited researchers, without such expertise, exploring information from scRNA-seq data. Therefore, there is a tremendous need to develop easy-to-use software, covering all the aspects of scRNA-seq data analysis.ResultsWe proposed a clear analysis framework for scRNA-seq data, which emphasized the fundamental and crucial roles of cell identity annotation, abstracting the analysis process into three stages: upstream analysis, cell annotation and downstream analysis. The framework can equip researchers with a comprehensive understanding of the analysis procedure and facilitate effective data interpretation. Leveraging the developed framework, we engineered Shaoxia, an analysis platform designed to democratize scRNA-seq analysis by accelerating processing through high-performance computing capabilities and offering a user-friendly interface accessible even to wet-lab researchers without programming expertise.ConclusionShaoxia stands as a powerful and user-friendly open-source software for automated scRNA-seq analysis, offering comprehensive functionality for streamlined functional genomics studies. Shaoxia is freely accessible at http://www.shaoxia.cloud, and its source code is publicly available at https://github.com/WiedenWei/shaoxia.

Linear-Time Algorithms for Front-Door Adjustment in Causal Graphs

Causal effect estimation from observational data is a fundamental task in empirical sciences. It becomes particularly challenging when unobserved confounders are involved in a system. This paper focuses on front-door adjustment – a classic technique which, using observed mediators allows to identify causal effects even in the presence of unobserved confounding. While the statistical properties of the front-door estimation are quite well understood, its algorithmic aspects remained unexplored for a long time. In 2022, Jeong, Tian, and Bareinboim presented the first polynomial-time algorithm for finding sets satisfying the front-door criterion in a given directed acyclic graph (DAG), with an O(n³(n+m)) run time, where n denotes the number of variables and m the number of edges of the causal graph. In our work, we give the first linear-time, i.e., O(n+m), algorithm for this task, which thus reaches the asymptotically optimal time complexity. This result implies an O(n(n+m)) delay enumeration algorithm of all front-door adjustment sets, again improving previous work by a factor of n³. Moreover, we provide the first linear-time algorithm for finding a minimal front-door adjustment set. We offer implementations of our algorithms in multiple programming languages to facilitate practical usage and empirically validate their feasibility, even for large graphs.

Enhancing source code retrieval with joint Bi-LSTM-GNN architecture: A comparative study with ChatGPT-LLM

Retrieving relevant source code from large repositories is a significant and ongoing challenge in the field of software engineering, primarily due to the vast and ever-expanding amount of available code. Existing deep learning methods, although effective to some extent, exhibit limitations in capturing the intricate and complex structural information embedded within source code, which hinders their ability to provide highly accurate retrieval results. This study endeavors to tackle this prominent issue by introducing a novel and innovative approach known as the Joint Bi-directional LSTM and Graph Neural Networks (JBLG) model for source code retrieval. The central aim is to harness the combined strengths and capabilities of Bi-directional Long Short-Term Memory (LSTM) networks and Graph Neural Networks (GNNs) to significantly enhance the model’s capacity to capture and interpret the complex structural characteristics intrinsic to source code. The proposed JBLG model employs a unique fusion of Bi-directional LSTM, which excels in capturing sequential and temporal dependencies within code, and GNN, which is adept at modeling the intricate graph structure of the code. By leveraging this hybrid architecture, the model aims to provide a comprehensive and highly effective solution for source code retrieval tasks. To assess the efficacy of the JBLG model, extensive experiments are conducted, and the model’s performance is evaluated against well-established benchmarks, including LSTM, GNN, and ChatGPT, using two diverse datasets: CodeSearchNet and CosBench datasets. These evaluations span multiple programming languages, ensuring a comprehensive and robust assessment of the model’s capabilities. The experimental results indicate that the JBLG model consistently outperforms its counterparts, including Bi-LSTM, GNN, ChatGPT, and DGMS, across various evaluation metrics. the JBLG model showcases an exceptional ability to handle and extract the intricate structural information inherent in source code, resulting in significantly enhanced retrieval accuracy. The JBLG model emerges as a highly promising solution for real-world source code retrieval applications, with the potential to revolutionize the field. The success of this model underscores the importance of combining deep learning techniques like Bi-directional LSTM and GNNs for tackling complex software engineering challenges. Furthermore, future research directions could involve exploring advanced techniques such as attention mechanisms and extending the model’s applicability to other software engineering tasks like code summarization and code completion. The findings of this study are expected to have a lasting impact on the advancement of source code retrieval methodologies.

African American Students’ Academic and Web Programming Self-Efficacy, Learning Performance, and Perceptions towards Computer Programming in Web Design Courses

Computer programming has been included in computer literacy education in many countries in the last decade. This study examined the effects of gender and the prior programming experience of computer programming on academic and web programming self-efficacy and learning performance in the web design course among African American students, as well as their perceptions towards computer programming. This study’s 14-week web design course taught African American students multiple web programming languages, including HTML, CSS, and JavaScript, in order. A one-group pretest–posttest design was adopted in the experiment. The quantitative method was primarily used in data analysis. This study revealed that African American students’ academic and web programming self-efficacy significantly increased after the web design course. Most of the African American students’ perceptions of computer programming became positive after attending the web design course. This study also found that male African American students had a significantly higher level of web programming self-efficacy than female students before the web design course. Interestingly, this difference disappeared after the course. Additionally, both gender and prior experience in computer programming did not significantly affect students’ learning performance in the web design course. The findings of this study not only contribute to the understanding of the feasibility of teaching multiple programming languages in web programming courses for African American students, they also provide evidence of the positive influence of web programming on African American students’ perceptions of computer programming.

Real-Time Collaboration in Code Development: A Comprehensive Review of Code Jett

CodeJett is a pioneering collaborative real-time code editor that serves as a beacon of innovation in the field of software development. With the increasing prevalence of remote work and distributed development teams, the need for tools that facilitate seamless collaboration among developers is paramount. CodeJett rises to this challenge by offering an array of features, including real-time synchronization, syntax highlighting, version control integration, and a built-in chat system. This paper provides a comprehensive overview of CodeJett, highlighting its significance and the evolving landscape of collaborative development tools. In a world where software development transcends borders and time zones, CodeJett acts as a unifying force, enabling developers to work together harmoniously on coding projects. The real-time nature of the tool eliminates the need for manual synchronization, enhancing productivity and reducing the likelihood of errors. CodeJett's versatility extends to its support for multiple programming languages, making it accessible to a broad spectrum of developers. This abstract underscore the role of CodeJett in facilitating collaboration, real-time development, and efficient communication within the software development community. The paper also explores the technical challenges faced in the development of CodeJett and offers insights into its future potential as a transformative tool in an ever-changing software development landscape. With keywords such as collaboration, real-time synchronization, syntax highlighting, and version control, this abstract encapsulates the essence of CodeJett's importance and its enduring impact on the field of software development.

Exploring the Integration of Machine Learning Models in Programming Languages on GitHub: Impact on Compatibility to Address Them

GitHub repositories are often used for collaborative development, allowing multiple developers to work on the same codebase and contribute their changes. Each repository is typically associated with a specific project, and it can contain everything from code files to documentation, bug reports, and feature requests. Depending on the context, it can contain files, directories, other resources related to a project, and it is often associated with a particular programming language. By default, GitHub automatically detects the primary programming language used in a repository based on the file extensions and content within the repository. However, this detection is not true all the time; there are some potential issues to consider. One of these problems is that the detected language may not accurately reflect the actual programming languages used in the project, especially if the project utilizes multiple programming languages or has undergone language migrations. In this study, we apply an alternative technology to resolve problems with classifying the programming language of a GitHub repository by analysing file extensions and identifying all programming languages used in the project. We also determine the appropriate primary programming language for the repository. This paper investigates how this technology can address the issues surrounding GitHub’s automatic detection of a repository’s primary programming language and how it can provide information on all the programming languages used in a project.

Towards end-to-end deep RNN based networks to precisely regress of the lettuce plant height by single perspective sparse 3D point cloud

Nowadays, 3D point cloud is supposed to be the most direct and effective data form for studying plant morphology structure. However, automatic and high-throughput acquisition of accurate individual plant height traits from 3D point cloud remains an urgent challenging problem. Summarizing the related research results in recent years, the factors limiting its application mainly come from these aspects: (1) Many existing methods require spatial auxiliary information such as ground control points (GCP), digital terrain models (DTM) and digital surface models (DSM) to obtain accurate plant height; (2) For 3D point cloud data in different environments, specialized modeling and careful parameter fine-tuning are usually required; (3) Sometimes, the point cloud processing involves the combined utilization of multiple programming languages and software, which is difficult for system integration. Focusing on these challenges, firstly, we proposed a novel end-to-end deep Recurrent Neural Network (RNN) based regression network framework called DRN, which consists of three parts: point cloud feature extraction network, deep RNN and regression network. The convolution operations-based point cloud feature extraction network is function as filtering noise, outliers and redundant information; The deep RNN network with long and short-term memory (LSTM) ability is used to learn the relationships between the feature points on the high-dimensional feature sequence separated by a certain distance; regression network is used to regress the output from deep RNN to plant height value. Experiments results on the 3rd Greenhouse Growing Challenge datasets show that DRN can directly regress the plant height of a single plant effectively without manual operations and the participation of spatial auxiliary information with an R2 of 0.948 and a relative root mean square error (RRMSE) of 10.06% in four different varieties of lettuce at different growth period. After studying the influence of the weights of the x, y, z coordinate of the input 3D point cloud on the regression result, then, we design a Dimension Attention (DA) module at the front end of the feature extraction network to learning the characteristic coordinate weight for every input point cloud sample. The DRN network with a DA module is called D-DRN, experiment results indicate D-DRN tend to achieve better result (R2 =0.960 ; RRMSE=8.680%) than DRN. Considering the end-to-end-based DRN and D-DRN network capable of ease of integration and their considerable prediction accuracy on public datasets, we believe they has a certain complementary effect on the existing study methods of obtaining plant morphological structure phenotype by point cloud data.

GAST: A generic AST representation for language-independent source code analysis

Organizations use various programming languages to develop their systems. These aim to take advantage of the most appropriate features of each language for a given domain and require programmers to command different languages and also to face the growing complexity of software development and maintenance. So, they need tools to help them analyze programs to identify relationships between their internal elements, uncover patterns, and calculate quality metrics. However, most tools have limited support for parsing multiple programming languages and high acquisition costs. Therefore, there is a need for new methods to analyze code written in multiple programming languages. This article describes the design of a method to automatically transform the syntax of various programming languages into a universal language with a generic syntax. The function of the generic language is to encapsulate the specificities of each specific language, so that the analysis of programs is facilitated in a single programming syntax and not in multiple syntaxes. The advantage of this approach is that only one analysis engine is required, not multiple code analyzers, to study the programs.

Static Vulnerability Analysis Using Intermediate Representations: A Literature Review

Analysis (SA) in Cybersecurity is a practice aimed at detecting vulnerabilities within the source code of a program. Modern SA applications, though highly sophisticated, lack programming language agnostic generalization, instead requiring codebase specific implementations for each programming language. The manner in which SA is implemented today, though functional, requires significant man hours to develop and maintain, higher costs due to custom applications for each language, and creates inconsistencies in implementation from SA-tool to SA-tool. A source of programming language generalization occurs within compilers. During the compilation process, source code is converted into a grammatically consistent Intermediate Representation (IR) (e.g. LLVM-IR) before being converted to an output format. The grammatical consistencies provided by the IR theoretically allow the same program written in different languages to be analyzed using the same mechanism. By using the IRs of compiled programming languages as the codebase of SA practices, multiple programming languages can be encompassed by a single SA tool. To begin understanding the possibilities the combination of SA and IRs may reveal, this research presents the following outcomes: 1) a systematic literature search, 2) a literature review, and 3) the classification of existing work pertaining to SA practices using IRs. The results of the study indicate that generalized Static Analysis using the LLVM IR is already a common practice in all compilers, but that the extended use of the LLVM IR in Cybersecurity SA practices aimed at finding vulnerabilities in source code remains underdeveloped.

Privacy Property Graph: Towards Automated Privacy Threat Modeling via Static Graph-based Analysis

Privacy threat modeling should be done frequently throughout development and production to be able to quickly mitigate threats. Yet, it can also be a very time-consuming activity. In this paper, we use an enhanced code property graph to partly automate the privacy threat modeling process: It automatically generates a data flow diagram from source code which exhibits privacy properties of data flows, and which can be analyzed semi-automatically via queries. We provide a list of such reusable queries that can be used to detect various privacy threats. To enable this analysis, we integrate a taint-tracking mechanism into the graph using privacy-specific labels. Since no benchmark for such an approach exists, we also present a test suite for privacy threat implementations which comprises implementations for 22 privacy threats in multiple programming languages. We expect that our approach significantly reduces time consumption of threat modeling and show that it also has potential beyond the threat categories defined by LINDDUN, e.g. to detect privacy anti-patterns and verify compliance to privacy policies.

Declarative static analysis for multilingual programs using CodeQL

SummaryDeclarative static program analysis has become one of the widely‐used program analysis techniques. Declarative static analyzers perform three steps: creating databases of facts from program source code, evaluating rules to generate new facts, and running queries over facts to extract all information related to specific properties via query systems. Declarative static analyzers can easily target diverse programming languages by modifying only databases and rules for new languages. Because query systems are independent of programming languages, they are reusable for new languages. However, even when declarative analyzers support multiple programming languages they do not currently support the analysis of multilingual programs written in two or more programming languages. We propose a systematic methodology that extends a declarative static analyzer supporting multiple languages to support multilingual programs as well. The main idea is to reuse existing components of the analyzer. Our approach first generates a merged database of facts, consisting of multiple logical language spaces. It allows existing language‐specific rules to derive new facts for the corresponding language from the facts in the corresponding language space. Then, it defines language‐interoperation rules that handle the language interoperation semantics. Finally, it uses the same query system to get analysis results leveraging the language interoperation semantics. We develop a proof‐of‐concept declarative static analyzer for multilingual programs by extending CodeQL, which can track dataflows across language boundaries. Our evaluation shows that the analyzer successfully tracks dataflows across Java‐C and Python‐C language boundaries and detects genuine interoperation bugs in real‐world multilingual programs.

Could Tierless Languages Reduce IoT Development Grief?

Internet of Things (IoT) software is notoriously complex, conventionally comprising multiple tiers. Traditionally an IoT developer must use multiple programming languages and ensure that the components interoperate correctly. A novel alternative is to use a single tierless language with a compiler that generates the code for each component and ensures their correct interoperation. We report a systematic comparative evaluation of two tierless language technologies for IoT stacks: one for resource-rich sensor nodes (Clean with iTask) and one for resource-constrained sensor nodes (Clean with iTask and mTask). The evaluation is based on four implementations of a typical smart campus application: two tierless and two Python-based tiered. (1) We show that tierless languages have the potential to significantly reduce the development effort for IoT systems, requiring 70% less code than the tiered implementations. Careful analysis attributes this code reduction to reduced interoperation (e.g., two embedded domain-specific languages and one paradigm versus seven languages and two paradigms), automatically generated distributed communication, and powerful IoT programming abstractions. (2) We show that tierless languages have the potential to significantly improve the reliability of IoT systems, describing how Clean iTask/mTask maintains type safety, provides higher-order failure management, and simplifies maintainability. (3) We report the first comparison of a tierless IoT codebase for resource-rich sensor nodes with one for resource-constrained sensor nodes. The comparison shows that they have similar code size (within 7%), and functional structure. (4) We present the first comparison of two tierless IoT languages, one for resource-rich sensor nodes and the other for resource-constrained sensor nodes.

TeraChem protocol buffers (TCPB): Accelerating QM and QM/MM simulations with a client-server model.

The routine use of electronic structures in many chemical simulation applications calls for efficient and easy ways to access electronic structure programs. We describe how the graphics processing unit (GPU) accelerated electronic structure program TeraChem can be set up as an electronic structure server, to be easily accessed by third-party client programs. We exploit Google's protocol buffer framework for data serialization and communication. The client interface, called TeraChem protocol buffers (TCPB), has been designed for ease of use and compatibility with multiple programming languages, such as C++, Fortran, and Python. To demonstrate the ease of coupling third-party programs with electronic structures using TCPB, we have incorporated the TCPB client into Amber for quantum mechanics/molecular mechanics (QM/MM) simulations. The TCPB interface saves time with GPU initialization and I/O operations, achieving a speedup of more than 2× compared to a prior file-based implementation for a QM region with ∼250 basis functions. We demonstrate the practical application of TCPB by computing the free energy profile of p-hydroxybenzylidene-2,3-dimethylimidazolinone (p-HBDI-)-a model chromophore in green fluorescent proteins-on the first excited singlet state using Hamiltonian replica exchange for enhanced sampling. All calculations in this work have been performed with the non-commercial freely-available version of TeraChem, which is sufficient for many QM region sizes in common use.

LakeCoSTR: A tool to facilitate use of Landsat Collection 2 to estimate lake surface water temperatures

AbstractAlthough remote sensing of lake surface water temperature has been ongoing for decades and has led to important discoveries regarding the warming of lake surface temperature in some regions, the ability to access and use remote sensing data is still primarily limited to remote sensing experts. Effective use of remote sensing data involves technical skills in coding (often in multiple programming languages), application of appropriate atmospheric corrections, and integrating spatially heterogeneous remote sensing data with in situ data obtained at specific geographic locations. To improve access to remote sensing data and broaden the understanding of changes in lake surface water temperature over the past four decades, we created lakeCoSTR (lake Collection 2 Surface Temperature Retrieval), a user‐friendly, cloud‐based script that gives ecologists lacking specialized training in remote sensing the ability to access the Landsat Collection 2 temperature estimates for lakes with a surface area of at least 0.4 ha. Additionally, if in situ data are provided, a paired dataset can be created within the tool. To demonstrate lakeCoSTR, we retrieved surface water temperature data for a lake with a long monitoring history, Lake Sunapee, NH, USA, and compared long‐term surface temperature trends between data obtained via lakeCoSTR and in situ measurements. When compared with Landsat Collection 1 temperature estimates derived from a single‐channel algorithm, the Landsat Collection 2 data from lakeCoSTR required no calibration to in situ data. This suggests that lakeCoSTR can be used to document temporal trends in lakes and to reprocess analyses that relied on Collection 1 data. From 1983 until 2020, remotely sensed surface water temperature estimates at Lake Sunapee increased by 0.07–0.09°C year−1 during July and August, a trend that agreed with long‐term in situ record. As climate change continues to impact freshwater systems, a better understanding of long‐term temperature data will be vital. Remotely sensed data, like those acquired using lakeCoSTR, may provide a window into temperature trends for lakes.

Meclib: Dynamic and Interactive Figures in STACK Questions Made Easy

Abstract—Making and interpretation of drawings and schematics is an important skill to be developed in engineering education. The question type STACK in the Moodle learning management system combined with the interactive graphics library JSXGraph has a great potential for implementation of e-learning resources addressing these skills in formative and summative settings. The authoring process of such materials is complex due to multiple markup and programming languages like HTML, LaTeX, Maxima and JavaScript (JS) being involved. The Meclib concept mitigates this complexity and allows for efficient bulk production of material with consistent appearance and user experience. The core of Meclib is a set of pre-defined JSXGraph-based objects with an interface to Maxima, such that no problem specific JS code is required at all. The present paper outlines the basic ideas of the implementation and demonstrates the approach for some typical examples of different complexity. The focus is on applications in engineering mechanics, starting from static illustrations up to an editor for free body diagrams, with rich adaptive formative feedback.

From One Language to the Next: Applications of Analogical Transfer for Programming Education

The 1980s and 1990s saw a robust connection between computer science education and cognitive psychology as researchers worked to understand how students learn to program. More recently, academic disciplines such as science and engineering have begun drawing on cognitive psychology research and theories of learning to create instructional materials and teacher professional development materials based on theories of learning, to some success. In this paper, we follow a similar approach by highlighting common areas of interest between computer science education and cognitive psychology–specifically theories of analogical transfer–and discuss how cross-pollination of theoretical constructs between disciplines can support research on the teaching and learning of multiple programming languages. We will also discuss areas where computing education research can adapt the existing theories from cognitive psychology to develop domain-specific theories of knowledge transfer in computing and feed back into cognitive psychology research to inform larger debates about the nature of cognition and learning.

Development of a multi-use decision support system for scientific management and breeding of sheep

As the challenges of food insecurity and population explosion become more pressing, there is a dire need to revamp the existing breeding and animal management systems. This can be achieved by the introduction of technology for efficiency and the improvement of the genetic merit of animals. A fundamental requirement for animal breeding is the availability of accurate and reliable pedigreed data and tools facilitating sophisticated computations. Keeping this in view, Smart Sheep Breeder (SSB) was developed using the waterfall methodology and multiple programming languages. It is a multi-use online artificial intelligence (AI) based and internet of things (IoT) compatible decision support system (DSS). It is capable of automatic performance recording, farm data management, data mining, biometrical analysis, e-governance, and decision-making in sheep farms. A centralized database was also developed capable of ranking sheep across multiple farms based on genetic merit and effective dissemination of germplasm. The system in India is available as a web-based tool and android application which facilitates performance recording and generates customized reports on various aspects of sheep production. SSB uses artificial intelligence and biometrical genetic algorithms to calculate breeding values, and inbreeding coefficients, construct selection indices and generate pedigree, and history sheets as well as more than 40 types of custom-tailored animal and farm reports and graphs. The algorithms used were validated using on farms using farm data and also by comparison with established methods and software. Smart Sheep Breeder could thus prove to be indispensable for the present farming systems which could be used by sheep farm managers and breeders across India.

The design of expert system to determine the university majoring based on multiple intelligence using forward chaining method

An expert system is one of artificial intelligence that uses a computer to work like and as well as humans do. An expert system is a computer system that is designed so that it has the ability and reasoning like an expert. But, the expert system is not used for replacing an expert who in this problem im a psychologist as a consultant for students want to recognize the potential and their abilities. The expert system is only used as equipment and limited tools. There is a problem is how to design tools to help students recognize their potential and abilities, so that they can choose the right department in the University based on their potential and abilities. Expert system design for assign department in the University based Multiple Intelligence use visual basic programming language and Microsoft Office Access as database.

Inter-Twine-d: Combining Organic Chemistry Laboratory and Choose-Your-Own-Adventure Games

Remote delivery approaches to laboratory courses in the response to the COVID-19 pandemic have included a spectrum spanning passive options such as providing students with prerecorded videos of experiments to replacing in-person laboratory experiences with immersive virtual reality environments. While interactive activities that require students to make choices about experimental design or procedure, mimicking levels of inquiry present in in-person laboratory experiments, are preferred, creating custom activities of this type often require expensive equipment, a media production team, and knowledge of multiple programming languages. The open source, nonlinear storytelling platform, Twine, provides a free alternative that allows instructors to create custom interactive choose-your-own-adventure activities based on their existing laboratory curriculum and inclusive of text, images, and video clips. We used Twine to create a choose-your-own-adventure laboratory activity as the final experiment in a remote delivery format organic chemistry laboratory course that allowed students to obtain customized data based on their experimental choices. We expanded this work to create an entire course of Twine choose-your-own-adventure laboratory activities for a third term organic chemistry laboratory course.