Future directions in programming languages

This paper describes the implementation of recursive algorithms in unique factorization domains, namely multivariate polynomial greatest common divisors (gcd) and factorization into irreducible parts in the Java computer algebra library (JAS). The implementation of gcds, resultants and factorization is part of the essential building blocks for any computation in algebraic geometry, in particular in automated deduction in geometry. There are various implementations of these algorithms in procedural programming languages. Our aim is an implementation in a modern object oriented programming language with generic data types, as it is provided by Java programming language. We exemplify that the type design and implementation of JAS is suitable for the implementation of several greatest common divisor algorithms and factorization of multivariate polynomials. Due to the design we can employ this package in very general settings not commonly seen in other computer algebra systems. As for example, in the coefficient arithmetic for advanced Grobner basis computations like in polynomial rings over rational function fields or (finite, commutative) regular rings. The new package provides factory methods for the selection of one of the several implementations for non experts. Further we introduce a parallel proxy for gcd implementations which runs different implementations concurrently.

Research that connects the effects of urbanization on biodiversity and ecosystem services is lacking. Ants perform multifarious ecological functions that stabilize ecosystems and contribute to a number of ecosystem services. We studied responses of ant communities to urbanization in the Lake Tahoe basin by sampling sites along a gradient of urban land development. We sampled ant communities, measured vegetation characteristics, quantified human activities, and evaluated ant-community responses by grouping ants into service-providing units (SPUs), defined as a group of organisms and their populations that perform specific ecosystem services, to provide an understanding of urbanization impacts on biodiversity and their delivery of ecosystem services. Species richness and abundance peaked at intermediate levels of urban development, as did the richness of 3 types of ant SPUs (aerators, decomposers, and compilers). With increasing land development aerator and decomposer ants significantly declined in abundance, whereas compiler ants significantly increased in abundance. Competing models demonstrated that precipitation was frequently among the strongest influences on ant community structure; however, urban development and human activities also had a strong, negative influence on ants, appearing in most models with DeltaAIC(c) < 2 for species richness and abundance patterns of SPUs and generalists. Response diversity was observed within SPUs, which suggests that the corresponding ecosystem services were maintained until development reached 30-40%. Our data provide evidence that ecosystem functions, such as water infiltration and soil productivity, may be diminished at sites subject to greater levels of urbanization and that conserving ant communities and the ecosystem services they provide could be an important target in land-use planning and conservation efforts.

Research shows that errors in natural language can be corrected by translating texts to another language and back using language models. We explore to what extent this latent correction capability extends to Automated Program Repair (APR) by investigating Round-Trip Translation (RTT): translating code from one programming language into another programming or natural language and back, using Large Language Models (LLMs). We hypothesize that RTT restores patterns most commonly seen in the LLM’s training corpora through regression toward the mean , replacing infrequent bugs with more frequent, natural , bug-free code. To test this hypothesis, we employ nine LLMs and four common APR benchmarks in Java, and perform a detailed quantitative and qualitative analysis of RTT-generated patches. We find that RTT through English generates plausible patches for 100 of 164 bugs with GPT-4 on the HumanEval-Java benchmark, and 97 are found to be correct in our manual assessment. Moreover, RTT uniquely generates plausible patches for 46 bugs that were missed by LLMs specifically fine-tuned for APR. While this demonstrates the viability of RTT for APR, we also observe limitations, such as a lower overall bug fix rate than the state-of-the-art and diluting the original coding style. We analyze the impact of these limitations and discuss the potential of using RTT as a complementary component in APR frameworks.

A search of the web reveals that natural language is considered to be language which a human would consider natural and unnatural language is language a human would consider artificial. This clearly raises interpretation questions and plenty of leeway so I am going to take the latter view here and bend it somewhat to consider programming languages as particularly good examples of unnatural language.Programming languages are something like 60 years old. Their evolution tells us much about the triumph of creativity over parsimony; the need for diversity and the frequent triumph of politics over common sense or logic. Yet their minimum set of features is defined by the beauty and simplicity of the Böhm-Jacopini theorem [1] and their underlying similarity in information theoretic terms is shown in the theorem proved by myself in [3],[4].This essay is a reflection on programming languages both old and new, engineering, systems evolution and the role of education.KeywordsProgramming LanguageUnnatural LanguageDisparate SystemUnderlying SimilarityStandard PageThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The complexity and importance of learning programming fundamentals (i.e., sequences of sentences that express actions, conditions, and repetitions in computing) for undergraduate students has motivated the development of an intense educational research area. One frequently studied problem is the difficulty in the learning of traditional context-free grammars which are present, for example, in programming languages such as Pascal and C. This study experimentally investigates the use of natural language in the learning of programming fundamentals by two groups of undergraduate students without prior knowledge of programming and compares its use with that of a traditional grammar language. Results suggest that the use of natural language is a good alternative, despite the small differences, to the use of traditional programming languages defined by context-free grammars. This alternative is attractive and promising because the student does not need to learn a formal grammar to learn the fundamentals of programming.

Code retrieval is to find the code snippet from a large corpus of source code repositories that highly matches the query of natural language description. Recent work mainly uses natural language processing techniques to process both query texts (i.e., human natural language) and code snippets (i.e., machine programming language), however, neglecting the deep structured features of query texts and source codes, both of which contain rich semantic information. In this article, we propose an end-to-end deep graph matching and searching (DGMS) model based on graph neural networks for the task of semantic code retrieval. To this end, we first represent both natural language query texts and programming language code snippets with the unified graph-structured data, and then use the proposed graph matching and searching model to retrieve the best matching code snippet. In particular, DGMS not only captures more structural information for individual query texts or code snippets, but also learns the fine-grained similarity between them by cross-attention based semantic matching operations. We evaluate the proposed DGMS model on two public code retrieval datasets with two representative programming languages (i.e., Java and Python). Experiment results demonstrate that DGMS significantly outperforms state-of-the-art baseline models by a large margin on both datasets. Moreover, our extensive ablation studies systematically investigate and illustrate the impact of each part of DGMS.

It is well known that students face many difficulties when they have to learn programming. Generally, these difficulties arise from two main reasons: i) the kind of exercises proposed by the teacher, and ii) the programming language used for solving those problems. The first problem is overcome by selecting an interesting application domain for the students. The second problem is tackled by using programming languages specialized for teaching. Nowadays, there are many programming languages aimed at simplifying the learning process. However, many of them still have the same drawbacks of traditional programming languages: the language used to write the statements is different from the programmers' native language; and the syntactic rules impose many tricky restrictions not easy to follow. This paper presents an approach for solving the problems previously mentioned. The approach consists of using: an application domain motivating for the student, the Project Hoshimi (PH); and a programming environment, PH-Helper that is a simple and user-friendly syntax-directed editor and compiler for Hoshimi Language (HL), the actual PH programming language.

Programming languages such as C, QuickBASIC and Programmable Logic Controller (PLC) ladder logic have been part of Engineering Technology curriculums for years. The increasing availability of powerful and low-cost computing hardware, graphical user interface based multitasking operating systems, visual programming languages, and new programming standards have provided the environment for the implementation of Rapid Application Development (RAD) programming techniques. Examples of these techniques such as menu, text, graphical, and icon based programming are presented. Their widespread use for mainstream industrial control and data acquisition applications have resulted in a migration away from the use of traditional programming languages. The implications of this migration for the content of Engineering Technology curriculums are explored.

Translation between programming languages and natural languages is an important solution to improve the understanding of coding in informatics students who often have difficulty understanding the syntax rules of programming languages. This research develops a translator application that is integrated with OCR technology and uses GPT Chat API to automatically translate text. This application supports seven programming languages (Python, Kotlin, CSS, Dart, HTML, Javascript, and Java) and two natural languages (Indonesian and English). This research uses the Waterfall software development method, starting from requirements analysis, system design, implementation, to testing using the blackbox method. The results show that this application can help users understand coding more effectively and efficiently through translation features, interactive quizzes, and user activity history. Thus, this application has the potential to be an innovative learning media that improves the programming skills of informatics students.

Programming language, natural language? Supporting the diverse computational activities of novice programmers

Code corpora, as observed in large software systems, are now known to be far more repetitive and predictable than natural language corpora. But why? Does the difference simply arise from the syntactic limitations of programming languages? Or does it arise from the differences in authoring decisions made by the writers of these natural and programming language texts? We conjecture that the differences are not entirely due to syntax, but also from the fact that reading and writing code is un-natural for humans, and requires substantial mental effort; so, people prefer to write code in ways that are familiar to both reader and writer. To support this argument, we present results from two sets of studies: 1) a first set aimed at attenuating the effects of syntax, and 2) a second, aimed at measuring repetitiveness of text written in other settings (e.g. second language, technical/specialized jargon), which are also effortful to write. We find that this repetition in source code is not entirely the result of grammar constraints, and thus some repetition must result from human choice. While the evidence we find of similar repetitive behavior in technical and learner corpora does not conclusively show that such language is used by humans to mitigate difficulty, it is consistent with that theory. This discovery of “non-syntactic” repetitive behaviour is actionable, and can be leveraged for statistically significant improvements on the code suggestion task. We discuss this finding, and other future implications on practice, and for research.

The first international Workshop on Programming Language Processing presents interdisciplinary contributions that address programming language procession problems with machine learning and data mining techniques. Recently, there are lots of successful natural language processing methods. But the mining of programming languages could not exactly follow the manner of natural language processing. The difference between natural language and programming language brings in new research challenges and opportunities. The workshop will bring together researchers from machine learning, data mining and software engineering to discuss and debate the path forward for mining the value of programming languages.

The object of the study is the procedures for automated design and analysis of digital signal processing algorithms on the SoC technology platform. The subject of the study is models, methods and procedures for designing and optimal selection of SoC components for the implementation of digital signal processing algorithms for audio spectrum. The aim of the study is to develop models and procedures for determining the possibilities of a compromise distribution of signal processing algorithm computations in the cycle of computer-aided design on the SoC technology platform in terms of performance and the feasibility of using hardware and software algorithms realization. The article solves the following tasks: consideration of the procedures for interacting the processor core with programmable logic as part of system-on-chip systems; development of procedures for computer-aided design and analysis of signal processing systems using programming languages and hardware description languages for the implementation of embedded systems. The following methods are being used: implementation of digital signal processing algorithms in the C programming language and high-level synthesis tools for realizing IP blocks, diagnostic experiment by generating test signal patterns, and analysis of the processing results at the system output. The results achieved. Based on the analysis of the procedures for the interaction of the processor core and programmable logic on the selected SoC platform, a model of the audio spectrum signal processing system is designed. The practical implementation was performed based on the Vivado/Vitis/Vitis HLS CAD tool stack. The proposed model was verified using a programmable test signal generator and analyzing the obtained characteristics of digital filters at the system output. Conclusions. The article analyzes the principles of designing embedded information processing systems implemented in system-on-chip. The principles of building and analyzing digital signal processing systems based on system-on-chip containing programmable logic and processor parts are considered. The developed methods have been tested on the algorithms of CIC and FIR filters on the technological platform of SoC FPGA of the ZYNQ-7000 family of Xilinx company.

This paper deals with the implementation of algorithms and tools for the security of academic data protection in the Democratic Republic of the Congo. It consists principally in implementing two algorithms and two distinct tools to secure data and in this particular case, academic data of higher and university education in the Democratic Republic of the Congo. The design of algorithms meets the approach that any researcher in data encryption must use during the development of a computer system. Briefly, these algorithms are steps to follow to encrypt information in any programming language. These algorithms are based on symmetric and asymmetric encryption, the first one uses Christopher Hill’s algorithm, which uses texts in the form of matrices before they are encrypted and RSA as one of the asymmetric algorithms, it uses the prime numbers that we have encoded on more than 512 bits. As for tools, we have developed them in php which is only a programming language taken as an example because it is impossible to use all of them. The tools implemented are based on the algorithms of Caesar, Christopher Hill, and RSA showing how the encryption operations are carried out thanks to graphical interfaces. They are only tools for pedagogical reasons to help students and other researchers learn how to use developed algorithms. We have not developed them for pleasure but rather to be used in any information system, which would prevent and limit unauthorized access to computer systems. They will not be used only for the management of academic fees but for any other information system, which explains and shows the complexity of the tools developed. We have not been able to solve the problems of versions for the developed prototype, because if there is a new version later some functions may be obsolete, which would constitute the limitation of these tools. This work targets primarily the Ministry of Higher Education and Universities, which will make these results its own and implement them in order to solve the problem of intrusions, and unauthorized access to developers and researchers who will use tools already made instead of thinking about their development. We are trying to demonstrate the steps and the methodology that allowed us to reach our results, in the following lines.

Teaching People to program is a crucial requirement for our society to deal with the complexity of 21st-century challenges. In many teaching systems, the student is required to use a particular programming language or development environment. This paper presents an intelligent tutoring system to support blended learning scenarios, where the students can choose their programming language and development environment. For that, the system provides an interface where the students request test data and submit results to unit test their algorithms. The submitted results are analyzed by a machine learning system that detects common errors and provides adaptive feedback to the student. With this system, we are focusing on teaching algorithms rather than specific programming language semantics. The technical evaluation tested with the implementation of Mean and Median algorithm shows that the system can distinguish between error cases with an error rate under 20%. A first survey, with a small group of students, shows that the system helps them detect common errors and arrive at a correct/valid solution. We are in the process of testing the system with a larger group of students for gathering statistically reliable data.