Choice Of Programming Language Research Articles

The Two Powers: How Pascal and Python Shaped Programming Education

The choice of programming language for education is an intensely debated topic. On the one hand, the programming language is supposed to be relevant in that its organisation, structures, and paradigms adhere to current standards and best practices in industry and academia. On the other hand, the programming language is expected to be simple, that is, easy to use and, of course, easy to learn. This increasingly means that we need an introduction to concepts shaped by a long history without all the historic baggage. The flip side of these requirements of an educational programming language is its power to shape thought patterns, mental models, and best practices of the students, and thus of their future. Moreover, the aspect of simplicity may have the power to decide whether programming will become democraticed or remain the realm of a few highly paid specialists. From the many programming languages that have been used in programming education, few stand out for being as popular and widely used as Pascal and Python. Decades apart, both have been designed with simplicity and relevance in mind, and yet it seems they could not differ more in so many design choices, most notably typing. In this article we therefore consider the similarities and differences between Pascal and Python with emphasis on an educational perspective. We identify core features that might explain their suitability for education, compare their strengths and weaknesses, and discuss to what extent the premises behind their design still apply to the future of programming education.

COMPARING SCIENTIFIC COMPUTING ENVIRONMENTS FOR SIMULATING 2D NON-BUOYANT FLUID PARCEL TRAJECTORY UNDER INERTIAL OSCILLATION: A PRELIMINARY EDUCATIONAL STUDY

This study presents a preliminary numerical investigation of the two-dimensional trajectory of a non-buoyant fluid parcel subjected to inertial oscillations and abrupt external forcing events. The simulations were implemented using Python, GNU Octave, R, Julia, and Fortran open-source scientific computing environments. By running 1,000 iterations in each environment, we evaluated the computational performance of these languages in tackling this idealized problem. The results, visualized through static plots and animation, validate the numerical model's ability to represent the fundamental physics governing fluid motion. Statistical analysis using the Kruskal-Wallis test and Dunn's post-hoc test with Bonferroni correction revealed that Fortran exhibits significantly faster execution times than other environments. However, the choice of programming language should also consider factors such as coding expertise, library availability, and scalability requirements. This study focuses on the performance of scientific computing environments within each language rather than the languages themselves. The observed execution times should be interpreted in the context of the specific environments used, as they often leverage optimized libraries written in lower-level languages. Despite the limitations of this work, such as the simplified 2D model and the use of a single hardware configuration, this study provides valuable insights into selecting appropriate computational tools. It contributes to educational resources for teaching idealized fluid dynamics models. Future studies could explore more complex scenarios, a more comprehensive range of programming environments, and the impact of different numerical schemes and physical parameterizations.

Efficiency Divide: Comparative Analysis of Human & Neural Network Algorithm Development

The paper delves into a comparative analysis between human and artificial intelligence (AI) capabilities in algorithm development, with a specific focus on the challenges presented in the "Advent of Code." The research thoroughly investigates the performance of Generative Pre-trained Transformers (GPTs), such as ChatGPT and Bard, in solving intricate algorithmic problems and benchmarks these results against those achieved by human participants. A sizeable portion of the study is dedicated to understanding the nuances of prompt engineering in AI and how it affects the problem-solving process, alongside exploring the choice of programming languages used by both AI and humans. The methodology of the research is extensive, involving the participation of both AI models and human subjects, who vary in their levels of programming expertise. This approach allows for a comprehensive evaluation of the correctness and efficiency of solutions, along with the time taken to resolve the given problems. The results from this study reveal intriguing insights. While AI models like GPTs demonstrate an impressive speed in problem resolution, they often fall short in accuracy when compared to human problem-solvers, particularly in tasks demanding deeper contextual understanding and creative reasoning. Furthermore, the study delves into the impact of time constraints on the effectiveness of problem-solving strategies employed by both AI and humans. It finds that under strict time constraints, AI models can quickly generate solutions, but these solutions may lack the depth and accuracy found in those devised by human participants. This aspect of the research highlights the trade-off between speed and precision in AI-driven problem solving. The research extends its implications beyond mere performance comparison. It suggests the potential for a synergistic approach where the computational efficiency and rapid problem-solving abilities of AI can be effectively combined with the nuanced understanding and creative problem-solving skills inherent in humans. This hybrid approach could redefine the future landscape of programming and algorithm development. The study not only provides a critical analysis of the capabilities of AI in the realm of algorithmic problem-solving but also paves the way for future exploration into the collaborative dynamics of human-AI interaction in programming. It highlights the evolving role of AI in programming and underscores the importance of balancing AI’s computational prowess with human creativity and adaptability in solving complex, real-world problems.

An empirical examination of newcomer contribution costs in established OSS communities: a knowledge-based perspective

PurposeTo remain sustainable, open source software (OSS) projects must attract new members—or newcomers—who make contributions. In this paper, the authors develop a set of hypotheses based on the knowledge barriers framework that examines how OSS communities can encourage contributions from newcomers.Design/methodology/approachEmploying longitudinal data from the source code repositories of 232 OSS projects over a two-year period, the authors employ a Poisson-based mixed model to test how community characteristics, such as the main drivers of knowledge-based costs, relate to newcomers' contributions.FindingsThe results indicate that community characteristics, such as programming language choice, documentation effort and code structure instability, are the main drivers of knowledge-based contribution costs. The findings also suggest that managing these costs can result in more inclusive OSS communities, as evidenced by the number of contributing newcomers; the authors highlight the importance of maintaining documentation efforts for OSS communities.Originality/valueThis paper assumes that motivational factors are a necessary but insufficient condition for newcomer participation in OSS projects and that the cost to participation should be considered. Using the knowledge barriers framework, this paper identifies the main knowledge-based costs that hinder newcomer participation. To the best of the authors' knowledge, this is the first empirical study that does not limit data collection to a single hosting platform (e.g., SourceForge), which improves the generalizability of the findings.

Uloga i značaj programskih jezika u primeni ERP rešenja

ERP software solutions are important for every company because they significantly improve and simplify company operations through a system based on well-organized data and software modules. There are a large number of ERP solutions on the market in the form of commercial or opensource software. The paper focuses on the largest and most represented ERP software solutions, whose most significant features are presented in an overview. As these are software solutions that, by their nature, must be written in some programming language, the paper analyzes the programming languages that the presented ERP systems most use. The choice of programming language is very important when creating any new ERP solution because it often directs the system development and defines the framework of possible tasks that can be performed in it. Apart from the primary programming languages for each considered ERP system, other programming languages are also listed which they support at least in some of their segments. This approach in the paper can be useful to look at the existing situation in the field of relations between ERP software and programming languages, but also to provide a basis for the creation of some future ERP solutions viewed through the needs of the systems themselves and the capabilities of programming languages.

Synbit: synthesizing bidirectional programs using unidirectional sketches

We propose a technique for synthesizing bidirectional programs from the corresponding unidirectional code plus input/output examples. The core ideas are: (1) constructing a sketch using the given unidirectional program as a specification, and (2) filling the sketch in a modular fashion by exploiting the properties of bidirectional programs. These ideas are enabled by our choice of programming language, HOBiT, which is specifically designed to maintain the unidirectional program structure in bidirectional programming, and keep the parts that control bidirectional behavior modular. To evaluate our approach, we implemented it in a tool called Synbit and used it to generate bidirectional programs for intricate microbenchmarks, as well as for a few larger, more realistic problems. We also compared Synbit to a state-of-the-art unidirectional synthesis tool on the task of synthesizing backward computations. This is an extended version of the paper “Synbit: Synthesizing Bidirectional Programs using Unidirectional Sketches”, published at OOPSLA 2021. In addition to the OOPSLA’21 paper, this journal will contain additional formalization and detailed examples.

Использование сторонних библиотек при написании программ для обработки статистических данных

The technology of statistical data processing using software and the C# programming language is considered. Various approaches to the study of statistical data are considered, both with the help of special mathematical software tools such as Scilab, MATLAB, Octave and by writing their own programs using specialized mathematical libraries. An overview of the methods of mathematical processing of the results of the experiment is given. A comparative analysis of various libraries for performing statistical data processing for the C# programming language is performed. A comparative analysis of the choice of programming language is performed from the point of view of the use of application libraries, depending on the presence of mathematical libraries for statistical data processing. Software constructs are being built for the IL Numerics Ultimate VS library, which is an extension for Visual Studio and allows you to write and execute mathematical algorithms in a very fast and efficient way. Data interpolation and visualization functions are studied based on the construction of graphs of functions based on statistical data. The Microsoft Word XX library is considered. YY Object Library which allows you to process text, tabular and graphical data in order to place them and save them in MS Word.

Emergence and algorithmic information dynamics of systems and observers.

One of the challenges of defining emergence is that one observer’s prior knowledge may cause a phenomenon to present itself as emergent that to another observer appears reducible. By formalizing the act of observing as mutual perturbations between dynamical systems, we demonstrate that the emergence of algorithmic information does depend on the observer’s formal knowledge, while being robust vis-a-vis other subjective factors, particularly: the choice of programming language and method of measurement; errors or distortions during the observation; and the informational cost of processing. This is called observer-dependent emergence (ODE). In addition, we demonstrate that the unbounded and rapid increase of emergent algorithmic information implies asymptotically observer-independent emergence (AOIE). Unlike ODE, AOIE is a type of emergence for which emergent phenomena will be considered emergent no matter what formal theory an observer might bring to bear. We demonstrate the existence of an evolutionary model that displays the diachronic variant of AOIE and a network model that displays the holistic variant of AOIE. Our results show that, restricted to the context of finite discrete deterministic dynamical systems, computable systems and irreducible information content measures, AOIE is the strongest form of emergence that formal theories can attain.This article is part of the theme issue ‘Emergent phenomena in complex physical and socio-technical systems: from cells to societies’.

Analysis of programming languages used in solving energy problems

The use of Information and Communication Technology (ICT) in the energy sector is becoming increasingly common. From data collection, recording and dissemination of energy information to design, planning and management of energy systems, scientific computing skills and tools, amount them programming languages. Depending on the problem to be solved, the choice of programming language is often very decisive for achieving the expected results. This article presents an analysis of the state of art of programming languages used in the energy domain and propose a case study of the programming languages to be used depending on the problem to be solved renewable energies sector. Programming languages for scientific computing are divided into two main classes: General Purpose Languages (GPL) and Domain Specific Languages (DSL); knowing a GPL make one better at using DSL. The analysis carried out shows that in energy engineering, the most used GPLs are C++ and python. Likewise, the most prominent DSLs are Julia, R and Matlab. The most suitable language for scientific computing, data science, visualization, and high-performance computing problems is Python. However, Julia which is in full expansion is not to be overlooked as it is dedicated to energy.

An empirical study of developers’ discussions about security challenges of different programming languages

Given programming languages can provide different types and levels of security support, it is critically important to consider security aspects while selecting programming languages for developing software systems. Inadequate consideration of security in the choice of a programming language may lead to potential ramifications for secure development. Whilst theoretical analysis of the supposed security properties of different programming languages has been conducted, there has been relatively little effort to empirically explore the actual security challenges experienced by developers. We have performed a large-scale study of the security challenges of 15 programming languages by quantitatively and qualitatively analysing the developers’ discussions from Stack Overflow and GitHub. By leveraging topic modelling, we have derived a taxonomy of 18 major security challenges for 6 topic categories. We have also conducted comparative analysis to understand how the identified challenges vary regarding the different programming languages and data sources. Our findings suggest that the challenges and their characteristics differ substantially for different programming languages and data sources, i.e., Stack Overflow and GitHub. The findings provide evidence-based insights and understanding of security challenges related to different programming languages to software professionals (i.e., practitioners or researchers). The reported taxonomy of security challenges can assist both practitioners and researchers in better understanding and traversing the secure development landscape. This study highlights the importance of the choice of technology, e.g., programming language, in secure software engineering. Hence, the findings are expected to motivate practitioners to consider the potential impact of the choice of programming languages on software security.

A Beginner's Guide to the COBRA Toolbox.

COBRA toolbox is one of the most popular tools for systems biology analyses using genome-scale metabolic reconstructions. The toolbox permits the use of many constraint-based analytical methods for examining characteristics of metabolism in the biosystems ranging in complexity from single cells to microbial communities and ultimately multicellular organisms. The toolbox has a number of different variants that can be used depending on a user's choice ofprogramming language. Here, I provide a basic tutorial for beginners that plan to use the original MATLAB version of the toolbox.

Synbit: synthesizing bidirectional programs using unidirectional sketches

We propose a technique for synthesizing bidirectional programs from the corresponding unidirectional code plus a few input/output examples. The core ideas are: (1) constructing a sketch using the given unidirectional program as a specification, and (2) filling the sketch in a modular fashion by exploiting the properties of bidirectional programs. These ideas are enabled by our choice of programming language, HOBiT, which is specifically designed to maintain the unidirectional program structure in bidirectional programming, and keep the parts that control bidirectional behavior modular. To evaluate our approach, we implemented it in a tool called Synbit and used it to generate bidirectional programs for intricate microbenchmarks, as well as for a few larger, more realistic problems. We also compared Synbit to a state-of-the-art unidirectional synthesis tool on the task of synthesizing backward computations.

LỰA CHỌN NGÔN NGỮ LẬP TRÌNH TRONG DẠY HỌC Ở TRƯỜNG PHỔ THÔNG

There have been many research results and many criteria for choosing a programming language in teaching nowadays. This article reviews research related to the choice of programming languages in teaching programming in high schools, and then proposes the criteria for choosing a programming language with a teaching perspective in high schools, in the current period in Vietnam. Some survey results from trainers and teachers on the proposed criteria will also be presented in this article. Hopefully the article will contribute some information in choosing programming languages for IT teaching in high schools today, especially for grades 6 and 10, which are in a transition and standard stage being changed for the informatics teaching program according to the 2018 general education program.

PYTHON AS A BASIC LANGUAGE FOR TEACHING PROGRAMMING IN SCHOOL

The article deals with the issues related to the choice of programming language for initial training in programming at school. The choice of Python as the main programming language in the basic course of informatics in grades 7–9 is justified. The characteristics of Python whose allow to recommend it for initial programming training in school are described. The article also presents the results of a survey of informatics teachers who have been teaching programming on Python for several years at informatics lessons in grades 7–9.

On the Impact of Programming Languages on Code Quality

In a 2014 article, Ray, Posnett, Devanbu, and Filkov claimed to have uncovered a statistically significant association between 11 programming languages and software defects in 729 projects hosted on GitHub. Specifically, their work answered four research questions relating to software defects and programming languages. With data and code provided by the authors, the present article first attempts to conduct an experimental repetition of the original study. The repetition is only partially successful, due to missing code and issues with the classification of languages. The second part of this work focuses on their main claim, the association between bugs and languages, and performs a complete, independent reanalysis of the data and of the statistical modeling steps undertaken by Ray et al. in 2014. This reanalysis uncovers a number of serious flaws that reduce the number of languages with an association with defects down from 11 to only 4. Moreover, the practical effect size is exceedingly small. These results thus undermine the conclusions of the original study. Correcting the record is important, as many subsequent works have cited the 2014 article and have asserted, without evidence, a causal link between the choice of programming language for a given task and the number of software defects. Causation is not supported by the data at hand; and, in our opinion, even after fixing the methodological flaws we uncovered, too many unaccounted sources of bias remain to hope for a meaningful comparison of bug rates across languages.

RESEARCH TASKS AS A METHOD OF DEVELOPMENT OF PROGRAMMING SKILLS

The article reveals the relevance of the formation of research competence in high school and the first year of university in the study of informatics. It is proposed to achieve the goal while performing research tasks on programming. The structure of the study is described, a number of examples invariant to the choice of programming language is given. Performing tasks allows the student to formulate a generalized scheme for solving problems of a certain type. The use of research tasks in teaching practice contributes to the formation of the student’s holistic perception of programming language structures and corresponding data structures. As mastering the basic themes of the discipline, the student masters various mechanisms for solving problems. The proposed research tasks can expand the method of teaching informatics.

Open source software in quantum computing.

Open source software is becoming crucial in the design and testing of quantum algorithms. Many of the tools are backed by major commercial vendors with the goal to make it easier to develop quantum software: this mirrors how well-funded open machine learning frameworks enabled the development of complex models and their execution on equally complex hardware. We review a wide range of open source software for quantum computing, covering all stages of the quantum toolchain from quantum hardware interfaces through quantum compilers to implementations of quantum algorithms, as well as all quantum computing paradigms, including quantum annealing, and discrete and continuous-variable gate-model quantum computing. The evaluation of each project covers characteristics such as documentation, licence, the choice of programming language, compliance with norms of software engineering, and the culture of the project. We find that while the diversity of projects is mesmerizing, only a few attract external developers and even many commercially backed frameworks have shortcomings in software engineering. Based on these observations, we highlight the best practices that could foster a more active community around quantum computing software that welcomes newcomers to the field, but also ensures high-quality, well-documented code.

Discussion on Teaching Methods and Choice of Programming Language on Software Engineering Major

With the development of computer technology, a variety of programming languages, especially the rapid development of software engineering, the choice of programming language and the reform of teaching methods are also more urgent. In order to meet the need of employment, choose the right programming language, at the same time, the theory teaching and practice teaching should be combined effectively, the teaching should keep pace with the times, so as to cultivate the excellent programming personnel.

Computing the curriculum—Embedding computation in undergraduate acoustics

I will describe an approach to teaching acoustics to undergraduate engineering students that integrates mathematics, coding, numerical computation, and physical measurement. This approach developed and continues to develop according to my experience as Director of taught acoustical programmes at the Institute of Sound and Vibration Research (ISVR) in the Faculty of Engineering and the Environment at the University of Southampton, UK. Our program is somewhat unusual in that it teaches acoustics in depth to undergraduate students, whose understanding of the physics must develop in parallel with their proficiency in mathematical analysis and coding. I will explore how both numerical computation and coding can be integrated into an acoustics curriculum from the very start, how their inclusion can enhance learning, and how the curriculum can be restructured in order to make best use of these elements, highlighting the features of acoustics as a subject that make it particularly suited to this approach. I will also discuss how the choice of programming languages and environments, and modelling software affect the curriculum and vice versa. I will present examples from my teaching using Jupyter (formerly IPython) notebooks and discuss how to use these effectively.

Teaching Introductory Programming

Teaching programming to beginners is a complex task. In this article, the effects of three factors—choice of programming language, problem-solving training, and the use of formative assessment—on learning to program were investigated. The study adopted an iterative methodological approach carried out across 4 consecutive years. To evaluate the effects of each factor (implemented as a single change in each iteration) on students’ learning performance, the study used quantitative, objective metrics. The findings revealed that using a syntactically simple language (Python) instead of a more complex one (Java) facilitated students’ learning of programming concepts. Moreover, teaching problem solving before programming yielded significant improvements in student performance. These two factors were found to have variable effects on the acquisition of basic programming concepts. Finally, it was observed that effective formative feedback in the context of introductory programming depends on multiple parameters. The article discusses the implications of these findings, identifies avenues for further research, and argues for the importance of studies in computer science education anchored on sound research methodologies to produce generalizable results.