Programming Tasks Research Articles

Automated code development based on genetic programming in graphical programming language: A pilot study.

Continual technological advances associated with the recent automation revolution have tremendously increased the impact of computer technology in the industry. Software development and testing are time-consuming processes, and the current market faces a lack of specialized experts. Introducing automation to this field could, therefore, improve software engineers' common workflow and decrease the time to market. Even though many code-generating algorithms have been proposed in textual-based programming languages, to the best of the authors' knowledge, none of the studies deals with the implementation of such algorithms in graphical programming environments, especially LabVIEW. Due to this fact, the main goal of this study is to conduct a proof-of-concept for a requirement-based automated code-developing system within the graphical programming environment LabVIEW. The proposed framework was evaluated on four basic benchmark problems, encompassing a string model, a numeric model, a boolean model and a mixed-type problem model, which covers fundamental programming scenarios. In all tested cases, the algorithm demonstrated an ability to create satisfying functional and errorless solutions that met all user-defined requirements. Even though the generated programs were burdened with redundant objects and were much more complex compared to programmer-developed codes, this fact has no effect on the code's execution speed or accuracy. Based on the achieved results, we can conclude that this pilot study not only proved the feasibility and viability of the proposed concept, but also showed promising results in solving linear and binary programming tasks. Furthermore, the results revealed that with further research, this poorly explored field could become a powerful tool not only for application developers but also for non-programmers and low-skilled users.

Performance and programmability of GrPPI for parallel stream processing on multi-cores

GrPPI library aims to simplify the burdening task of parallel programming. It provides a unified, abstract, and generic layer while promising minimal overhead on performance. Although it supports stream parallelism, GrPPI lacks an evaluation regarding representative performance metrics for this domain, such as throughput and latency. This work evaluates GrPPI focused on parallel stream processing. We compare the throughput and latency performance, memory usage, and programmability of GrPPI against handwritten parallel code. For this, we use the benchmarking framework SPBench to build custom GrPPI benchmarks and benchmarks with handwritten parallel code using the same backends supported by GrPPI. The basis of the benchmarks is real applications, such as Lane Detection, Bzip2, Face Recognizer, and Ferret. Experiments show that while performance is often competitive with handwritten parallel code, the infeasibility of fine-tuning GrPPI is a crucial drawback for emerging applications. Despite this, programmability experiments estimate that GrPPI can potentially reduce the development time of parallel applications by about three times.

Time-dependant Bayesian knowledge tracing-Robots that model user skills over time.

Creating an accurate model of a user's skills is an essential task for Intelligent Tutoring Systems (ITS) and robotic tutoring systems. This allows the system to provide personalized help based on the user's knowledge state. Most user skill modeling systems have focused on simpler tasks such as arithmetic or multiple-choice questions, where the user's model is only updated upon task completion. These tasks have a single correct answer and they generate an unambiguous observation of the user's answer. This is not the case for more complex tasks such as programming or engineering tasks, where the user completing the task creates a succession of noisy user observations as they work on different parts of the task. We create an algorithm called Time-Dependant Bayesian Knowledge Tracing (TD-BKT) that tracks users' skills throughout these more complex tasks. We show in simulation that it has a more accurate model of the user's skills and, therefore, can select better teaching actions than previous algorithms. Lastly, we show that a robot can use TD-BKT to model a user and teach electronic circuit tasks to participants during a user study. Our results show that participants significantly improved their skills when modeled using TD-BKT.

AI-Assisted Programming Tasks Using Code Embeddings and Transformers

This review article provides an in-depth analysis of the growing field of AI-assisted programming tasks, specifically focusing on the use of code embeddings and transformers. With the increasing complexity and scale of software development, traditional programming methods are becoming more time-consuming and error-prone. As a result, researchers have turned to the application of artificial intelligence to assist with various programming tasks, including code completion, bug detection, and code summarization. The utilization of artificial intelligence for programming tasks has garnered significant attention in recent times, with numerous approaches adopting code embeddings or transformer technologies as their foundation. While these technologies are popular in this field today, a rigorous discussion, analysis, and comparison of their abilities to cover AI-assisted programming tasks is still lacking. This article discusses the role of code embeddings and transformers in enhancing the performance of AI-assisted programming tasks, highlighting their capabilities, limitations, and future potential in an attempt to outline a future roadmap for these specific technologies.

Aesthetic experience in technology education – the role of aesthetics for learning in lower secondary school robotic programming

IntroductionWithin the technology education research field, aesthetics has primarily been treated as either related to artifacts, design processes and innovation, or as related to students’ enjoyment, appreciation, and participation in technology and technology education. This study focuses on the role of aesthetics in technology learning more specifically the learning of programming. Previous research has pointed to aesthetics as important for the learning of programming, e.g., that programming activities in higher education typically involve experiences of frustration. While previous research is primarily based on student reports, there is a need for further exploration of processes of learning to program. The aim of this study is to explore the role of aesthetics for student learning to program in and what these processes may mean in relation to a disciplinary aesthetics of the technology subject.MethodsThe study was part of a design-based study with the overall purpose to develop the teaching of programming in lower secondary school. Data was collected from a programming task designed and implemented in school-year 9 (the students were aged 15–16) in Technology in two lower secondary classes. In total, three teachers participated in the implementation. The students pair-programmed Lego robots that should perform specific movements, such as following a curved line. Each group recorded their coding process along with audio, resulting in videos that documented the gradual evolution of their programs. These videos, capturing the real-time programming and associated student and teacher conversations, serve as the data for this study. In order to analyze the role of aesthetics in classroom conversations a Practical Epistemology Analysis was applied.ResultsThe results show that aesthetic judgments were important for orienting learning toward (1) the movement of the robot and (2) the ways to be in the programming activity. During the programming activity, the students expressed feelings of frustration but also joy and humor.DiscussionThe findings concur with previous research and contribute to further understanding the role of negative and positive aesthetic experiences in the teaching and learning of programming. The importance of the objects of aesthetic experience found in this study are discussed as part of a disciplinary aesthetic of programming.

Individual differences in computer programming: a systematic review

ABSTRACT The demand for programmers has grown exponentially in recent years, making programming an indispensable skill. However, the complex nature of programming poses various challenges for novice programmers, leading to high dropout rates in programming courses. The recognition of individual differences, encompassing distinct neurocognitive profiles, is acknowledged to influence the accuracy of predicting programming skill outcomes. As a result, an increasing awareness exists regarding the substantial contribution of human factors, including personality or cognitive ability, to programming performance. This study conducts a Systematic Literature Review (SLR) to explore recent research on individual differentiation or programmer profiling in programming tasks. The primary goal is to examine pertinent research exploring personal characteristics’ influence on programming in various contexts. As a result of this review, a taxonomy has been introduced to enhance the comprehension of programmers’ multifaceted individual differences, categorising influential factors into nine distinct dimensions. Furthermore, it has been found that individual differences influence performance in programming tasks, as well as in the learning of this discipline. In conclusion, this SLR emphasises human factors’ critical role in programming and proposes a taxonomy that is a valuable framework for researchers, educators, and practitioners to enhance their understanding of human factors’ influence on programming performance.

Design and Development of a Digital Personalized Learning Track: Bridging the Gap Between Textual and Visual Programming

Due to swift technological changes in society, programming tasks are proliferating in formal and informal education around the globe. However, challenges arise regarding the acquisition of programming skills. Many students are unequipped to develop programming skills due to limited instruction or background and therefore feel insecure when encountering programming in higher education. Some after-school initiatives focus on teaching younger students programming skills, however, not all students have the opportunity to attend. It can also be very challenging for teachers to teach programming—even more so due to significant differences in students’ knowledge and interests. To alleviate these challenges, we designed and developed a digital personalized learning (DPL) track for programming in the first grade of secondary education (12–14 year-old students) with a threefold purpose: (a) to encourage students bridging the gap between visual and more general-purpose textual programming languages (b) to meet differences in students’ programming knowledge by challenging them, albeit on their own pace, and subsequently (c) to support teachers in the delivery of programming education with relevant supportive learning materials. The design was tested by students and teachers, both of varying technical abilities. Assessments of the DPL-track were positive, with students identifying the tasks as challenging and the tools as motivating. Teachers praised the adaptivity, as well as the gradual transition from visual to textual programming. We present several suggestions for design improvement and dilemmas while reflecting on our design case.

Career Craft (A Student Training and Freelancing Platform)

As the world continues to rely on technology in all aspects of life, the demand for Information Technology graduates with sufficient background knowledge and necessary practical skills has never been higher. Computer science students are required to keep track of advancements and new trends in the field while maintaining good depth in present technologies. Unfortunately, many fresh graduates face significant challenges when entering the workforce, lacking practical experience and technical skills in the field. To mitigate this problem, students need more exposure to real-world experience and work in a job market environment. This paper presents the design and implementation of Career Craft, a platform that intends to enhance students’ programming and problem-solving skills by providing programming tasks that students compete to solve. Teachers develop and publish programing tasks and students solve them in order to gain points. A teacher is assigned as project manager and selects a group of students based on expertise and experience to develop the requested service. This exposes students to the real-world job environment and provides them with hands-on freelancing experience in these areas. By using the system, students can increase their employability by working as freelancers for clients. The system improves students' skills, increases their chances of employment, and increases their income, while helping clients obtain quality services.

Semantic Linear Genetic Programming for Symbolic Regression.

Symbolic regression (SR) is an important problem with many applications, such as automatic programming tasks and data mining. Genetic programming (GP) is a commonly used technique for SR. In the past decade, a branch of GP that utilizes the program behavior to guide the search, called semantic GP (SGP), has achieved great success in solving SR problems. However, existing SGP methods only focus on the tree-based chromosome representation and usually encounter the bloat issue and unsatisfactory generalization ability. To address these issues, we propose a new semantic linear GP (SLGP) algorithm. In SLGP, we design a new chromosome representation to encode the programs and semantic information in a linear fashion. To utilize the semantic information more effectively, we further propose a novel semantic genetic operator, namely, mutate-and-divide propagation, to recursively propagate the semantic error within the linear program. The empirical results show that the proposed method has better training and test errors than the state-of-the-art algorithms in solving SR problems and can achieve a much smaller program size.

Exploring Gender Bias In Remote Pair Programming Among Software Engineering Students: The twincode Original Study And First External Replication

ContextWomen have historically been underrepresented in Software Engineering, due in part to the stereotyped assumption that women are less technically competent than men. Pair programming is both widely used in industry and has been shown to increase student interest in Software Engineering, particularly among women; but if those same gender biases are also present in pair programming, its potential for attracting women to the field could be thwarted.ObjectiveWe aim to explore the effects of gender bias in pair programming. Specifically, in a remote setting in which students cannot directly observe the gender of their peers, we study whether the perception of the partner, the behavior during programming, or the style of communication of Software Engineering students differ depending on the perceived gender of their remote partner. To our knowledge, this is the first study specifically focusing on the impact of gender stereotypes and bias within pairs in pair programming.MethodWe have developed an online pair-programming platform (twincode) that provides a collaborative editing window and a chat pane, both of which are heavily instrumented. Students in the control group had no information about their partner’s gender, whereas students in the treatment group could see a gendered avatar representing the other participant as a man or as a woman. The gender of the avatar was swapped between programming tasks to analyze 45 variables related to the collaborative coding behavior, chat utterances, and questionnaire responses of 46 pairs in the original study at the University of Seville, and 23 pairs in the external replication at the University of California, Berkeley.ResultsWe did not observe any statistically significant effect of the gender bias treatment, nor any interaction between the perceived partner’s gender and subject’s gender, in any of the 45 response variables measured in the original study. In the external replication, we observed statistically significant effects with moderate to large sizes in four dependent variables within the experimental group, comparing how subjects acted when their partners were represented as a man or a woman.ConclusionsThe results in the original study do not show any clear effect of the treatment in remote pair programming among current Software Engineering students. In the external replication, it seems that students delete more source code characters when they have a woman partner, and communicate using more informal utterances, reflections and yes/no questions when they have a man partner, although these results must be considered inconclusive because of the small number of subjects in the replication, and because when multiple test corrections are applied, only the result about informal utterances remains significant. In any case, more mixed methods replications are needed in order to confirm or refute the results in the same and other Software Engineering students populations.

Switchable Wettability States of a Ferrofluid Droplet atop a Hydrophobic Interface.

Magnetically tuned soft machines offer great promise in performing a wide variety of programmable tasks via their dynamic shape adaptation and alteration. Despite dramatic recent advancements in this regard, selective reconfiguration of the wetting behavior of a ferrofluid droplet atop a hydrophobic interface adapted as a magnetically modulated micromachine remained elusive when the applied field intensity exceeds the saturation magnetization. Here we unveil a strategy to unsettle this perspective by harnessing a magnetic field-dependent magnetization phenomenon that may be exploited exclusively to arrive at highly controllable dynamic switchable wetting states of ferrofluid droplets, including the realization of wide ranges of contact angles for a given applied magnetic field. We arrive at a physical law from the resulting interplay of forces that quantifies the time dependence of the contact angle variation for a given magnetic field. Substantiated by experimental findings, our multiphysics-based simulations further evidence the possibilities of realizing switchable wetting states of soft magnetic matter over a wide range of physical parameters, delving into this principle. Disrupting the established notion of a trivially unique wetting phenomenon as governed by the droplet-substrate combination and the applied field alone, this paradigm may thus benefit a wide variety of practical applications, ranging from digital microfluidics to recombination chemistry.

Effectiveness of ChatGPT in Coding: A Comparative Analysis of Popular Large Language Models

This study explores the effectiveness and efficiency of the popular OpenAI model ChatGPT, powered by GPT-3.5 and GPT-4, in programming tasks to understand its impact on programming and potentially software development. To measure the performance of these models, a quantitative approach was employed using the Mostly Basic Python Problems (MBPP) dataset. In addition to the direct assessment of GPT-3.5 and GPT-4, a comparative analysis involving other popular large language models in the AI landscape, notably Google’s Bard and Anthropic’s Claude, was conducted to measure and compare their proficiency in the same tasks. The results highlight the strengths of ChatGPT models in programming tasks, offering valuable insights for the AI community, specifically for developers and researchers. As the popularity of artificial intelligence increases, this study serves as an early look into the field of AI-assisted programming.

A 65 nm General-Purpose Compute-in-Memory Processor Supporting Both General Programming and Deep Learning Tasks

A 65 nm General-Purpose Compute-in-Memory Processor Supporting Both General Programming and Deep Learning Tasks

Potentials of ChatGPT in Computer Programming: Insights from Programming Instructors

Potentials of ChatGPT in Computer Programming: Insights from Programming Instructors

Optimization of the magnetostrictional molding composites structure

The paper presents the methodology of multi-criteria synthesis of magnetostrictive cast composites with high physical-mechanical and anti-corrosive properties. The synthesis of composites is realized by solving nonlinear mathematical programming tasks sequentially. The quality of the modeling was evaluated using the composite quality functional. The numerical methods for solving the optimization problem of the target function of kinetic processes are based on the construction of Pareto sets, penalty functions of Arrow-Hurwitz, and the method of equal estimate regions. The necessary and sufficient criteria for forming optimal recipe-technological parameters were established, allowing to obtain composites with specified structure and properties. The proposed methods were tested in the development of a practical technology for producing polymer composites with magnetostrictive properties.

Application of artificial intelligence chatbots, including ChatGPT, in education, scholarly work, programming, and content generation and its prospects: a narrative review

This study aims to explore ChatGPT’s (GPT-3.5 version) functionalities, including reinforcement learning, diverse applications, and limitations. ChatGPT is an artificial intelligence (AI) chatbot powered by OpenAI’s Generative Pre-trained Transformer (GPT) model. The chatbot’s applications span education, programming, content generation, and more, demonstrating its versatility. ChatGPT can improve education by creating assignments and offering personalized feedback, as shown by its notable performance in medical exams and the United States Medical Licensing Exam. However, concerns include plagiarism, reliability, and educational disparities. It aids in various research tasks, from design to writing, and has shown proficiency in summarizing and suggesting titles. Its use in scientific writing and language translation is promising, but professional oversight is needed for accuracy and originality. It assists in programming tasks like writing code, debugging, and guiding installation and updates. It offers diverse applications, from cheering up individuals to generating creative content like essays, news articles, and business plans. Unlike search engines, ChatGPT provides interactive, generative responses and understands context, making it more akin to human conversation, in contrast to conventional search engines’ keyword-based, non-interactive nature. ChatGPT has limitations, such as potential bias, dependence on outdated data, and revenue generation challenges. Nonetheless, ChatGPT is considered to be a transformative AI tool poised to redefine the future of generative technology. In conclusion, advancements in AI, such as ChatGPT, are altering how knowledge is acquired and applied, marking a shift from search engines to creativity engines. This transformation highlights the increasing importance of AI literacy and the ability to effectively utilize AI in various domains of life.

A large-scale empirical study on mobile performance: energy, run-time and memory

Software performance concerns have been attracting research interest at an increasing rate, especially regarding energy performance in non-wired computing devices. In the context of mobile devices, several research works have been devoted to assessing the performance of software and its underlying code. One important contribution of such research efforts is sets of programming guidelines aiming at identifying efficient and inefficient programming practices, and consequently to steer software developers to write performance-friendly code.Despite recent efforts in this direction, it is still almost unfeasible to obtain universal and up-to-date knowledge regarding software and respective source code performance. Namely regarding energy performance, where there has been growing interest in optimizing software energy consumption due to the power restrictions of such devices. There are still many difficulties reported by the community in measuring performance, namely in large-scale validation and replication. The Android ecosystem is a particular example, where the great fragmentation of the platform, the constant evolution of the hardware, the software platform, the development libraries themselves, and the fact that most of the platform tools are integrated into the IDE’s GUI, makes it extremely difficult to perform performance studies based on large sets of data/applications. In this paper, we analyze the execution of a diversified corpus of applications of significant magnitude. We analyze the source-code performance of 1322 versions of 215 different Android applications, dynamically executed with over than 27900 tested scenarios, using state-of-the-art black-box testing frameworks with different combinations of GUI inputs. Our empirical analysis allowed to observe that semantic program changes such as adding functionality and repairing bugfixes are the changes more associated with relevant impact on energy performance. Furthermore, we also demonstrate that several coding practices previously identified as energy-greedy do not replicate such behavior in our execution context and can have distinct impacts across several performance indicators: runtime, memory and energy consumption. Some of these practices include some performance issues reported by the Android Lint and Android SDK APIs. We also provide evidence that the evaluated performance indicators have little to no correlation with the performance issues’ priority detected by Android Lint. Finally, our results allowed us to demonstrate that there are significant differences in terms of performance between the most used libraries suited for implementing common programming tasks, such as HTTP communication, JSON manipulation, image loading/rendering, among others, providing a set of recommendations to select the most efficient library for each performance indicator. Based on the conclusions drawn and in the extension of the developed work, we also synthesized a set of guidelines that can be used by practitioners to replicate energy studies and build more efficient mobile software.

The Good, the Bad, and the Missing: Neural Code Generation for Machine Learning Tasks

Machine learning (ML) has been increasingly used in a variety of domains, while solving ML programming tasks poses unique challenges due to the fundamental difference in the nature and the construct of general programming tasks, especially for developers who do not have ML backgrounds. Automatic code generation that produces a code snippet from a natural language description can be a promising technique to accelerate ML programming tasks. In recent years, although many deep learning-based neural code generation models have been proposed with high accuracy, the fact that most of them are mainly evaluated on general programming tasks calls into question their effectiveness and usefulness in ML programming tasks. In this article, we set out to investigate the effectiveness of existing neural code generation models on ML programming tasks. For our analysis, we select six state-of-the-art neural code generation models and evaluate their performance on four widely used ML libraries, with newly created 83K pairs of natural-language described ML programming tasks. Our empirical study reveals some good, bad, and missing aspects of neural code generation models on ML tasks, with a few major ones listed below. ( Good ) Neural code generation models perform significantly better on ML tasks than on non-ML tasks with an average difference of 10.6 points in BLEU-4 scores. ( Bad ) More than 80% of the generated code is semantically incorrect. ( Bad ) Code generation models do not have significance in improving developers’ completion time. ( Good ) The generated code can help developers write correct code by providing developers with clues for using correct APIs. ( Missing ) The observation from our user study reveals the missing aspects of code generation for ML tasks, e.g., decomposing code generation for divide-and-conquer into API sequence identification and API usage generation.

In-Situ Reflectance Absorption Infra-Red Spectroscopy (RAIRS) Study on ALD of HfO2 on TiN and Pt Electrodes

Ferroelectricity in hafnia fluoride analogues has been widely investigated for nonvolatile memory applications. Although ALD of HfO2 technology is enough mature for CMOS technology, hafnia-based ferroelectrics still suffer from major reliability issues such as relaxation, imprint, fatigue, and breakdown.1 These reliability characteristics are thought to be related to the interface conditions between the ferroelectric film and the electrode. Thus, many reports suggested routes to enhance reliability and ferroelectric characteristics by modifying the interface states of the metal-ferroelectric-metal (MFM) capacitors. Unfortunately, only few reports disclose direct observation on evolution of interface formation and its mechanism. Recently, it is reported that scaling down the ferroelectric hafnia-based films to a few nm range dramatically improves the MFM capacitors’ reliability where the series capacitance and surface effect dominate, making it crucial to understand the interface properties carefully.2 In this presentation, we perform In-situ characterization using reflectance absorption infrared spectroscopy (RAIRS) to monitor the surface reaction and interface formation while HfOx ALD using TDMA-Hf with various oxidation reactants, such as H2O, O3, and anhydrous H2O2. TiN and Pt bottom electrodes are investigated at 250 °C. The RAIRS results show that the initial exposure of the oxidant generates the most amount of oxidation on the TiN surface by showing Ti-O and TiN-O peaks which were not observed for the Pt substrate. Moreover, the Ti-O and TiN-O peak area increase versus the dosing time of oxidant was minimum for H2O, and O3 showed continuous peak increase even after 12 s exposure. Interestingly, for the H2O2 case, there was almost no peak increase in the Ti-O bond region like H2O, but the TiN-O and TiO-O peaks appeared after the first 3 s and saturated after 6 s exposure. Also, not only on the TiN substrate, the peak for O-O stretching was observed for the Pt substrate as well, indicating that the H2O2 generates self-limiting peroxide bonds on the surface with a minimum interface formation. This suppressed interface formation with superior film properties using H2O2 might be the reason behind the robust reliability of the H2O2-based HZO capacitor. We are going to report reliability characteristics in conjunction with interface evolution.Acknowledements: We thank to YEST and KEIT for supporting the project through ISTD Program (No.20010806). This work is also supported by GRC-LMD program (task#3001.001) through SRC. The BRUTE hydrogen peroxide was provided by RASIRC. Ozone generator for TMEICReferences 1 J. Mohan et al., ACS AELM, 4, 4 (2022). 2 K. Toprasertpong et al., ACS AMI, 14 (2022).

Layout and cross-sectional size optimization of truss structures with mixed design variables based on gradient method

The object of the study was truss-type rod structures, which were investigated for the purpose of finding the optimal design solution in a mixed (continuous and discrete) space of variables. The parameters of the geometric scheme of the truss, as well as the dimensions of the cross-sections of its elements, were considered as design variables. The stated optimization problem is represented as a nonlinear programming task, in which the objective function and nonlinear constraints of the mathematical model are continuously differentiable functions of the design variables. The system of constraints includes strength and stability inequalities, formulated for the design cross-sections of the rod elements of the structure, which is subject to the effect of the design load combinations of the ultimate limit states. As a part of the system of constraints, the displacement constraints formulated for the specified structural nodes, which is subject to the action of design load combinations of the serviceability limit states, are considered. To solve the stated optimization problem, a method of the objective function gradient on the surface of active constraints was used, with the simultaneous elimination of residuals in the violated restrictions. For design variables, the variation of which must be performed according to a given set of possible discrete values, a discretization procedure for the optimal solution obtained in the continuous space of design variables is proposed. A comparison of the proposed optimization methodology with alternative metaheuristic methods and algorithms reported in the literature was performed. On the considered problem of parametric optimization of a 47-span tower structure, a design solution with a weight of 835,403 kg was obtained, which is 1.53...4.6 % better than the optimal solutions obtained by other authors