12 research outputs found
The Impact of Language Syntax on the Complexity of Programs: A Case Study of Java and Python
No full textProgramming is the cornerstone of computer science, yet it is difficult to learn and program. The syntax of a programming language is particularly challenging to comprehend, which makes learning arduous and affects the program\u27s testability. There is currently no literature that definitively gives quantitative evidence about the effect of programming language complex syntax. The main purpose of this article was to examine the effects of programming syntax on the complexity of their source programs. During the study, 298 algorithms were selected and their implementations in Java and Python were analyzed with the cyclomatic complexity matrix. The results of the study show that Python\u27s syntax is less complex than Java\u27s, and thus coding in Python is more comprehensive and less difficult than Java coding. The Mann-Whitney U test was performed on the results of a statistical analysis that showed a significant difference between Java and Python, indicating that the syntax of a programming language has a major impact on program complexity. The novelty of this article lies in the formulation of new knowledge and study patterns that can be used primarily to compare and analyze other programming languages
Analysis of Code Vulnerabilities in Repositories of GitHub and Rosettacode: A comparative Study
No full textOpen-source code hosted online at programming portals is present in 99% of commercial software and is common practice among developers for rapid prototyping and cost-effective development. However, research reports the presence of vulnerabilities, which result in catastrophic security compromise, and the individual, organization, and even national secrecy are all victims of this circumstance. One of the frustrating aspects of vulnerabilities is that vulnerabilities manifest themselves in hidden ways that software developers are unaware of. One of the most critical tasks in ensuring software security is vulnerability detection, which jeopardizes core security concepts like integrity, authenticity, and availability. This study aims to explore security-related vulnerabilities in programming languages such as C, C++, and Java and present the disparities between them hosted at popular code repositories. To attain this purpose, 708 programs were examined by severity-based guidelines. A total of 1371 vulnerable codes were identified, of which 327 in C, 51 in C++, and 993 in Java. Statistical analysis also indicated a substantial difference between them, as there is ample evidence that the Kruskal-Wallis H-test p-value (.000) is below the 0.05 significance level. The Mann-Whitney Test mean rank for GitHub (Mean-rank=676.05) and Rosettacode (Mean-rank=608.64) are also different. The novelty of this article is to identify security vulnerabilities and grasp the nature severity of vulnerability in popular code repositories. This study eventually manifests a guideline for choosing a secure programming language as a successful testing technique that targets vulnerabilities more liable to breaching security.
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Pedagogical Significance of Natural Language Programming in Introductory Programming
No full text Learning programming is hard for novice students. Complicated syntax and semantic of programming languages and lack of previous knowledge are the contributing factors behind the hardness of programming. Natural programming language allows to program in a natural language and thereby ease the programming. In this paper, it is ascertained whether natural programming language is fruitful in learning the elementary programming concepts and supportive in preparing students for introductory programming courses. The discussion included in this paper can be used to design supportive programming languages and formulating effective courses and learning material to ameliorate performance of students’ in learning of introductory programming environments.</jats:p
Learners Programming Language a Helping System for Introductory Programming Courses
No full textProgramming is the core of computer science and due to this momentousness a special care is taken in
designing the curriculum of programming courses. A substantial work has been conducted on the
definition of programming courses, yet the introductory programming courses are still facing high
attrition, low retention and lack of motivation. This paper introduced a tiny pre-programming language
called LPL (Learners Programming Language) as a ZPL (Zeroth Programming Language) to illuminate
novice students about elementary concepts of introductory programming before introducing the first
imperative programming course. The overall objective and design philosophy of LPL is based on a
hypothesis that the soft introduction of a simple and paradigm specific textual programming can increase
the motivation level of novice students and reduce the congenital complexities and hardness of the first
programming course and eventually improve the retention rate and may be fruitful in reducing the
dropout/failure level. LPL also generates the equivalent high level programs from user source program
and eventually very fruitful in understanding the syntax of introductory programming languages. To
overcome the inherent complexities of unusual and rigid syntax of introductory programming languages,
the LPL provide elementary programming concepts in the form of algorithmic and plain natural language based computational statements. The initial results obtained after the introduction of LPL are very encouraging in motivating novice students and improving the retention rate
Unveiling Inefficiencies in Open-Source Code Using Multistage Analysis with Software Metrics
No full textSoftware development is challenging due to its technical complexity and time-consuming nature. To overcome these difficulties, various technical solutions have been introduced. In commercial software development, code repositories serve as valuable resources, reducing the time and cost involved in the process. The utilization of pre-developed open code repositories has proven to reduce development time. However, ample amount of work has not determined whether these repositories are testable, maintainable, free of dead code, and have a concise implementation of equivalent algorithms. The objective of this article is to address this gap by thoroughly analyzing the complexity and maintainability of code repositories, determining the impact of removing dead code on size, complexity, and maintainability. For this study, a total of 200 Python open-source code were analyzed using RADON, a widely-used metric tool for assessing cyclomatic complexity, size, volume, and maintainability. The identification of dead code within the repositories was accomplished using Vulture, supplemented by expert evaluation. It has been revealed that the majority of the examined code included dead code, and the removal of this code led to a significant reduction in cyclomatic complexity, volume, and size, while improving code maintainability, as observed by the Mann Whitney U test. The study concludes that the blind use of open-source code is not safe. It strongly recommends the community to thoroughly explore and examine such code from different perspectives before actual implementation. The novelty of this study lies in the use of multiple software metrics in a multi-stage analysis to examine the impact of removing dead code on program complexity, size, and maintainability
C in CS1: Snags and Viable Solution
No full textProgramming is one of the career rewarding skills; however, learning programming skill is extremely hard and arduous as supported by several studies. The first programming language has an everlasting impact on the programmer’s program’s development abilities. In most of the universities the imperative paradigm is used for introductory programming courses and generally C language is used as a base language of a first programming course. The C language is a leading programming language and extensively utilized for commercial applications. The majority of the programming languages are highly motivated from the C language, yet its intrinsic complexities and non-pedagogic origin evidently makes it hard and a complex choice for a first programming course. This paper proposed a rational and realizable solution that can make a C language a suitable choice for a first the course of programming
Unveiling Inefficiencies in Open-Source Code Using Multistage Analysis with Software Metrics
No full textSoftware development is challenging due to its technical complexity and time-consuming nature. To overcome these difficulties, various technical solutions have been introduced. In commercial software development, code repositories serve as valuable resources, reducing the time and cost involved in the process. The utilization of pre-developed open code repositories has proven to reduce development time. However, ample amount of work has not determined whether these repositories are testable, maintainable, free of dead code, and have a concise implementation of equivalent algorithms. The objective of this article is to address this gap by thoroughly analyzing the complexity and maintainability of code repositories, determining the impact of removing dead code on size, complexity, and maintainability. For this study, a total of 200 Python open-source code were analyzed using RADON, a widely-used metric tool for assessing cyclomatic complexity, size, volume, and maintainability. The identification of dead code within the repositories was accomplished using Vulture, supplemented by expert evaluation. It has been revealed that the majority of the examined code included dead code, and the removal of this code led to a significant reduction in cyclomatic complexity, volume, and size, while improving code maintainability, as observed by the Mann Whitney U test. The study concludes that the blind use of open-source code is not safe. It strongly recommends the community to thoroughly explore and examine such code from different perspectives before actual implementation. The novelty of this study lies in the use of multiple software metrics in a multi-stage analysis to examine the impact of removing dead code on program complexity, size, and maintainability
AI vs. Human Programmers: Complexity and Performance in Code Generation
No full textLarge language models, like ChatGPT, have shown the ability to do a variety of tasks in different fields, and this has increased efficiency greatly. However, their increasing use is causing concern about the potential job displacement, particularly in the technical fields. While there have been many studies on the performance of large language models in technical fields, there is a notable absence in assessing their performances in programming. This study fills this gap by comparing ChatGPT (GPT-4) and human experts in the coding discipline to determine if ChatGPT has advanced to a point where it can replace human programmers. To accomplish this goal, this study has produced 300 Python programs with ChatGPT (GPT-4) and compared them with functionally equivalent programs written by three experienced human programmers. The evaluation included both quantitative and qualitative evaluations using measures such as Halstead Complexity, Cyclomatic Complexity, and expert judgment by two human evaluators. The results showed statistically significant differences between the ChatGPT-generated and human-written code. Programs that were generated by ChatGPT were shown to be verbose, complex, and resource demanding, which is reflected in higher program volume, difficulty, and cyclomatic complexity scores. In qualitative terms, ChatGPT\u27s code was easier to read, but lagged behind in some key areas, such as the quality of documentation, structuring of functions, and compliance with coding standards. On the other hand, human-written programs performed well in terms of maintainability, error handling, and dealing with edge cases. Although ChatGPT was found to be incredibly efficient at creating working code, the output needed a lot of review and refinement to be considered standard. The study concluded while ChatGPT is a useful tool for generating code, it has not yet reached the level needed to replace human expertise in programming
IMPACT OF PAIR PROGRAMMING ON NOVICE PERFORMANCE IN ONLINE FIRST PROGRAMMING LANGUAGE (FPL) DURING COVID-19
No full textThe intention of this research is to investigate effectiveness and impact of pairprogramming on the performance of novices in first online programminglanguage like C during covid-19 pandemic. This study analyzed the efficacy ofpair programming used as a teaching tool in online introductory programminglanguage. It was found that pair programming technique in teaching FPL hasconsiderable impact on grades, learning, error handlining, cognitiveprogramming and collaborating learning of novices in CS1.This paper reports onthe final results of novices indicating their progress in first online programminglanguage course. It is indicated that collective erudition had valuable impressionon novice learning outcomes thus making learning first programming languagemore interactive, appealing and exhilarating. We were concentrating on howpair programming has affected performance, retention, dropout rate, femaleand male performance of novices in online programming course. We inferredthat the treatment group with pair programming teaching approach performedbetter in programming and produced better programs with good understandingof error handling and recovery and their confident level much better in theirsolutions and relished with completion of their assignments and achieved bettergrades than the control group, in which no pair programming teaching methodwas used. The results of analysis indicate that in experimental treatment grouppass rate was high, female novices performed more effectively as compared tonon-pairs control group
