Programming in Python is faster than in C, C++ or Java
the verdict
CONTESTED
contested - evenly split
refutedsupported
the weight of evidence
3 sources for · 4 against
Evidence regarding programming speed and development time in Python versus C, C++ or Java is mixed; some computational sources note that Python offers faster development time and lower complexity, whereas execution and runtime speed benchmarks consistently find that C, C++ and Java outperform Python.
Quality plays center stage in any software development industry. Software metrics have proven over time as the best measure to be used to assess and assure the software developers of the quality of their products. Halstead software science is an essential technique for measuring software complexity at the source code. In this study, we present a comparative study using this technique to help the developer by evaluating the code complexity by considering the structural composition of a programming language. In this study, an experiment was done using Halstead metrics to evaluate the complexity of PHP, C++, Java, C and Python programming languages. This study demonstrate that Halstead gives a better approach in evaluating the level of complexity of programming languages at source code level. The results showed that C++ and Java are the most complex programming languages while Python was the least complex warranting less of the programmer's time and effort when developing a similar project. These findings can be used by the software developers to make decisions on the programming language to adopt when they want to come up with less complex software of high quality. In the future, the researchers will advance the study to incorporate other software paradigms and also modify the technique to capture also inter and intra-modular structural complexity of the various programming languages.
Our research focuses on the intersection of artificial intelligence (AI) and software development, particularly the role of AI models in automating code generation. With advancements in large language models like ChatGPT, developers can now generate code from natural language prompts, a task that traditionally required significant manual input and expertise. AI-generated code promises to boost productivity by enabling faster prototyping and automating repetitive coding tasks. However, as these models are increasingly adopted in real-world applications, questions surrounding their efficiency and code quality become critical. This research investigates ChatGPT-4o, a state-of-the-art language model, and its ability to generate functional, high-quality code in different programming languages. By comparing performance between Python and Java, the study seeks to shed light on AI’s capabilities and limitations in code generation, addressing not only functional correctness but also broader software engineering concerns such as memory usage, runtime efficiency, and maintainability. The study addresses key questions related to the performance, code quality, and error management of AI-generated code by analyzing solutions for 300 data structure problems and 300 problems from the LeetCode platform. The findings reveal notable performance differences between the two languages: Java demonstrated superior runtime performance, particularly for medium and hard problems, while Python exhibited
In software engineering, software complexity measures how complicated it is to design, test, maintain, and comprehend a system or a program. Metrics have been appreciated over time as a measure of various attributes of software products. Some of the most well-known languages for scientific, object-oriented, and imperative programming are Python, Java, and C, respectively. However, it is not easy to distinguish the structural complexity of these programming languages and the existing studies have overlooked this issue. This study, therefore, uses a technique based on Halstead Software Science to conduct a comparative investigation to evaluate the lexicographical complexity of sequence, selection, and looping program structures in object-oriented, scientific, and imperative programming languages. Halstead Complexity Metrics were implemented utilizing sequence, selection, and loop control structures in Java, C, and Python to accomplish the study's goal. When subjected to the Halstead software science comprising of nine measurement criteria, the findings of the experiment demonstrated that in sequence and Loops program structures C language has the highest lexicographical complexity followed by Java, while in Selection program structures Java is more slightly complex than C. Python on the other hand, had the least lexicographical complexity across all three essential program structures—sequence, selection, and loops during the comparative study, therefore, it is the most appropriate programming language among the three that are being studied here in terms of program complexity. Using the results of this study, we intend to use effort prediction models in the future to estimate the programming effort. We also intend to do additional experiments with the same program structures using larger program samples in the future. A replication of the study using different programming languages is also suggested.
13. Program development in Python is faster than in Fortran, C, C++, or Java, thus making Python well … and program- ming is more convenient than in Fortran, C, C++, or Java. Perl, Python, Ruby, Scheme, and Tcl … accomplish in a language like Python than in Fortran, C, C++, or Java. Chapter 3 presents lots of examples
In today's technology era, programming is more essential than ever. Python and C++ are the most widely used programming languages. Python, the most popular programming language of the twenty-first century, is a higher-level object-oriented language, while C++ (the language that powers most operating systems) is a lowerlevel object-oriented language. Python and C++ are compared in this article. This book covers an introduction to various languages, memory management techniques, and reasons for their program execution speed. Furthermore, this study looked at the execution time and memory requirements of a variety of algorithms in both languages, comparing the best, average, and worst situations in each language. Furthermore, they are compared in terms of their benefits and drawbacks. C++ is faster than Python in terms of execution speed, according to the findings, but Python is a better option for beginners due to its simplicity. Aside from that, the language should be selected depending on the project type in order to get the best results. Programmers play a crucial role in computing. C/C++, Python, and Java are the most common languages. Beginning programming classes often utilize C++ and Java. Python has grown in popularity as an introductory programming language due to a variety of reasons. As an entry-level programming language, C, C++, Java, and Python were compared. More students selected Python over C++ because of its simpler pseudo-code syntax, better learning environm
In today's technology era, programming is more essential than ever. Python and C++ are the most widely used programming languages. Python, the most popular programming language of the twenty-first century, is a higher-level object-oriented language, while C++ (the language that powers most operating systems) is a lower-level object-oriented language. Python and C++ are compared in this article. This book covers an introduction to various languages, memory management techniques, and reasons for their program execution speed. Furthermore, this study looked at the execution time and memory requirements of a variety of algorithms in both languages, comparing the best, average, and worst situations in each language. Furthermore, they are compared in terms of their benefits and drawbacks. C++ is faster than Python in terms of execution speed, according to the findings, but Python is a better option for beginners due to its simplicity. Aside from that, the language should be selected depending on the project type in order to get the best results. Programmers play a crucial role in computing. C/C++, Python, and Java are the most common languages. Beginning programming classes often utilize C++ and Java. Python has grown in popularity as an introductory programming language due to a variety of reasons. As an entry-level programming language, C, C++, Java, and Python were compared. More students selected Python over C++ because of its better pseudo-code syntax, simpler learning environ
Abstract Background The performance of different programming languages has previously been benchmarked using abstract mathematical algorithms, but not using standard bioinformatics algorithms. We compared the memory usage and speed of execution for three standard bioinformatics methods, implemented in programs using one of six different programming languages. Programs for the Sellers algorithm, the Neighbor-Joining tree construction algorithm and an algorithm for parsing BLAST file outputs were implemented in C, C++, C#, Java, Perl and Python. Results Implementations in C and C++ were fastest and used the least memory. Programs in these languages generally contained more lines of code. Java and C# appeared to be a compromise between the flexibility of Perl and Python and the fast performance of C and C++. The relative performance of the tested languages did not change from Windows to Linux and no clear evidence of a faster operating system was found. Source code and additional information are available from http://www.bioinformatics.org/benchmark/ Conclusion This benchmark provides a comparison of six commonly used programming languages under two different operating systems. The overall comparison shows that a developer should choose an appropriate language carefully, taking into account the performance expected and the library availability for each language.
Everything we examined (7) — 6 independent sources
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