Pedagogical Suitability: A Software Metrics-Based Analysis of Java and Python

Muhammad Naveed

Authors

Muhammad Naveed Department of Computer Science, University of Balochistan

Keywords:

Introductory Programming, Programming Language Comparison, Halstead Complexity Metrics, Java, Python

Abstract

Programming is one of the foundational skills essential for computer science professionals, yet attaining proficiency in this skill is widely acknowledged as a formidable challenge. The intrinsic complexity of programming is often cited as the primary factor contributing to its difficulty. The choice of programming language for IP courses typically relies on past experiences and empirical evidence, rather than on a quantitative basis, which can affect its effectiveness and suitability for novice learners. The study presented in this article conducted a quantitative analysis of Java and Python to assess their suitability for use in IP courses. The analysis involved evaluating programs based on a total of 210 elementary programming algorithms using HCM. The results of the study indicated that Python programs, compared to Java programs, have a reduced reliance on lexical elements, are less complex, and have a smaller code size. Additionally, Python was found to produce less complex programs and required less effort and time for development and maintenance. Moreover, Python programs tend to have fewer bugs. Overall, the study concluded that Python is better suited for IP courses than Java. The novelty of this study lies in its quantitative comparison of Java and Python using HCM, revealing that Python is more appropriate for IP courses due to its lower complexity, reduced development effort, and fewer bugs.

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