Towards Identifying Code Proficiency through the Analysis of Python Textbooks
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arXiv
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| Format: | Preprint |
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2024
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| author | Rojpaisarnkit, Ruksit Robles, Gregorio Kula, Raula Gaikovina Wang, Dong Ragkhitwetsagul, Chaiyong Gonzalez-Barahona, Jesus M. Matsumoto, Kenichi |
| author_facet | Rojpaisarnkit, Ruksit Robles, Gregorio Kula, Raula Gaikovina Wang, Dong Ragkhitwetsagul, Chaiyong Gonzalez-Barahona, Jesus M. Matsumoto, Kenichi |
| contents | Python, one of the most prevalent programming languages today, is widely utilized in various domains, including web development, data science, machine learning, and DevOps. Recent scholarly efforts have proposed a methodology to assess Python competence levels, similar to how proficiency in natural languages is evaluated. This method involves assigning levels of competence to Python constructs, for instance, placing simple 'print' statements at the most basic level and abstract base classes at the most advanced. The aim is to gauge the level of proficiency a developer must have to understand a piece of source code. This is particularly crucial for software maintenance and evolution tasks, such as debugging or adding new features. For example, in a code review process, this method could determine the competence level required for reviewers. However, categorizing Python constructs by proficiency levels poses significant challenges. Prior attempts, which relied heavily on expert opinions and developer surveys, have led to considerable discrepancies. In response, this paper presents a new approach to identifying Python competency levels through the systematic analysis of introductory Python programming textbooks. By comparing the sequence in which Python constructs are introduced in these textbooks with the current state of the art, we have uncovered notable discrepancies in the order of introduction of Python constructs. Our study underscores a misalignment in the sequences, demonstrating that pinpointing proficiency levels is not trivial. Insights from the study serve as pivotal steps toward reinforcing the idea that textbooks serve as a valuable source for evaluating developers' proficiency, and particularly in terms of their ability to undertake maintenance and evolution tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_02262 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Identifying Code Proficiency through the Analysis of Python Textbooks Rojpaisarnkit, Ruksit Robles, Gregorio Kula, Raula Gaikovina Wang, Dong Ragkhitwetsagul, Chaiyong Gonzalez-Barahona, Jesus M. Matsumoto, Kenichi Software Engineering D.2.0; D.2.7 Python, one of the most prevalent programming languages today, is widely utilized in various domains, including web development, data science, machine learning, and DevOps. Recent scholarly efforts have proposed a methodology to assess Python competence levels, similar to how proficiency in natural languages is evaluated. This method involves assigning levels of competence to Python constructs, for instance, placing simple 'print' statements at the most basic level and abstract base classes at the most advanced. The aim is to gauge the level of proficiency a developer must have to understand a piece of source code. This is particularly crucial for software maintenance and evolution tasks, such as debugging or adding new features. For example, in a code review process, this method could determine the competence level required for reviewers. However, categorizing Python constructs by proficiency levels poses significant challenges. Prior attempts, which relied heavily on expert opinions and developer surveys, have led to considerable discrepancies. In response, this paper presents a new approach to identifying Python competency levels through the systematic analysis of introductory Python programming textbooks. By comparing the sequence in which Python constructs are introduced in these textbooks with the current state of the art, we have uncovered notable discrepancies in the order of introduction of Python constructs. Our study underscores a misalignment in the sequences, demonstrating that pinpointing proficiency levels is not trivial. Insights from the study serve as pivotal steps toward reinforcing the idea that textbooks serve as a valuable source for evaluating developers' proficiency, and particularly in terms of their ability to undertake maintenance and evolution tasks. |
| title | Towards Identifying Code Proficiency through the Analysis of Python Textbooks |
| topic | Software Engineering D.2.0; D.2.7 |
| url | https://arxiv.org/abs/2408.02262 |