The rapid adoption of AI coding agents is fundamentally shifting software developers’ roles from code authors to code reviewers. While developers spend a significant portion of their time reading and comprehending code, the linguistic proficiency and complexity of the Python code generated by these agents remain largely unexplored. This study investigates the code proficiency of AI agents to determine the skill level required for developers to maintain their code. Leveraging the AIDev dataset, we mined 591 pull requests containing 5,027 Python files generated by three distinct AI agents and employed pycefr, a static analysis tool that maps Python constructs to six proficiency levels, ranging from A1 (Basic) to C2 (Mastery), to analyze the code. Our results reveal that: AI agents predominantly generate Basic-level code, with over 90% of constructs falling into the A1 and A2 categories, and less than 1% classified as Mastery (C2); AI agents’ and humans’ pull requests share a broadly similar proficiency profile; High-proficiency code by AI agents are from feature addition and bug fixing tasks. These findings suggest that while AI-generated code is generally accessible to developers with basic Python skills, specific tasks may require advanced proficiency to review and maintain complex, agent-generated constructs.

In an era increasingly shaped by generative AI, code understanding remains a critical foundation for ensuring software quality, reliability, and maintainability. While AI systems can accelerate code generation, developers still face substantial challenges in comprehending, debugging, and effectively integrating the resulting artifacts. Code proficiency, defined not only by comprehension but also by the ability to write efficient, idiomatic code, plays a central role in addressing these challenges. Existing tools, such as those assigning CEFR-based levels, which outline six levels of proficiency from A1 to C2, to code constructs that offer initial frameworks for assessing code difficulty. However, their manually derived classifications lack empirical validation and often diverge from pedagogical progressions found in computer science textbooks. This research seeks to address the absence of a standardized, data-driven metric for determining the proficiency levels required to understand specific programming elements. We propose an automated framework grounded in textbook analysis and clustering techniques to establish a scalable proficiency metric applicable to both human-written and AI-generated code. Preliminary findings reveal strong alignment with educational sequencing and suggest promising applications in AI-assisted software development, particularly in enhancing code review workflows and tailoring AI-generated code to developers' proficiency levels.

In this paper, we present jscefr (pronounced jes-cee-fer), a tool that detects the use of different elements of the JavaScript (JS) language, effectively measuring the level of proficiency required to comprehend and deal with a fragment of JavaScript code in software maintenance tasks. Based on the pycefr tool, the tool incorporates JavaScript elements and the well-known Common European Framework of Reference for Languages (CEFR) and utilizes the official ECMAScript JavaScript documentation from the Mozilla Developer Network. jscefr categorizes JS code into six levels based on proficiency. jscefr can detect and classify 138 different JavaScript code constructs. To evaluate, we apply our tool to three JavaScript projects of the NPM ecosystem, with interesting results. A video demonstrating the tool's availability and usage is available at https://youtu.be/Ehh-Prq59Pc.

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.

With the urgent need to secure supply chains among Open Source libraries, attention has focused on mitigating vulnerabilities detected in these libraries. Although awareness has improved recently, most studies still report delays in the mitigation process. This suggests that developers still have to deal with other contributions that occur during the period of fixing vulnerabilities, such as coinciding Pull Requests (PRs) and Issues, yet the impact of these contributions remains unclear. To characterize these contributions, we conducted a mixed-method empirical study to analyze NPM GitHub projects affected by 554 different vulnerability advisories, mining a total of 4,699 coinciding PRs and Issues. We believe that tool development and improved workload management for developers have the potential to create a more efficient and effective vulnerability mitigation process.

Python is known to be used by beginners to professional programmers. Python provides functionality to its community of users through PyPI libraries, which allows developers to reuse functionalities to an application. However, it is unknown the extent to which these PyPI libraries require proficient code in their implementation. We conjecture that PyPI contributors may decide to implement more advanced Pythonic code, or stick with more basic Python code. Are complex codes only committed by few contributors, or only to specific files? The new idea in this paper is to confirm who and where complex code is implemented. Hence, we present a visualization to show the relationship between proficient code, contributors, and files. Analyzing four PyPI projects, we are able to explore which files contain more elegant code, and which contributors committed to these files. Our results show that most files contain more basic competency files, and that not every contributor contributes competent code. We show how our visualization is able to summarize such information, and opens up different possibilities for understanding how to make elegant contributions.