GitHub Copilot AI pair programmer: Asset or Liability? (2206.15331v2)

Abstract: Automatic program synthesis is a long-lasting dream in software engineering. Recently, a promising Deep Learning (DL) based solution, called Copilot, has been proposed by OpenAI and Microsoft as an industrial product. Although some studies evaluate the correctness of Copilot solutions and report its issues, more empirical evaluations are necessary to understand how developers can benefit from it effectively. In this paper, we study the capabilities of Copilot in two different programming tasks: (i) generating (and reproducing) correct and efficient solutions for fundamental algorithmic problems, and (ii) comparing Copilot's proposed solutions with those of human programmers on a set of programming tasks. For the former, we assess the performance and functionality of Copilot in solving selected fundamental problems in computer science, like sorting and implementing data structures. In the latter, a dataset of programming problems with human-provided solutions is used. The results show that Copilot is capable of providing solutions for almost all fundamental algorithmic problems, however, some solutions are buggy and non-reproducible. Moreover, Copilot has some difficulties in combining multiple methods to generate a solution. Comparing Copilot to humans, our results show that the correct ratio of humans' solutions is greater than Copilot's suggestions, while the buggy solutions generated by Copilot require less effort to be repaired.

An Analysis of GitHub Copilot as a Developer's Assistant

The research paper entitled "GitHub Copilot AI Pair Programmer: Asset or Liability?" provides a nuanced exploration into the efficacy and potential pitfalls of GitHub Copilot, an AI-based programming assistant developed by OpenAI and Microsoft. While the promise of automatic program synthesis has intrigued software engineering audiences for decades, it is only recently that deep learning models such as Copilot, which leverage extensive code databases, show promise as viable industrial solutions.

Core Investigations and Methodology

The authors systematically investigate Copilot's capabilities across two primary dimensions: solving fundamental algorithmic problems and performing programming tasks seen in educational settings. These evaluations aim to understand Copilot's practical utility for developers, assessing both the quality and the correctness of the AI's generated solutions.

1. Algorithmic Challenges: The paper rigorously tests Copilot against a suite of fundamental algorithmic issues, including sorting algorithms, data structures like binary search trees, and graph algorithms. These are selected for their grounding in computer science education and their frequent occurrence in technical interviews and practical software engineering tasks.
2. Coding Task Variability: By contrasting Copilot's performance against human coders—specifically junior developers in educational settings—the paper gauges the AI's capacity to mimic or even improve upon human coding solutions. The dataset chosen for the paper includes Python programming tasks with varying levels of complexity and breadth, offering a representative scope of coding activities encountered in academic and novice environments.

Evaluation Criteria

The paper employs several evaluation criteria to thoroughly assess Copilot:

• Correctness: Both syntactic and operational correctness are evaluated, with particular emphasis on whether Copilot solutions pass predefined unit tests peculiar to each problem set.
• Optimality and Complexity: For solutions that are functionally correct, the authors assess whether the solutions are optimized, considering time complexity as a crucial factor.
• Reproducibility: Tests were conducted to see if Copilot could reproduce or vary its correct solutions over multiple trials, examining the consistency of the AI-generated code.
• Diversity and Code Quality: The diversity of the code output, especially solutions within and across trials for similarity, is assessed. Additionally, cyclomatic complexity provides insight into the maintainability and understandability of the generated code.

Key Findings

• Programming Competence: Copilot demonstrates notable competence in producing code that is both correct (passes unit tests) and optimal (time complexity considerations). However, its performance relies heavily on the conciseness and clarity of problem statements.
• Consistency and Quality: While Copilot's suggestions are competitive with human-coded solutions in terms of complexity and correctness, a notable inconsistency is observed in producing correct solutions across different attempts and trials, attributed in part to the black-box nature of its underlying model.
• Bug Fixing and Repairs: Analysis shows Copilot’s buggy solutions can often be rectified with relatively low complexity compared to those produced by novice developers. This observation suggests that Copilot’s flawed outputs are often closer to correctness, facilitating easier debugging.

Practical Implications and Future Directions

The paper's findings have significant implications for software engineering practices, particularly regarding the integration of AI assistants in development workflows. Experienced developers may find Copilot a valuable asset for rapid prototyping and tackling monotonous coding tasks, while novice developers must exercise caution and maintain a critical engagement with AI-suggested solutions to filter out errors and suboptimal code.

Future research could expand on this paper by incorporating industrial-level coding datasets to assess Copilot's utility in varied real-world scenarios. Additionally, more in-depth investigations into how developers of different skill levels interact with and adapt to AI suggestions could offer insights into optimizing workflows and educational strategies.

In conclusion, the paper contributes to the growing discourse around AI's role in software development, presenting an empirical assessment that can guide both industry adoption and further academic inquiry into tools like Copilot.