Is Stack Overflow Obsolete? An Empirical Study of the Characteristics of ChatGPT Answers to Stack Overflow Questions

Title of the Paper

Is Stack Overflow Obsolete? An Empirical Study of the Characteristics of ChatGPT Answers to Stack Overflow Questions

Paper Information

• Subject Area: Performance comparison of programming assistance tools and user behavior analysis
• Keywords: Stack Overflow, Q&A platform, large language models, ChatGPT, misinformation

Research Background and Issues

• Identified Problems or Challenges:
  • With the widespread application of large language models (LLMs) like ChatGPT, their impact on programmers' online help-seeking behavior is becoming increasingly significant.
  • The quality of ChatGPT's answers (e.g., correctness, consistency, comprehensiveness, and conciseness) has not been systematically analyzed.
  • Misinformation generated by ChatGPT may mislead users, resulting in software design flaws and even potential societal risks.
• Significance of the Research:
  • The spread of misinformation can threaten software quality, cybersecurity, and the normal functioning of society as a whole.
  • Programmers are increasingly inclined to use ChatGPT instead of traditional community platforms (e.g., Stack Overflow), necessitating an in-depth understanding of this trend and its implications.
• Research Motivation and Related Work:
  • Existing research mainly focuses on ChatGPT's capabilities in fields such as law, finance, and healthcare, lacking a comprehensive analysis of its answer quality and characteristics for programming questions.
  • This study aims to fill this gap by exploring ChatGPT's performance in answering programming questions and the programming community's perception of it.

Research Questions

The study addresses the following aspects:

1. What are the differences in correctness and quality between ChatGPT's answers and Stack Overflow answers?
2. What are the specific categories of quality issues in ChatGPT's answers, and what are the underlying causes of these issues?
3. Does the type of programming question affect the quality of ChatGPT's answers?
4. What are the linguistic characteristics of ChatGPT's answers compared to Stack Overflow answers?
5. Are there differences in the emotional tone of ChatGPT's answers compared to Stack Overflow answers?
6. Can programmers distinguish between ChatGPT-generated answers and human-written answers?
7. Can programmers identify misinformation in ChatGPT's answers?
8. Do programmers prefer ChatGPT or Stack Overflow when selecting answers?

Solutions

• Research Methods and Steps:

  • Data Collection: Collected 517 programming questions from Stack Overflow and generated corresponding answers using ChatGPT; additionally, randomly sampled 2,000 questions for linguistic analysis.
  • Manual Analysis: Employed multi-label open coding to annotate ChatGPT answers, focusing on correctness, consistency, comprehensiveness, and conciseness.
  • Linguistic and Sentiment Analysis: Used the LIWC tool and RoBERTa sentiment analysis model to automatically evaluate linguistic features and emotional tone.
  • User Study: Designed a user experiment involving 12 participants to assess answer quality, preference selection, and misinformation identification.

• Technical Innovations:

  • Proposed a classification system for errors in ChatGPT's answers and identified their root causes.
  • Revealed linguistic characteristics of ChatGPT's answers, such as formality, analytical nature, and emotional tone, through linguistic analysis.
  • Conducted user experiments to uncover programmer preferences and sensitivity to misinformation.

Research Findings

Key Discoveries:

1. Error Characteristics of ChatGPT's Answers:

   • Lack of Correctness: 52% of answers contained misinformation, with conceptual errors accounting for the highest proportion (54%).
   • Verbose Language: 77% of answers included redundant, irrelevant, or excessive information.
   • Consistency Issues: 78% of answers differed from human answers in terms of conceptual accuracy, consistency, and the number of solutions provided.
   • Linguistic Characteristics: ChatGPT's answers were more formal, analytical, and positive in tone, while human answers were more casual but included more risk warnings.

2. User Perception of Answers:

   • Human answers scored significantly higher than ChatGPT's in terms of correctness, conciseness, and practicality.
   • Users appreciated ChatGPT's higher linguistic sophistication and comprehensiveness, but approximately 39% of users failed to identify misinformation in its answers.

3. Impact of Question Type on Quality:

   • Popularity and age of questions: Answers to more popular and older questions were of higher quality.
   • Question type: Answers to debugging questions were less accurate but more concise, while conceptual and instructional questions tended to be more verbose.

Limitations and Future Directions:

• Limitations:

  • The data is biased toward the free version of ChatGPT (GPT-3.5), which may differ from the performance of the latest GPT-4.
  • Multi-turn interactions that could potentially improve answer quality were not considered.
  • The analysis involved subjective elements and was influenced by human preferences.

• Future Directions:

  • Develop more robust verification mechanisms to reduce the impact of misinformation, including automated content review frameworks.
  • Explore ways to improve large language models' reasoning capabilities and semantic understanding of questions.
  • Enhance communication of uncertainty in programming tasks and develop effective visualization frameworks.
  • In the education field, utilize erroneous text as new teaching materials for learning purposes.