Can We Identify Stack Overflow Questions Requiring Code Snippets? Investigating the Cause & Effect of Missing Code Snippets

• URL: http://arxiv.org/abs/2402.04575v1
• Date: Wed, 7 Feb 2024 04:25:31 GMT
• Title: Can We Identify Stack Overflow Questions Requiring Code Snippets? Investigating the Cause & Effect of Missing Code Snippets
• Authors: Saikat Mondal, Mohammad Masudur Rahman, Chanchal K. Roy
• Abstract summary: On the Stack Overflow (SO) Q&A site, users often request solutions to their code-related problems. They often miss required code snippets during their question submission. This study investigates the cause & effect of missing code snippets in SO questions whenever required.
• Score: 8.107650447105998
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: On the Stack Overflow (SO) Q&A site, users often request solutions to their code-related problems (e.g., errors, unexpected behavior). Unfortunately, they often miss required code snippets during their question submission, which could prevent their questions from getting prompt and appropriate answers. In this study, we conduct an empirical study investigating the cause & effect of missing code snippets in SO questions whenever required. Here, our contributions are threefold. First, we analyze how the presence or absence of required code snippets affects the correlation between question types (missed code, included code after requests & had code snippets during submission) and corresponding answer meta-data (e.g., presence of an accepted answer). According to our analysis, the chance of getting accepted answers is three times higher for questions that include required code snippets during their question submission than those that missed the code. We also investigate whether the confounding factors (e.g., user reputation) affect questions receiving answers besides the presence or absence of required code snippets. We found that such factors do not hurt the correlation between the presence or absence of required code snippets and answer meta-data. Second, we surveyed 64 practitioners to understand why users miss necessary code snippets. About 60% of them agree that users are unaware of whether their questions require any code snippets. Third, we thus extract four text-based features (e.g., keywords) and build six ML models to identify the questions that need code snippets. Our models can predict the target questions with 86.5% precision, 90.8% recall, 85.3% F1-score, and 85.2% overall accuracy. Our work has the potential to save significant time in programming question-answering and improve the quality of the valuable knowledge base by decreasing unanswered and unresolved questions.

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