dataset_info:
features:
- name: question_id
dtype: int64
- name: title
dtype: string
- name: question_body
dtype: string
- name: question_type
dtype: string
splits:
- name: train
num_bytes: 3344568
num_examples: 3443
download_size: 0
dataset_size: 3344568
Dataset Card for "stackoverflow_question_types"
Dataset Description
Recent research has taken a look into leveraging data available from StackOverflow (SO) to train large language models for programming-related tasks. However, users can ask a wide range of questions on stackoverflow; The "stackoverflow question types" is a dataset of manually annotated questions posted on SO with an associated type. Following a previous study, each question was annotated with a type capturing the main concern of the user who posted the question. The questions were annotated with the given types:
- How to do it: Providing a scenario and asking about how to implement it (sometimes with a given technology or API).
- Debug/corrective: Dealing with problems in the code under development, such as runtime errors and unexpected behaviour.
- Seeking different solutions: The questioner has a working code yet is seeking a different approach to doing the job.
- Need to know: Questions regarding the possibility or availability of (doing) something. These questions normally show the lack of knowledge or uncertainty about some aspects of the technology (e.g. the presence of a feature in an API or a language).
- Other: Something else
We note the following distinction between the three first categories.
- How to do it: the user wants to do "x", has no clear idea or solution/doesn't know how to do it -> wants any solution for solving "x".
- Debug: the user wants to do "x", has a clear idea/solution "y" but it is not working, and is seeking a correction to "y".
- Seeking-different-solution: the user wants to do "x", and found already a working solution "y", but is seeking an alternative "z".
Naturally, some questions may have multiple concerns (i.e. could correspond to multiple categories). However, this dataset contains mainly questions for which we could assign a clear single category to each question. Currently, all questions annotated are a subset of the stackoverflow_python dataset.
Languages
The currently annotated questions concern posts with the python tag. The questions are written in English.
Dataset Structure
Data Instances
[More Information Needed]
Data Fields
- question_id: the unique id of the post
- question_body: the (HTML) content of the question
- question_type: the assigned category/type/label
- "howto",
- "debug",
- "seeking",
- "conceptual",
- "other"
Data Splits
[More Information Needed]
Dataset Creation
Annotations
Annotation process
Previous research looked into mining natural language-code pairs from stackoverflow. Two notable works yielded the StaQC and ConaLA datasets. Parts of the dataset used a subset of the manual annotations provided by the authors of the papers (available at (staqc)[https://huggingface.co/datasets/koutch/staqc], and (conala)[https://huggingface.co/datasets/neulab/conala]). The questions were annotated as belonging to the "how to do it" category.
To ease the annotation procedure, we used the argilla platform and multiple iterations of few-shot training with a SetFit model.
Considerations for Using the Data
Discussion of Biases
[More Information Needed]
Other Known Limitations
[More Information Needed]