Modeling and explaining beacons in code comprehension

The goal of this project is to understand how software engineers comprehend computer programs.
Do they use beacons or references in programs to ease their comprehension? Are their critical parts of the program they tend to focus and spend time on more?
We also investigate how state of the art generative models like GPT perform on the task of identifying such beacons.

Setup

conda create -n program-comprehension python=3.8.11
conda env update --file env.yml --prune
conda activate program-comprehension

or

PIP_EXISTS_ACTION=w conda env create -f env.yml

Data

Programs used in the behavioral experiments were sourced from the following repositories:

https://github.com/githubhuyang/refactory
https://github.com/jkoppel/QuixBugs

To run

Step 1

First, get model output information for each stimuli:

Mode 1: Get last-layer model activations for each input token

For each problem, this mode generates a torch pkl containing a dict: tokens -> tensor.
Path: ./experiments/custom-anonym

python comprehend/model_outputs.py \
--model_names santa-coder \
--number_of_records -1 \
--dataset_name custom-anonym \
--dataset_path ./data \
--infer_interval 1 \
--expt_dir ./experiments \
--mode 1

Mode 2: Get model LL support sizes for each input token

This mode generates a CSV for each problem.
Path: ./experiments/custom-anonym

python comprehend/model_outputs.py \
--model_names santa-coder \
--number_of_records -1 \
--dataset_name custom-anonym \
--dataset_path ./data \
--infer_interval 1 \
--expt_dir ./experiments \
--mode 2

Step 2

Next, align model output data with participant responses available as Qualtrics data (which needs to be placed in ./data)

python comprehend/prepare_dataset.py \
--responses_path data/code-comprehend_March 13, 2023_10.00.xlsx \ 
--token_wise_ll_support_path experiments/custom-anonym \
--token_wise_representations_path experiments/custom-anonym \
--out_path experiments/results

Step 3

Analyze the prepared data by training models

python comprehend/analyze.py \
--dataset_path experiments/results \

Test config

For comprehend/model_outputs.py

[
"--model_names", "codeberta-small",
"--number_of_records", "-1",
"--infer_interval", "2",
"--dataset_name", "custom-anonym",
"--dataset_path", "./data",
]