Understanding and Predicting Software Developer Expertise in Stack Overflow and GitHub
Bibliographic record
Abstract
Knowledge and experience are touted as both the necessary and sufficient conditions to make a person an expert.This thesis attempts to investigate this issue in the context of software development by studying and predicting software developer's expertise based on their activity and experience on GitHub and Stack Overflow platforms.The thesis studies how developers themselves define the notion of an "expert", as well as why or why not developers contribute to online collaborative platforms.An exploratory survey was conducted with 73 software developers and a mixed methods approach was applied to analyze the survey results.The results provided deeper insights into how an expert in the field could be defined.The study provides a better understanding of the underlying factors that drive developers to contribute to GitHub and Stack Overflow, and the challenges they face when participating on either platform.Further, using machine learning techniques the thesis attempts to predict software developer expertise based on their participation on social coding platforms.I, Sri Lakshmi Vadlamani, would like to express my sincere gratitude to my amazing supervisor, Professor Olga Baysal, for her continuous guidance, advice, and friendly discussions.I was able to successfully complete this work solely because of her continuous efforts, her valuable feedback and positive reinforcements at every stage of this project and also through out my MS journey.I am very thankful to God for giving me two sons, Karthik and Krithik; my children thoroughly supported me during this journey by showing great situational awareness and for encouraging me and keeping me optimistic at every step of this journey and especially in some difficult moments.Also, I am thankful to my parents, my in-laws
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".