Recommending Programming Languages by Identifying Skill Gaps Using Analysis of Experts. A Study of Stack Overflow
Bibliographic record
Abstract
The increasing variety of programming languages available to computer programmers has led to the discussion of what language(s) should be learned. A key point in the choice of a programming language is the availability of support from experienced programmers. In this paper, we explore the use of graph theory in recommending programming languages to novice and expert programmers in a question and answer collaborative learning environment, Stack Overflow. Using social network analysis techniques, we investigate the relationship between experts (using an expertise graph) in different programming languages to identify what languages can be recommended to novice and experienced programmers. In addition, we explore the use of the expertise graph in inferring the importance of a programming language to the community. Our results suggest that programming languages can be recommended within organizational borders and programming domains. In addition, a high number of experts in a programming language does not always mean that the language is popular. Furthermore, disconnected nodes in the expertise graph suggest that experts in some programming languages are primarily on Stack Overflow to support that language only and do not contribute to questions or answers in other languages. Finally, developers are comfortable with mastering a single, general purpose language. The results of our study can help educators and stakeholders in computer education to understand what programming languages can be suggested to students and what languages can be taught and learned together.
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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 | 0.004 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".