Make it simple: an empirical analysis of GNU make feature use in open source projects
Bibliographic record
Abstract
Abstract—Make is one of the oldest build technologies and is still widely used today, whether by manually writing Makefiles, or by generating them using tools like Autotools and CMake. Despite its conceptual simplicity, modern Make implementations such as GNU Make have become very complex languages, featuring functions, macros, lazy variable assignments and (in GNU Make 4.0) the Guile embedded scripting language. Since we are interested in understanding how widespread such complex language features are, this paper studies the use of Make features in almost 20,000 Makefiles, comprised of over 8.4 million lines, from more than 350 different open source projects. We look at the popularity of features and the difference between hand-written Makefiles and those generated using various tools. We find that generated Makefiles use only a core set of features and that more advanced features (such as function calls) are used very little, and almost exclusively in hand-written Makefiles. I.
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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 | 0.011 | 0.133 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".