Mining Experts from Source Code Analysis: An Empirical Evaluation
Bibliographic record
Abstract
Modern software development increasingly depends on third-party libraries to boost productivity and quality. This development is complex and requires specialists with knowledge in several technologies, such as the nowadays libraries. Such complexity turns it extremely challenging to deliver quality software, given the pressure. For this purpose, it is necessary to identify and hire qualified developers, to obtain a good team, both in open source and proprietary systems. For these reasons, enterprise and open source projects try to build teams composed of highly skilled developers in specific libraries. However, their identification may not be trivial. Despite this fact, we still lack procedures to assess developers skills in widely popular libraries. In this paper, we first argue that source code activities can identify software developers’ hard skills, such as library expertise. We then evaluate a mining-based strategy to reduce the search space to identify library experts. To achieve our goal, we selected the 9 most popular Java libraries and 6 libraries for microservices (i.e., 15 libraries in total). We assessed the skills of more than 1.5 million developers in these libraries by analyzing their commits in more than 17 K Java projects on GitHub. We evaluated the results by applying two surveys with 158 developers. First, with 137 library expert candidates, they observed 63% precision for popular Java libraries’ used strategy. Second, we observe a precision of at least 71% for 21 library experts in microservices. These low precision values suggest space for further improvements in the evaluated strategy.
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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 | 0.043 | 0.140 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".