Learn or Earn? - Intelligent Task Recommendation for Competitive Crowdsourced Software Development
Bibliographic record
Abstract
Background: Competitive crowdsourced development encourages online software developers to register for tasks offered on the crowdsourcing platform and implement them in a competitive mode. As a large number of tasks are uploaded daily, the scenery of competition is changing continuously. Without appropriate decision support, online developers often make task decisions in an ad hoc and intuitive manner. Aims: To provide dynamic decision support for crowd developers to select the task that fit best to their personal learning versus earning objectives, taking into account the actual competitiveness situation. Method: We propose a recommendation system called EX2 ("EX-Square") that combines both explorative ("learn") and exploitative ("earn") search for tasks, based on a systematic analysis of workers preference patterns, technologies hotness, and the projection of winning chances. The implemented prototype allows dynamic recommendations that reflect task updates and competition dynamics at any given time. Results: Based on evaluation from 4007 tasks monitored over a period of 2 years, we show that EX2 can explore and adjust task recommendations corresponding to context changes, and individual learning preferences of workers. A survey was also conducted with 14 actual crowd workers, showing that intelligent decision support from EX2 is considered useful and valuable. Conclusions: With support from EX2, workers benefit from the tool from getting customized recommendations, and the platform provider gets a higher chance to better cover the breadth of technology needs in case recommendations are taken.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".